satellite gravimetric applications for groundwater

i

Satellite Gravimetric Applications for

Groundwater Resource Management in Indus

Basin of Pakistan

Naveed Iqbal

Ph.D Geophysics

Department of Earth Sciences

Quaid-i-Azam University, Islamabad

2019

ii

AUTHOR’S DECLARATION

I, Naveed Iqbal, hereby state that my PhD thesis titled “Satellite Gravimetric

Applications for Groundwater Resource Management in Indus Basin of Pakistan” is my

own effort and has not been submitted previously by me for taking any degree from this

university, Quaid-i-Azam University or anywhere else in the country/world.

At any time, if my statement is found to be incorrect even after my graduation, the

university has the right to withdraw my PhD degree.

Naveed Iqbal

Date: ………………………..

iii

PLAGEARISM UNDERTAKING

I solemnly declare that research work presented in the thesis titled “Satellite

Gravimetric Applications for Groundwater Resource Management in Indus Basin of

Pakistan” is solely my research work with no significant contribution from any other person.

Small contribution/help wherever taken has been duly acknowledged and that complete thesis

has been written by me.

I understand the zero tolerance policy of the HEC and Quaid-i-Azam University

towards plagiarism. Therefore, I as an Author of the above titled thesis declare that no portion

of my thesis has been plagiarized and any material used as reference is properly referred/cited.

I undertake that if I am found guilty of any formal plagiarism in the above tiled thesis even

after award of PhD degree, the University reserves the rights to withdraw/revoke my PhD

degree and that HEC and the University has the right to publish my name on the

HEC/University website on which names of students are placed who submitted plagiarized

thesis.

Signature:

Name: Naveed Iqbal

iv

CERTIFICATE OF APPROVAL

This is to certify that the research work presented in this thesis, entitled “Satellite

Gravimetric Applications for Groundwater Resource Management in Indus Basin of

Pakistan” was conducted by Mr. Naveed Iqbal under the supervision of Prof. Dr.

Muhammad Gulraiz Akhter.

No part of this thesis has been submitted anywhere else for any other degree. This thesis

is submitted to the Department of Earth Sciences of Quaid-i-Azam Universtiy-45320

Islamabad in partial fulfillment of the requirements for the degree of Doctor of Philosophy in

field of Geophysics, Department of Earth Sciences, Quaid-i-Azam Universtiy-45320

Islamabad.

Student Name: Naveed Iqbal Signature:___________________

Examination Committee:

a) External Examiner I:

Name: Dr. Shahid Nadeem Qureshi Signature:___________________

(Designation & Office Address)

Associate Professor (Rtd.),

Department of Earth Sciences,


House No. 406, Street No. 17, Phase-III

Bahria Town, Rawalpindi

E-mail: [email protected]

b) External Examiner II:

Name: Dr. Muhammad Qaisar Signature:___________________

(Designation & Office Address)

Advisor for Earthquake Studies,

National Centre for Physics



Supervisor Name: Dr. M. Gulraiz Akhter Signature:___________________

Associate Professor


Quaid-i-Azam University,

Islamabad

Name of Head/ HoD: Dr. M. Gulraiz Akhter Signature:___________________


Quaid-i-Azam University,

Islamabad

mailto:[email protected]


v

OFFICE OF THE CONTROLLER OF EXAMINATION

NOTIFICATION

No. Date:

It is notified for the information of all concerned that Mr. Naveed Iqbal, PhD scholar of

Department of Earth Sciences of Quaid-i-Azam University, Islamabad, Pakistan has completed

all the requirements for the award of PhD degree in the discipline Geophysics as per detail

given hereunder:

PhD in Education Cumulative Result

Registration No. Scholar Name Father’s

Name

Credit Hours Cumulative

Grade Point

Average

(CGPA)

Course

work

Research

Work

Total

03111213001-

ES/PhD-2012

Naveed Iqbal Muhammad

Hayat

20

Research Topic: “Satellite Gravimetric Applications for Groundwater Resource

Management in Indus Basin of Pakistan”

Local Supervisor Name: Prof. Dr. Muhammad Gulraiz Akhter

Foreign/External Examiners:

a) Name: Dr. Mehdi Eshagh

Professor of Geodesy,

Department of Engineering Science,

University West

46186 Trollhattan

Sweden


b) Name: Dr. Allan. E. Fryar

Associate Professor

Department of Earth and Environmental

Sciences

University of Kentucky

101 Slone Building

Lexington, KY 40506-0053 USA


The detail of research articles published on the basis of thesis research work are given below;

1. Iqbal, N., Faisal, H., Hyongki L., and Gulraiz A. (2016). Satellite gravimetric estimation

of groundwater storage variations over Indus Basin in Pakistan. IEEE JSTAR, 9(8), 3524–

3534. doi:10.1109/JSTARS.2016.2574378.

2. Iqbal, N., Faisal, H., Hyongki L., and Gulraiz A. (2017). Integrated groundwater resource

management in Indus Basin using satellite gravimetry and physical modeling tools.

Environmental Monitoring and Assessment, Vol, 189(3), pp. 1-16. doi:10.1007/s10661-

017-5846-1.

Note: This result is declaration as notice only. Errors and omissions, if any, are subject to

subsequent rectification.

Signed by

Controller of Examination



vi

DEDICATION

Dedicated to my beloved Wife and Daughter whose unforgettable

sacrifice and unconditional support, motivated me to complete my PhD

vii

ACKNOWLEDGEMENTS

All praises are to Almighty Allah, the most merciful and the most beneficent, Who

created the universe for the human beings, and offered them to explore his master piece the

earth and to see the signs of his powerfulness. I am thankful to my ALLAH Who has blessed

me with courage and strength for the accomplishment of this thesis. Secondly, praises are for

the last and beloved Prophet Muhammad (Peace Be Upon Him) who is a continuous source of

guidance for mankind towards the righteous path.

I am very grateful to my respected supervisor, Prof. Dr. Muhammad Gulraiz Akhter for

his guidance, encouragement and inspiration, which has finally resulted in the compilation of

my dissertation. His continuous cooperation and encouragement provided me motivation to

accomplish my PhD.

I would like to express my immense gratitude to Dr. A. D. Khan, Ex-Director General,

Pakistan Council of Research in Water Resources (PCRWR) for his professional guidance. It

would not be the justice if I could not acknowledge the technical and financial supported

extended by Dr. Faisal Hossain, University of Washington, USA. His unconditional support

and enthusiasm provided me an opportunity to complete my research work well in time. I am

also thankful to Dr. Muhammad Ashraf, Chairman (PCRWR) for his encouragement to find

some economical and practically viable solution of the complex problem related to

groundwater resource management.

Scarp Monitoring Organization (SMO), WAPDA, Lahore, Punjab Irrigation

Department, Lahore, Pakistan Meteorological Department (PMD), Islamabad are greatly

acknowledged for the provision of related datasets. Finally, I wish to express my deepest

gratitude to my mother and brothers especially Muzaffar Iqbal (late), for their prayers,

encouragement and moral support during my studies. I am also pleased to extend my sincere

thanks to all my friends and colleagues specially Dr. Hammad Gilani and Dr. Waqas A. Qazi

for their well wishes and professional support for my success.

Naveed Iqbal

viii

ABSTRACT

The goal of this study is accurate quantification of groundwater storage changes for

effective groundwater resource management in Indus Basin of Pakistan. This study uses

satellite integrated physical modeling methodology to analyze the groundwater dynamics over

Upper Indus Plain (UIP), which covers Punjab Province of Pakistan. The GRACE (Gravity

Recovery and Climate Experiment) data has been used for the extraction of Terrestrial Water

Storage (TWS) changes and then Variable Infiltration Capacity (VIC) Model has been applied

to derive Groundwater Storage (GWS) changes from 2003-2010. The VIC model has been

specifically developed for Indus Basin at 0.1˚ × 0.1˚ grid scale to simulate daily soil moisture

and surface water fluxes. The evenly distributed ground observation data of about 150

piezometric water level changes has been used for the calibration (2003-2007) and validation

(2008-2010) purposes. In comparison with Indus Basin of Pakistan (IBP), the results suggest

that UIP is at the wave of more rapid variations both in terms of total water storage as well as

groundwater storage anomalies due to over exploitation of groundwater for anthropogenic

purposes. The intensity of these variations in terms of decrease either in TWS or GWS over

UIP is about three times higher than IBP. While investigation and analysis, it is estimated that

UIP has lost a stock of about 11.84 km3 of fresh groundwater storage in just 8 years of time

(2003-2010) through extensive groundwater abstraction. The projected scenario (2011-2014)

indicate further loss of fresh groundwater storage due to increasing dependence on

groundwater.

The potential of GRACE derived methodology has been evaluated at effective

groundwater management scales (doabs) using numerical downscaling technique. The

accuracy of GRACE derived GWS has been evaluated at each doab (the area bounded by two

rivers) scale and the phenomenon of groundwater depletion and recharge have been quantified.

The seasonal to annual changes have been analyzed to study the groundwater system behavior,

flooding impact and critical areas have been identified where; groundwater sustainability is at

risk. While studying the groundwater dynamics at local scales, the detailed investigation

reveals that GRCAE is more effective when the study area is comparable enough to the spatial

resolution of GRACE or the trends of groundwater recharge and depletion are significantly

persistent. Resultantly, GRACE has found more successful in two (Bari and Rechna doabs) out

of four doabs where groundwater depletion trends (depletion or recharge) are more prominent

and persistent enough. Subsequently, the vulnerability of groundwater sustainability is at the

verge of moderate to severe in Rechna and Bari doabs respectively. It is estimated that GWS

ix

has depleted at the rate of 0.38 km3/year in Bari and 0.21 km3/year in Rechna doabs over the

period 2003-2010. It is observed that the areas of Lower Bari (Multan, Lodhran, Khanewal

including Lahore) and some parts of Rechna doabs (Toba Tek Singh and parts of the Jhang

districts) are under stress where the excessive pumping dominates the recharge. Therefore,

immediate attention is required by the concerned departments with some remedial measures.

The designed methodology has been compared and found in good agreement with the

traditional approaches like piezometric monitoring and groundwater modeling in terms of

trends. Being an underground resource with dynamic nature, the integrated methodology

consisting of the GRACE, VMOD and piezometric monitoring is suggested quite useful, which

would help improve the accurate quantification of abstraction and recharge mechanisms. This

would impact the competency to plan effective management strategies. The evaluation of

statistical approach for future projection resulted an average standard error (SE) of 9 mm and

7 mm in Bari and Rechna doabs respectively with favorable correlation and found suitably

appropriate for 3-6 monthly future projection. However, this technique is not found appropriate

for Chaj and Bari doabs due to disagreement with Piezo-GWS over the calibration period.

The study also outlines the potential opportunities and challenges associated with

satellite gravimetric applications for operational groundwater management. This study has also

suggested appropriate management strategies to ensure the aquifer sustainability of Indus

Basin.

x

Table of Contents

CHAPTER 1 ............................................................................................................................. 1

INTRODUCTION.................................................................................................................... 1

1.1 BACKGROUND ................................................................................................................. 1

1.2 STUDY AREA .................................................................................................................. 2

1.3 HYDROLOGY ................................................................................................................... 6

1.4 LITERATURE REVIEW .................................................................................................... 16

1.5 PROBLEM STATEMENT .................................................................................................. 22

1.6 OBJECTIVES .................................................................................................................. 22

CHAPTER 2 ........................................................................................................................... 23

DATASETS AND METHODOLOGY ................................................................................. 23

2.1 GRACE DATASETS ...................................................................................................... 23

2.2 PIEZOMETRIC DATASETS............................................................................................... 24

2.3 VARIABLE INFILTRATION CAPACITY (VIC) MODEL DATASETS .................................... 24

2.4 METHODOLOGY ............................................................................................................ 25

CHAPTER 3 ........................................................................................................................... 29

GRACE DATA PROCESSING AND HYDROLOGICAL MODELING ........................ 29

3.1 RELATION BETWEEN SURFACE MASS AND GRAVITY .................................................... 29

3.2 PROCESSING OF SPHERICAL HARMONIC COEFFICIENTS ................................................ 32

3.2.1 Step-0: Rename Data Files................................................................................... 32

3.2.2 Step-1: Extract SHCs ........................................................................................... 33

3.2.3 Step-2 & 3: Geocentre & Truncation ................................................................... 34

3.2.4 Step-4 & 5: Average Calculation and Reference Subtraction ............................. 34

3.2.5 Step-6 & 7: Remove PGR and Decorrelation Filter ............................................ 35

3.2.6 Step-8 & 9: Transform SHCs to Mass and Mass to Grids ................................... 35

3.2.7 Step-10: Gaussian Smoothing and Leakage Reduction ....................................... 36

3.3 SIGNAL RESTORATION .................................................................................................. 36

3.4 VARIABLE INFILTRATION CAPACITY MODEL (VIC)...................................................... 37

3.4.1 VIC Model Simulation and Calibration ............................................................... 38

CHAPTER 4 ........................................................................................................................... 45

ESTIMATION OF GWS VARIATIONS OVER INDUS BASIN ..................................... 45

4.1 TOTAL WATER STORAGE VARIATIONS ......................................................................... 45

4.2 GROUNDWATER STORAGE VARIATIONS........................................................................ 46

4.3 GWS CALIBRATION ANALYSIS ..................................................................................... 48

4.4 FLOODING ANALYSIS .................................................................................................... 52

CHAPTER 5 ........................................................................................................................... 54

INTEGRATION OF SATELLITE GRAVIMETRY WITH PHYSICAL MODELING

TOOLS .................................................................................................................................... 54

5.1 GROUNDWATER MONITORING THROUGH GROUND OBSERVATIONAL NETWORK .......... 54

5.2 GROUNDWATER MODELING .......................................................................................... 57

5.3 SATELLITE GWS DOAB SCALE ESTIMATION ................................................................ 60

5.4 INTEGRATED GROUNDWATER MANAGEMENT ............................................................... 67

xi

5.5 GRACE – A SPATIAL DECISION SUPPORT TOOL .......................................................... 83

5.6 TRACKING GROUNDWATER FROM SPACE ...................................................................... 84

5.6.1 Opportunities........................................................................................................ 84

5.6.2 Challenges ............................................................................................................ 84

CHAPTER 6 ........................................................................................................................... 86

CONCLUSION AND RECOMMENDATIONS ................................................................. 86

6.1 CONCLUSIONS ............................................................................................................... 86

6.2 RECOMMENDATIONS ..................................................................................................... 88

REFERENCES ....................................................................................................................... 90

LIST OF PUBLICATIONS .................................................................................................. 95

SEMINAR PRESENTATIONS ............................................................................................ 95

CONFERENCE AND WORKSHOP PARTICIPATION ................................................. 95

REPRINTS OF PUBLICATIONS ....................................................................................... 97

APPENDIX ............................................................................................................................. 99

APPENDIX-A: EXAMPLES OF MODEL BUILDER TOOL FOR DATA PROCESSING AND ANALYSIS

IN ARC GIS SOFTWARE ......................................................................................................... 99

APPENDIX-B: VIC SIMULATION RESULTS OVER INDUS BASIN (2002-2010) ........................ 99

APPENDIX-C: OBSERVED ANNUAL RIVER INFLOWS (MAF) ............................................... 100

APPENDIX-D: ESTIMATION OF GROUNDWATER STORAGE VARIATIONS DERIVED FROM

GLDAS AND VIC ............................................................................................................... 101

APPENDIX-E: CALCULATION PROCEDURE FOR GROUNDWATER STORAGE ANOMALIES ..... 104

xii

LIST OF FIGURES

Figure 1.1: Location map of study area. AJK stands for Azad Jammu and Kashmir. ............... 3

Figure 1.2: Topographic variations over UIP. The contours (purple color) are derived from

SRTM 90 meter USGS-DEM with 5-meter interval. AJK stands for Azad Kashmir. .............. 5

Figure 1.3: Projected scenario of increasing population (red line) versus water availability (blue

color). The population is in million extracted from National Census of 1981, 1998 and 2017

conducted by Population Census Organization (PCO), Pakistan. ............................................. 7

Figure 1.4: Indus Basin Irrigation System (IBIS) in UIP. The irrigation system (river and

canals) are in blue color whereas, the red dots are the locations of barrages. ........................... 9

Figure 1.5: Annual average rainfall variations from 1971-2015 in Punjab Province. The blue

lines is the rainfall time series generated using PMD station data. The dotted line in red color

shows the overall rainfall trend. ............................................................................................... 10

Figure 1.6: Groundwater development in UIP over last three decades (1985-2015) .............. 11

Figure 1.7: District-wise Distribution of percentage of total number of tube wells in UIP (2012).

The different colors represent different districts of Punjab province. The percentages show the

contribution of number of tube-wells installed in that particular district. ............................... 11

Figure 1.8: Doab-wise, tube wells density in UIP (2012)........................................................ 12

Figure 1.9: Variations in area coverage under different depth to water table in UIP. The green,

purple and cyan colors show area coverage (%) under maximum depth > 600 cm, 450-600 and

300-450 cm. ............................................................................................................................. 13

Figure 1.10: Average depth to water table variations over UIP in 2010. The red color shows

highest depth to water table and is the highly depleted area. The black lines show the different

districts of Punjab (Khan et al. 2016a)..................................................................................... 15

Figure 2.1: GRACE mission data flow describes the process of data collection and different

data processing levels (adopted from http://www.csr.utexas.edu/grace/) ................................ 23

Figure 2.2: Flow chart methodology for the estimation of groundwater storage anomaly ...... 26

Figure 3.1: Step by step methodological approach for the GRACE data processing .............. 32

Figure 3.2: Calibration stations, the numbers are the normalized RMSE at each station. The

Indus ......................................................................................................................................... 40

Figure 3.3: Variations of SMR anomalies during February 2003 over Indus Basin (0.1˚ × 0.1˚)

.................................................................................................................................................. 43

Figure 3.4: Variations of average SMR anomalies (2003-2010) over Indus Basin (1˚ × 1˚). . 43

Figure 3.5: Variations of average SMR anomalies (2003-2010) over UIP (0.1˚× 0.1˚) .......... 44

Figure 4.1: Mean trend map of TWS anomalies from 2003-2010 over Indus Basin. The red

color represents highest depletion in total water storage followed by yellowish, light green and

cyan colors ............................................................................................................................... 45

Figure 4.2: Comparison of TWS, GWS and SM from 2003-2010 over UIP........................... 46

Figure 4.3: Comparison of VIC based GRACE-GWS changes (blue) with GLDAS-1 based

GRACE-GWS changes (yellow) ............................................................................................. 48

xiii

Figure 4.4: Comparison of GRACE-GWS (red color) anomalies with piezometric-GWS (blue

color) over UIP (2003-2010) ................................................................................................... 48

Figure 4.5 Groundwater stock variations over UIP from 2003-2014 ...................................... 52

Figure 5.1: Piezometric network of water level monitoring in UIP. The reddish dots are the

water table measurement locations used for calibration purpose ............................................ 54

Figure 5.2: Variations in average depth to water table over UIP in 2010. .............................. 56

Figure 5.3: Average depth to water table variations in Lodhran, Multan and Khanewal from

2005-2010. LMK (yellow bar) is annual average trend of groundwater depletion in three

districts. .................................................................................................................................... 57

Figure 5.4: Doab scale annual average variations in groundwater simulated with Visual

ModFlow over UIP from 2000-2010 ....................................................................................... 58

Figure 5.5: ModFlow simulated annual average variations in groundwater over Bari and

Rechna doabs from 2000-2010 ................................................................................................ 59

Figure 5.6: ModFlow simulated annual average variations in groundwater over Chaj doab from

2000-2010 ................................................................................................................................ 59

Figure 5.7: ModFlow simulated annual average variations in groundwater over Thal doab from

2000-2010 ................................................................................................................................ 59

Figure 5.8: Annual average groundwater storage variations in 2003 over UIP. Dark red color

shows negative change representing depletion in groundwater storage .................................. 61

Figure 5.9: Annual average groundwater storage variations in 2004 over UIP....................... 62

Figure 5.10: Annual average groundwater storage variations in 2005 over UIP ..................... 62






Figure 5.16: Annual average groundwater storage variations from 2003-2009 over UIP ....... 65

Figure 5.17: Annual average groundwater storage variations from 2003-2010 over UIP ....... 66

Figure 5.18: Change in groundwater storage from July-August, 2010 over UIP .................... 66

Figure 5.19: Comparison of the GRACE along with Piezometric derived variations in

groundwater storage over Bari doab from 2003-2010 ............................................................. 69


groundwater storage over Rechna doab from 2003-2010 ........................................................ 69

Figure 5.21: Seasonal changes in groundwater stock over Bari doab from 2003-2010 .......... 72

Figure 5.22: Seasonal changes in groundwater stock over Rechna doab from 2003-2010 ..... 72


groundwater storage over Chaj doab from 2003-2010 ............................................................ 73


groundwater storage over Thal doab from 2003-2010 ............................................................ 73

xiv

Figure 5.25: Seasonal changes in groundwater stock over Chaj doab from 2003-2010.......... 76

Figure 5.26: Seasonal changes in groundwater stock over Thal doab from 2003-2010 .......... 76

Figure 5.27: Correlation between the GRACE and piezometric groundwater storage variations

over Bari doab during calibration period (2003-2007) ............................................................ 78

Figure 5.28: Variations in standard error over Bari doab during projected period (January-June,

2011) ........................................................................................................................................ 78


over Rechna doab during calibration period (2003-2007) ....................................................... 79

Figure 5.30: Variations in standard error over Rechna doab during projected period (January-

June, 2011) ............................................................................................................................... 79


over Chaj doab during calibration period (2003-2007) ........................................................... 80

Figure 5.32: Variations in standard error over Chaj doab during projected period (January-June,

2011) ........................................................................................................................................ 80


over Thal doab during calibration period (2003-2007) ............................................................ 81

Figure 5.34: Variations in standard error over Thal doab during projected period (January-June,

2011) ........................................................................................................................................ 81

xv

LIST OF TABLES Table 1.1: Summary of main features of UIP ………………………………………………….6

Table 3.1: Performance of the VIC model over Indus basin ………………………………….40

Table 4.1: Calculation of groundwater storage variations over UIP ………………………….51

Table 4.2: Summary of groundwater depletion and recharge calculations …………………...53

Table 5.1: Summary of piezometric analysis of groundwater depletion in Lower Bari

doab Area …...........................................................................................................55

Table 5.2: Comparison of numerical downscaling results at different grid scale …………….61

Table 5.3: Calculation of groundwater storage variations over Bari doab …………………..70

Table 5.4: Calculation of groundwater storage variations over Rechna doab ………………..71

Table 5.5: Estimation of groundwater stock changes over Bari doab from 2003-2010 ……..72

Table 5.6: Estimation of groundwater stock changes over Rechna doab from 2003-2010 …...72

Table 5.7: Calculation of groundwater storage variations over Chaj doab …………………..74

Table 5.8: Calculation of groundwater storage variations over Thal doab …………………..75

Table 5.9: Estimation of groundwater stock changes over Chaj doab from 2003-2010 ….....76

Table 5.10: Estimation of groundwater stock changes over Thal doab from 2003-2010 ……..76

Table 5.11: Calculation of standard error during validation period over Bari doab ………….78

Table 5.12: Calculation of standard error during validation period over Rechna doab ………79

Table 5.13: Calculation of standard error during validation period over Chaj doab ………….80

Table 5.14: Calculation of standard error during validation period over Thal doab ………….81

xvi

LIST OF ABBREVIATIONS

BCM Billion Cubic Meter

DEM Digital Elevation Model

FAO Food and Agriculture Organization, United Nations

GIS Geographic Information System

GLA Groundwater Level Anomaly

GLC Groundwater Level Changes

GRACE Gravity Recovery and Climate Experiment

GSA Groundwater Storage Anomaly

GWS Groundwater Storage

IBIS Indus Basin Irrigation System

IBP Indus Basin of Pakistan

IWASRI International Waterlogging and Salinity Research Institute

MAF Million Acre Feet

NASA National Aeronautics and Space Administration

PCRWR Pakistan Council of Research in Water Resources

PGR Post Glacial Rebound

PID Punjab Irrigation Department

PMD Pakistan Meteorological Department

RS Remote Sensing

SM Soil Moisture

SMO SCARP Monitoring Organization

SRTM Shutter Radar Topographic Mission

TWS Total Water Storage

USGS United States Geological Survey

UIP Upper Indus Plain

VIC Variable Infiltration Capacity Model

WAPDA Water and Power Development Authority

1

CHAPTER 1

Introduction

1.1 Background

Groundwater is a finite, dependable and life sustained resource. It is also a

renewable underground resource and an important component of hydrological cycle.

The aquifers help to ensure constant water supply throughout the year where, the

surface water supplies are inconsistent. The Indus Basin is one of the large basins of

the world and Pakistan is one of the countries who share this transboundary basin (Long

et al. 2014). In Pakistan, the groundwater has emerged as a main source to meet about

90% drinking water requirements and more than 60% irrigation water supplies are also

supplemented through groundwater (Cheema et al. 2014). The agriculture sector is

called the backbone of the country. It is not only contributing about 21% in GDP, but

also providing about 24% employment opportunities in the rural areas (Qureshi et al.

2003). Increasing population, inadequate storage capacity, inconsistent surface water

supplies, ineffective water management, traditional irrigation practices and climatic

variability has increased the dependence on groundwater. The abundance of fresh

groundwater availability and lack of groundwater regulation has further hampered the

groundwater sustainability. Thus, the farmers have the liberty to drill tube wells

anywhere and pump any quantity of groundwater. Consequently, the number of private

tube wells has exponentially increased over time. More than one million tube wells

(public & private) are pumping fresh groundwater in Upper Indus Plain – Punjab

Province (Bureau of Statistics 2012). As a result of unsustainable use of groundwater,

the water table is depleting along with groundwater quality deterioration (Qureshi et al.

2008; Qureshi et al. 2010). In Upper Indus Plain, some areas are under physical

groundwater mining due to imbalance between recharge and pumping. Under such

situation, the long-term agricultural productivity is directly linked with the

sustainability of groundwater aquifer.

The effective groundwater management requires accurate assessment of

recharge and discharge processes. For this purpose, the availability of frequent and

reliable information pertaining to groundwater behavior, utilization patterns and its

response to climatic implications, helps to devise better management strategies. Being

an underground resource, the estimation of such groundwater parameters becomes

challenging due to system complexities and dynamic nature of Indus Basin.

2

Additionally, the provision of such type of detailed information in spatio-temporal

domains is generally not available in developing countries like Pakistan. The

insufficient and sporadic ground monitoring networks, data sharing issues, week

institutional capacities and professional skills are the key challenges for groundwater

management. These challenges have not only hampered the efforts of national to basin-

wide groundwater budgeting but also limit the scope of traditional tools and methods

in space and time. In the context of Pakistan, the groundwater regulation requires

systematic monitoring mechanism for successful implementation, which is presently

not in place. Thus, the prevailing situation emphasizes the need for the exploration of

potential alternate technologies to bridge information gaps in spatio-temporal domains.

1.2 Study Area

Indus is a trans-boundary basin with total area of 1,143,000 km2 (Long et al.

2014) shared by Pakistan, India, China and Afghanistan. With 60% basin area coverage

in Pakistan, Indus Basin is playing major role by meeting irrigation requirements and

considered as backbone of the agriculture-based economy of Pakistan. Originating from

Tibetan Plateau, Indus River passes through high mountains in the north and then meets

the Arabian Sea by making its way through Indus Plain. The mighty Indus along with

its five tributaries (Kabul, Jhelum, Chenab, Ravi and Sutlej) irrigates the fertile land of

Punjab and Sindh Provinces to meet most of the food and fiber requirements of the

Country. The Upper Indus Plain (UIP) consisting of major part of Punjab Province is

blessed with plenty of fresh groundwater resource in the form of unconfined aquifer.

Being sandy in nature, the aquifer gets replenishment through seepage from Indus Basin

Irrigation System (IBIS) as well as infiltration from rainwater. The IBIS is more than a

half century’s old system of irrigation, which was developed after Indus Water Treaty

(IWT) in 1960. The IWT was signed between Pakistan and India by allocating the water

rights of western rivers (Indus, Jhelum and Chenab) to Pakistan whereas; the three

eastern rivers (Ravi, Sutlej and Bias) were given to India. In the eastern rivers, India

only releases surplus water mostly in monsoon (July-September) period whereas these

rivers remain dry for most of the period and flow like drains. To maintain the water

supplies in the eastern rivers, Pakistan has developed an interconnected system of

irrigation canals called as IBIS. The main objective of this IBIS was to maintain the

water supplies in the eastern rivers by diverting the additional water from western rivers

through a network of link canals. For this purpose, various structures like barrages and

3

headworks were constructed on Indus, Jhelum, Chenab, Ravi and Sutlej rivers. The

flows of Indus and Jhelum rivers mostly consist of snow melt water from upper

catchment of Hindu Kush Himalaya (HKH) region along with rainfall whereas, Chenab

River originates from Indian Territory and carries mainly rainfall runoff water. The UIP

consists of four doabs named as Thal, Chaj, Rechna and Bari with total area of 109,418

km2 (Fig. 1.1). The doab is a local term used for the area bounded by two rivers.

Figure 1.1: Location map of study area. AJK stands for Azad Jammu and Kashmir.

Being fertile land with generally fresh groundwater availability, the doabs are

famous landmasses for agricultural production in Pakistan. Each doab is a unique

hydrological unit with varying geology and climatological conditions. These doabs are

composed of alluvial deposits; fine to coarse sand lithology dominates with lenticular

variations of clay and silt. The average depth to bedrock is 400 meter, which varies

from doab to doab ranging from 200-1000 meters whereas; the investigation studies

suggest that the depth to bedrock (DTB) has been most commonly investigated as 200-

600 meters (Bennett et al. 1967). Being dependent on tectonic activity, DTW as such

does not vary with proximity to rivers. However, the vertical heterogeneity controls the

groundwater dynamics due to lithological variations of alluvial deposits at each doab

scale (Bennett et al. 1967).

4

The geology of Thal doab is composed of unconsolidated quaternary alluvial

and aeolian deposits. A thick layer of the alluvial material is underlain by basement

rocks. These basement rocks are as old as Precambrian. The Salt Range covers one side

of upper part of Thal doab and consists of highly fractured, folded and fossiliferous

rocks of Cambrian to Pleistocene age (Bennett et al. 1967). The piedmont alluvial

deposits are found near the foothills of Salt Range whereas the central part of Thal doab

is covered with extensive surficial aeolian sand deposit. In Chaj and Rechna doabs, the

quaternary alluvium deposition is of Precambrian age, which extends on semi-

consolidated tertiary rocks. The northern part of Chaj doab is covered by Pabbi Hills,

which is a range belonging to the Himalayan foothills. Its upper part belongs to Siwalik

System with Tertiary age (Greenman et al. 1967). The Siwalik rocks form the lower

and outermost hills of the Himalayan mountain ranges with middle Miocene to early

Pleistocene age. The Kirana hills forms the oldest rocks in Rechna doab having

Precambrian age. Basically, Kirrana hills are a group of rocks found in the areas of

Sangla, Chiniot and Shah Kot. The geology of Bari doab is very much similar to Rechna

doab. The flood plains abandoned flood plains and bar upland are three dominant

physiographic features of Bari doab. The flood plain area is locally known as “Sailaba”.

It is a narrow strip of about 2-8 miles wide. The abandoned flood plains is the dominant

unit as it covers about two third area whereas the Bar Uplands forms the central part of

Bari doab.

The topography of UIP is generally flat except the Northern part where the high

elevation represents mainly Salt Range. The digital elevation model with 90-meter

spatial resolution derived from Shuttle Radar Topographic Mission (SRTM) is used for

topographic analysis. The SRTM is developed by United States of Geological Survey

(USGS). The topographic analysis of UIP shows a gentle slope from Northeast to

Southwest. The slope is higher in the northern part than Southern part ranging from

0.4m/km to 0.2m/km respectively (Alam and Olsthoorn 2014). The elevation varies

from 95 to 795 meter over UIP (Fig. 1.2). The central part of each doab is comparatively

at high elevation then its bounding rivers.

5

Figure 1.2: Topographic variations over UIP. The contours (purple color) are derived from SRTM 90

meter USGS-DEM with 5-meter interval. AJK stands for Azad Kashmir.

The UIP is densely populated area with extensive agricultural activities. Wheat,

Rice, Sugarcane, Cotton and some other cash crops (pulses, vegetables, etc.) are the

major crops. The climate of UIP varies from semi-humid to arid. The summers are very

hot (> 45 C˚) whereas the temperature during winter seasons remains around 20 C˚. The

rainfall is very erratic and mostly received during monsoon period, which prevails from

July to September. The last forty years meteorological records indicate that annual

average rainfall over Punjab Province is 580 mm (Ahmad et al. 2014). The Chaj

followed by Rechna and Thal doabs receive maximum rainfall whereas, the annual

average rainfall in Bari doab is reported to be the least (varies from 100-500 mm)

(Ahmad et al. 2014). The major characteristics of UIP are summarized in Table 1.1.

6

1.3 Hydrology

In 1950, the surface water was adequate to meet the irrigation demand in

Pakistan with 5000 m3 per capita water availability, which has been decreased to <1000

m3 (Yu et al. 2013) due to increasing water demand caused by exponential population

growth. Over the time, the water demand has increased whereas the total water

availability remained the same. Under such situation, Pakistan is enlisted among water

scarce or water insecure countries. According to Falkenmark et al. (2007), any country

having <1000 m3 per capita water availability falls under the category of water insecure

countries. The reduced storage capacity of existing reservoirs due to siltation and

dramatic increasing in population of 207.77 million people has resulted imbalance

between demand and total water availability. The projected water scenarios show that

the situation will be worst in future if the storage enhancement remains at the same pace

(Fig. 1.3). Despite of the clear need, the lack of political consensus among provinces

and financial constraints are the major hindrance in the construction of medium to mega

dams such as Bhasha and Kalabagh. Additionally, the climatic implications such as

devastating flooding events has further aggravated the situation with variable surface

water supplies. Resultantly, the pressure on groundwater has gradually increased to

meet the deficit in overall water supplies.

Table 1.1: Summary of main features of UIP

Characteristics Bari doab Rechna doab Chaj doab Thal doab

Bounded by

Rivers

Sutlej and Ravi Ravi and Chenab Chenab and

Jhelum

Chenab and

Indus

Area (Mha) 2.96 3.12 1.36 3.35

Lithology Medium to coarse

sand, silt with clay

lenses

Clay to sandy

loam

Fine to medium

Sand with Silt

Fine to coarse

sand with clay

lenses

Total Tube

wells (Million)

0.12 0.33 0.13 0.17

Precipitation

(mm)

358

Annual average

690

Annual average

778

Annual average

500

Annual average

7

Figure 1.3: Projected scenario of increasing population (red line) versus water availability (blue

color). The population is in million extracted from National Census of 1981, 1998 and 2017

conducted by Population Census Organization (PCO), Pakistan.

The aquifer properties are the important features, which control the groundwater

system response to abstraction and climatic implications (Foster and MacDonald 2014).

The Upper Indus Plain aquifer is an unconfined and well transmissive with generally

fresh quality of groundwater. The alluvial nature and composition of unconsolidated

material has provided favorable sub-surface conditions for storage and water pumping.

The vertical lithological variations in the form of clay lenses somehow limits its scope

at local to regional scale. But the role of this factor is negligible while considering the

huge volume of alluvium in Indus Plain as the average depth to bedrock is 400 meter

(Ahmad 1993). The horizontal permeability is of higher order than vertical. Being

alluvium aquifer, the sandy beds dominate in UIP. However, the existence of clay

lenses in Rechna doab is comparatively higher. In Rechna doab aquifer, the percentage

of sandy beds is about 65-75% (Mundorff et al. 1976). However, the presence of fine

to coarse sandy strata helps to replenish the groundwater system through seepage from

IBIS as well as infiltration from rainfall. The previous studies indicate a general trend

in the hydraulic conductivity of Indus Basin, which varies from >60 to <10 m/day as

we move down from upper to lower Indus basin (Ahmad 1993; Bennett et al. 1967;

Khan et al. 2008). Particularly in UIP, the values of hydraulic conductivity and specific

yield are very high due to alluvial formation (Bonsor et al. 2017). The specific yield

0

500

1000

1500

2000

2500

0

50

100

150

200

250

300

350

400

450

500

1981 1991 1998 2001 2011 2017 2020 2025 2030 2040 2050

Wat

er A

vai

lab

ilit

y (

m3/P

erso

n)

Po

pula

tio

n (

Mil

lio

n)

8

varies over UIP due to the lateral and vertical lithological heterogeneity. The analysis

of 103 pumping tests data conducted by USGS in UIP suggest that the specific yield

ranges from 0.01 to 0.42 with average value of 0.14 (Bennett et al. 1967). The minimum

value of 0.01 pertains to clay deposits whereas 0.42 represents coarse sand.

The presence of extensive irrigation system in the form of IBIS provides a good

source of surface water and groundwater interactions in UIP. In addition to rainfall, a

significant recharge is received through seepage from irrigation system consisting of 5

rivers, 13 barrages and headworks along with 12 link canals in UIP. The role of link

canals is to regulate the surface water supply in the eastern rivers, which are otherwise

having low flows. The link canals appear like rivers as these carry much high surface

water flow than the eastern rivers. After IWT, the flows in eastern rivers are very low

as India has managed to utilize maximum water pertaining to these rivers. Therefore,

the significant flows are only available during the flooding period when India releases

additional water to Pakistan. Consequently, Pakistan faces a massive additional

flooding in Jhelum and Chenab Rivers other than eastern rivers. Due to non-availability

of storages on Chenab, Ravi and Sutlej Rivers, this massive flooding turns into

devastating disaster causing a huge damage to agriculture, livestock, property and

humans. Pakistan receives about 60% of its total annual rainfall only during summer

monsoon period (Latif and Syed 2016). On the other hand, these flooding events are

natural source of aquifer replenishment as well as overhauling of the groundwater

system. Pakistan is among those countries, which are consistently facing flooding

events. Due to global warming, the increasing river flows have augmented the flood

vulnerability. The major recent flooding events include, 1992, 1994, 1995 and 2010

where the floods1992 and 2010 majorly affected the most of the Country (Federal Flood

Commission 2017). The pounding of flood water in the adjacent areas along the rivers

helps to recharge the groundwater system. Figure 1.4 provides the detailed picture of

IBIS.

9

Figure 1.4: Indus Basin Irrigation System (IBIS) in UIP. The irrigation system (river and canals) are

in blue color whereas, the red dots are the locations of barrages.

The climatic implications have further aggravated the situation by not only

shifting the rainfall patterns, seasonal change (Hanif et al. 2013) but its intensity and

duration. The rainfall events are now more intensified with short duration. The summers

have been prolonged where the winters are contracted. Interestingly, the time series

analysis of rainfall (PMD) shows that the amount of annual average rainfall has been

increased from 1971-2015 (Fig. 1.5). Quantitatively, more rainfall is now available but

practically, it is not available at the time when critically required. Hence, the surface

water supplies are irregular and insufficient to meet the existing water demand of

207.77 million people of Pakistan. The data of 19 PMD stations falling in Punjab

province has been used to generate the rainfall time series mentioned below as Fig. 1.5.

The names of these stations include; Lahore, Okara, Multan, Sahiwal, Jhang,

Gujranwala, Faisalabad, Toba Tek Singh, Sargodha, Jhelum, Sialkot, Gujrat, Chakwal,

Mangla, D.G. Khan, Bakhar, Bahawalpur, Bahawalnagar, and Rahimyarkhan.

10

Figure 1.5: Annual average rainfall variations from 1971-2015 in Punjab Province. The blue lines is

the rainfall time series generated using PMD station data. The dotted line in red color shows the

overall rainfall trend.

In Pakistan, the flood irrigation method is commonly used for irrigation. The

share of irrigation water (surface water) is allocated to farmers according to their land

holdings (Bandaragoda 1995). The farmers get their allocated share of irrigation water

once in a week (warabandi system). The increasing food and fiber requirements have

put the farmers naturally under pressure to increase the productivity. Under such

circumstances, the farmers are struggling hard by utilizing all the resources and

exercising all the available options. They are managing fertilizers and applying costly

pesticides/herbicides to increase per acre productivity. The farmers are supplementing

their irrigation demand through abstraction of groundwater (Alam and Olsthoorn 2014).

The ease of availability and flexibility for desired pumping of unregulated

groundwater resource has encouraged accelerated groundwater development in UIP.

Eventually, the cropping intensities have doubled since 1970s (Basharat and Tariq

2015) primarily through extensive groundwater abstraction. This over abstraction of

groundwater has helped significantly in achieving the food security in the Country but

has resulted a number of challenges related to both groundwater quantity and quality

(Khan et al. 2016b). Over time, the exponential growth of private tube-wells dominates

the total number of tube wells, which has reached over one million in UIP from 1985-

2015 (Fig. 1.6). This shows that the dependence on groundwater has significantly

increased over time. Figure 1.7 shows the district-wise distribution of the percentage of

total tube wells available for irrigation supplies (Bureau of Statistics 2012). The Sialkot

is top district with highest (8% of total) number of tube wells followed by Gujranwala

(7%) and Layyah (7%). The districts of Mandi Bahauddin, Muzaffargarh are at third

350

400

450

500

550

600

650

700

750

800

850

900

950

Annual

Aver

age

Rai

nfa

ll (

mm

)

11

(6%) however, 5% of the total number of tube wells exist in each of Sargodha, Jhang,

Narowal and Bakkar districts.

Figure 1.6: Groundwater development in UIP over last three decades (1985-2015)

0.0

0.2

0.4

0.6

0.8

1.0

1.2

198

4-1

985

198

5-1

986

198

6-1

987

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989

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9-2

000

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015

201

5-2

016N

um

ber

of

To

tal

Tu

bew

ells

(M

illi

on

)

Sialkot8% Gujranwala

7%

Layyah7%

Muzaffargarh6%

Mandi Bahauddin6%

Sargodha5%

Jhang5%

Narowal5%

Bakkar5%

Okara4%

Faisalabad4%

Sheikhupura4%

Kasur3%

Hafizabad3%

Gujrat3%

Khanewal2%

Nankana Sahib2%

D.G. khan2%

Vehari2%

Toba Tek Singh2%

Pakpattan2%

Sahiwal2%

Chiniot2%

Multan2%

Mianwali1%

Lodhran1%

Khushab1%

Lahore1%

Figure 1.7: District-wise Distribution of percentage of total number of tube wells in UIP (2012). The different

colors represent different districts of Punjab province. The percentages show the contribution of number of tube-

wells installed in that particular district.

12

The highest tube well density of 0.10 (tube wells per hectare) is found in Rechna

(Fig. 1.8) where about 33,000 tube wells are pumping groundwater with a total area of

3.12 million hectare. The Rechna doab is a part of famous rice belt (Narowal, Sialkot,

Gujranwala districts, etc.) in Punjab where extensive irrigation is applied for rice crop

using flood irrigation method with almost 90% dependency on groundwater. The Chaj,

Thal and Bari doabs have tube well density of about 0.09, 0.05 and 0.04 tube wells per

hectare respectively.

Figure 1.8: Doab-wise, tube wells density in UIP (2012).

In Pakistan, the contribution of groundwater in total water supplies for irrigation

has reached over 60%. The consistently huge groundwater abstraction has encountered

a number of groundwater management challenges such as groundwater depletion (Khan

et al. 2008; Rodell et al. 2009; Sufi et al. 1998; Tiwari et al. 2009) increased salinity at

shallow depths (Qureshi et al. 2008; Qureshi et al. 2010; Saeed and Ashraf 2005) and

groundwater quality deterioration (Qureshi et al. 2010). The limited surface water

Bari

Rechna

Chaj

Thal

0.00

0.02

0.04

0.06

0.08

0.10

0.12

Upper Indus Plain (Doabs)

Tu

bew

ell

Den

sity

(M

illi

on

Tu

bew

ells

per

Mh

a)

13

supplies against exceeding water demand has become groundwater a main source of

irrigation supplies in the countries like Pakistan (Siebert et al. 2010; Wada et al. 2012).

The imbalance between abstraction and recharge caused by over-exploitation has led

groundwater depletion (Döll et al. 2012; Gleeson et al. 2012; Konikow 2011; Rodell et

al. 2009; Taylor et al. 2013; Wada et al. 2010). The groundwater depletion further

impacts in lowering of groundwater levels (Famiglietti et al. 2011; Scanlon et al. 2010).

With 80 km3 per year abstraction, Pakistan is the fifth largest user of groundwater

globally (Wada et al. 2014). As an immediate impact, the water table is lowering

significantly and the areas under shallow depth to water are decreasing rapidly. The

analysis of depth to water table data collected from International Waterlogging and

Salinity Research Institute a department of Pakistan water and Power Development

Authority (WAPDA), Lahore show that the area coverage under shallow depths (<600

cm or 6 m) has decreased from 1991-2011. A reduction of about 22% in area covered

under shallow depth to water table has been experienced due to groundwater mining

during last two decades in Punjab Province (Fig. 1.9). This change has impacted to

increase the area under deep water table (> 6 m), which is reached up to 52 % of the

total area of Punjab during last two decades (1991-2011).

Figure 1.9: Variations in area coverage under different depth to water table in UIP. The green, purple

and cyan colors show area coverage (%) under maximum depth > 600 cm, 450-600 and 300-450 cm.

It is reported by Qureshi et al. (2010) that in some regions of Punjab, the water

table depletion is prevalent even about 2-3 meters per year, which is an alarming

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2006 2007 2008 2009 2010 2011

A

r

e

a

%

0-90 cm 90-150 cm 150-300 cm 300-450 cm 450-600 cm >600 cm

14

situation for groundwater sustainability perspective. Out of 43 canal commands, the

water table depletion in 26 canal commands is reported by Bhutta and Sufi (2004) due

to extensive groundwater abstraction. Khan et al. (2008) has predicted a groundwater

mining situation in lower Rechna doab with water table depletion ranging from 10-20

m over the period 2002 to 2025. A similar situation of significant water table depletion

in central Chaj and lower Bari doabs is reported also due to over exploitation of

groundwater for irrigation supplies (Ashraf and Ahmad 2008; Basharat and Tariq 2013;

Basharat et al. 2014). In Punjab province, about 20% irrigated area is under

groundwater depletion where DTW is more than 12 m (Basharat et al. 2014).

Due to intensive irrigation and excessive groundwater pumping, the average

DTW ranges from 0.5-22.8 meter over UIP as recorded by Punjab Irrigation

Department (PID), Lahore in 2010. The average DTW is about 11.7 meter. The analysis

of spatial variations in DTW show that some regions of UIP aquifer are under stress

where abstraction exceeds the recharge. To analyze the extent of depletion, a

classification has been developed by International Waterlogging and Salinity Research

Institute (IWASRI)-Pakistan by considering the depth to water table, annual depletion

rate and energy requirements for groundwater pumping. According to IWASRI

classification, the areas where DTW >18 meter are considered as highly depleted.

Therefore, it is analyzed that the most of the area of UIP is normal (3-9 m depth)

whereas, some areas of Rechna and Bari doabs are under groundwater depletion (13-

18 m depth) as shown in Fig. 1.10. The lower parts of Bari doab are especially

experiencing the groundwater mining conditions where the groundwater sustainability

is under risk.

15

Figure 1.10: Average depth to water table variations over UIP in 2010. The red color shows highest

depth to water table and is the highly depleted area. The black lines show the different districts of Punjab (Khan et al. 2016a)

In the adjoining areas of River Jhelum, specifically in upper parts of Thal and

Sargodha districts in Chaj doab, the waterlogging also exists because of excessive river

recharge. Similarly, the waterlogging is also reported in the lower parts of Thal doab

(Muzaffargarh district) due to recharge from River Indus. Basically, this is a very small

strip where the distance from the bounding rivers (Indus and Chenab) is very small.

The groundwater recharge is spatially variable in Indus Basin depending upon

the availability of enough rainfall, soil conditions and proximity to surface water or

irrigation system (Basharat and Tariq 2015). The most of the areas of UIP get recharged

through seepage from surface water system by constitutes the recharge from rivers and

canals including return flow from agricultural fields. The rainfall induced recharge is

also available but limited to either upper central parts of doabs due to subsurface

lithological irregularity (existence of clay lenses). In Indus Basin, the surface water

beautifully interacts with groundwater for its replenishment.

The groundwater quality (salinity) also varies in Punjab Province both laterally

and vertically to its marine origin. The Upper Indus Plain was once the part of Arabian

Sea, which gradually retreated and UIP came in to existence. The native groundwater

16

of UIP is saline (Ashraf et al. 2012). However, a thin layer of fresh groundwater has

developed over the time due to the recharge from surface water and rainfall. The

thickness of this fresh groundwater layer varies spatially due to variability in recharge.

In center of the doabs, the layer of fresh quality groundwater is shallow in the center of

doabs whereas it is deeper along the rivers and canals. Generally, the central parts of

all doabs get recharge mainly through rainfall, which is less as compared to recharge

induced by rivers or canals.

1.4 Literature Review

The synthesis of available literature suggest that the most of work done in the

past related to groundwater resource management in Indus Basin, remained mainly

focused on water logging and salinity issue (Qureshi et al. 2008; Qureshi et al. 2010;

Saeed and Ashraf 2005; Sufi et al. 1998), conjunctive use of surface water and

groundwater (Basharat and Tariq 2015; Khan et al. 2008), groundwater modeling by

developing different scenarios (Awan and Ismaeel 2014; Chandio et al. 2012; Khan et

al. 2008). Ashraf and Ahmad (2008) has applied FeFlow groundwater model in

combination with Geographic Information Science (GIS) and Remote Sensing (RS)

techniques to study groundwater variations in Upper Chaj doab. The RS and GIS

derived data inputs such as digital elevation mode (DEM), landuse/landcover and soil

properties were used in the model. They developed different scenarios to analyze the

aquifer response under climatic implications in terms of extreme events

(floods/droughts). The variable patterns of groundwater abstractions were also studied

and groundwater budget was computed in Upper Chaj doab. Basharat and Tariq (2015)

studied fluctuations and analyzed the variations in irrigation pumping cost at head,

middle and tail end farmers of lower Bari doab canal (LBDC). The crop water deficit

approach was used to estimate the groundwater pumping in the study area. This study

concluded that the tail-end farmers bear 2.19 times higher cost for irrigation as

compared to head-end farmers. They highlight that the groundwater depletion is more

critical at tail-end due to less availability of surface water supplies. Resultantly, the

farmers will have to bear additional cost. As an outcome, appropriate management

strategies were proposed based on the development of future scenarios.

Similarly in Rechna doab, a comprehensive study was performed (Khan et al.

2008) by using a dynamic approach to study the groundwater dynamics in Rechna doab.

In the context of excessive groundwater pumping at escalating rate, this study was

17

conducted to assess the future groundwater trends. The major findings of this study

include; depletion of groundwater levels from 10-20 m due to the limited availability

of surface water supplies in the Lower Rechna doab whereas, the Upper Rechna doab

is projected under high risk to salinization due to salt-water upconing. This upward

movement of salinization is caused by overexploitation of groundwater. The study

further concluded that if the current trends of groundwater pumping persistently prevail

in future, the leakage from river would help decrease the groundwater salinity in the

lower and middle parts of Rechna doabs. Another study was conducted by Awan and

Ismaeel (2014) in Rechna doab with focus on the assessment of groundwater recharge

in Lower Chenab Canal (LCC). The study demonstrated a new technique to map

groundwater recharge through Soil and Water Assessment Tool (SWAT) model in

combination with Surface Energy Balance Algorithm (SEBAL). They estimated

groundwater recharge through SWAT model at high spatial scale whereas a comparison

of SWAT simulated evapotranspiration with SEBAL was also performed, which

resulted a good agreement. The study concluded that an increase of about 40% in

groundwater recharge is projected in the study area. These studies have provided a very

good insight details and contributed effectively for understating the complexities of

issues and appropriate management options. However, these studies are limited in their

spatial scope and remained only focused to case study scale by covering hardly a canal

command area or a portion of doab. Recently, efforts have been made for Indus basin

scale groundwater accounting and quantification of spatial abstraction (Cheema et al.

2014). To quantify the spatial abstraction in Indus Basin, Cheema et al. (2014) used

methodology consisting of remotely sensed evapotranspiration and precipitation

products, hydrological modeling and spatially derived information pertaining to canal

water supply. They applied SWAT model as a major tool for hydrological modeling

and simulated basin scale important hydrological components. This study demonstrated

the technique to quantify the spatial patterns of groundwater abstract at high spatial

resolution of 1 km. This study concluded that during a period of one-year 2007, the

groundwater of about 68 km3 was abstracted along with groundwater depletion of about

31 km3 in the whole Indus Basin. Furthermore, the areas of Pakistani and Indian Punjab

and Haryana (India) were declared as most vulnerable to groundwater depletion.

Khan et al. (2016a) conducted a comprehensive study of groundwater resource

assessment by applying an integrated methodology consisting of geophysical surveys

18

for the quantification of usable groundwater for irrigation and drinking requirements,

application of isotope hydrology for the identification of groundwater recharge

mechanism and groundwater modeling for doab scale water balance estimation. The

study reported that the lower parts of Bari and Chaj, some parts of Rechna and Upper

Thal doabs are under groundwater stress where the high groundwater depletion was

noted in Bari doab. Khan et al. (2016b) developed a first physical based groundwater

modeling of whole Punjab Province.

These studies have successfully accounted the basin-wide groundwater

budgeting at annual scale but their reliability has hampered due to input data scarcity

(Khan et al. 2016b). The physical groundwater modeling requires a lot of observational

input data sets on various parameters for model development as well as its calibration

and validation purpose, which is hardly available in developing countries like Pakistan

(Brunner et al. 2007; Moore and Fisher 2012). The availability of spatially well

distributed and reliable input information is primarily important for groundwater

modeling to produce reliable strategies (Singh 2014; Wondzell et al. 2009). The

reliability of input data is directly proportional to the accuracy of modeling results.

Traditionally, the hydrological observations are available in the form of point

measurements however; the models require more distributed type regional information

or picture for accurate simulation. Usually, the models accept point data and then it is

interpolated to generate spatially distributed information. The physical groundwater

modeling is very effective for the assessment, monitoring and devising management

strategies but input data scarcity limits its role for basin scale applications.

Despite of the selection of a very good groundwater model with high

professional expertise, the lack of sufficient and reliable input information could

hamper the credibility by producing under/over estimations of modeling results (Singh

2014). In developing countries like Pakistan, the data paucity is a big challenge for the

hydrologists. The ground observations are limited in their spatial and temporal domains

due to week measurement network related most of the critically required parameters

such as rainfall, temperature, groundwater levels, stream discharges, etc. In Pakistan,

Scarp Monitoring Organization (SMO), a department of Pakistan Water and Power

Development Authority (WAPDA), Lahore has maintained a network of piezometers

in the Indus Basin. They have installed these piezometers with the objective to cover

all canal command areas. SMO collect depth to water table (DTW) information along

19

with groundwater quality biannually. These piezometers were installed a long time ago

during 1980s. Most of them are now redundant due to lack of proper maintenance.

Those, which are still operational, the data is only available before and after monsoon

period. The role of summer monsoon in the groundwater hydrology of Indus Basin is

very imperative. It comes with heavy rainfalls and lasts for almost three months from

July to September. It facilitates to somehow in the replenishment of groundwater

system as a significant rise in water levels is observed. Such a data at biannual

frequency is practically insufficient to support any management strategy.

Another issue with piezometric point information is its sensitivity to local

events/phenomena. The point data is always good to capture the local events therefore,

the water level fluctuation method is not considered very accurate for regional

assessment of groundwater depletion. This is due to very reason that the regional

phenomenon dominates the local, which reduces the accuracy of results at regional

scale. Therefore, the hydrologist more relay on groundwater models for accurate

quantification of recharge or groundwater abstract and future predictions.

The geophysical and isotopic applications are also very good in performance

for the groundwater resource assessment and analyzing the recharge mechanism. The

environmental isotopes are very help to determine the groundwater flow patterns and

studying the long-term groundwater system behavior. The methods are field oriented

as the field surveys are their integral part. The field activities involve a lot of time,

human resource and financial requirements, which limits their role as a continuous

activity for basin scale groundwater monitoring and management.

The lack of centralized water resource information system is another challenge

for the hydrologist while analyzing the long-term system behavior and its dynamics

under climatic variabilities. Due to non-appreciable trend of data sharing, a lot of efforts

are required to gather required information form relevant agencies in Pakistan. The non-

availability of data in digital format (mostly in hardcopy) is another challenge.

Recently, Pakistan Council of Research in Water Resources (PCRWR), a national

research organization working under Ministry of Science and Technology, Government

of Pakistan has taken an initiative in collaboration with Asian Development Bank

(ADB) to develop a common water resource data platform. The objective of this effort

is to provide a centralize water resource information system to facilitate the researchers,

hydrologists and policy makers for long-term planning and management activities. As

20

per plans, it is initially started from Balochistan and further will be expanded to national

scale by bringing all the provinces together.

The remote sensing technology has become very popular among researchers and

is increasingly used in hydrological applications. The literature suggest that the remote

sensing based products have been used as input datasets for groundwater modeling in

Indus Basin (Ashraf and Ahmad 2008; Awan and Ismaeel 2014; Cheema et al. 2014)

and in Ganges Basin (Bhanja et al. 2017; Bhanja et al. 2016a; Bhanja et al. 2016b;

Mukherjee et al. 2015). In integration with groundwater modeling, remote sensing is

also used for the improvement (such as calibration and validation) of groundwater

models (Brunner et al. 2007). However, the researchers have not yet benefited fully

from the true potential of remote sensing technology.

The Gravity Recovery and Climate Experiment (GRACE) is the National

Aeronautics and Space Administration (NASA) twin gravity satellite, which was

launched in 2002 in collaboration with German Space Centre (GFZ). It very accurately

maintains its distance between two satellites through laser. The GRACE is very

sensitive to changes in gravity and if a small change in gravity happens on or below the

surface, it gets recorded as anomaly (Rodell et al. 2009). Very uniquely, it senses the

complete water cycle by all covering its all components. The GRACE is very effective

tool to get the information about complete vertical profile starting from snow/glaciers

down up to groundwater (Longuevergne et al. 2010). The GRACE is capable to provide

gravity anomalies, which are useful to extract changes in Total Water Storage (TWS)

at 10 daily to monthly scale. The GRACE has facilitated the research by bridging the

input data gaps and very useful for global hydrological applications due to its global

coverage (Famiglietti et al. 2011; Rodell et al. 2009; Tiwari et al. 2009). By design, the

GRACE is a coarse spatial resolution satellite (~300-350 km). It is said that the changes

in gravity are induced by the redistribution of mass under, on and above the earth

surface (Wahr et al. 1998). The basic principal of the GRACE based groundwater

monitoring is assumed that the changes in gravity are essentially induced by changes in

mass of subsurface rocks, which is dependent on water content. A water bearing rock

have higher density (mass) then dry rock and therefore, changes in density cause

variations in gravity to which the GRACE is sensitive enough.

The GRACE data collection mechanism is also unique. GRACE mission is a

combination of two satellite, which are about 220 km apart from each another with

21

altitude of about 450 km. The distance between two satellites is maintained through a

very precise laser system. When there is any change (more mass) in gravity due to

variations in mass over or under the earth surface, the leading satellite slows down

however, the following satellite continue its speed until it approaches the same region

of leading satellite. Due to this change in distance induced by change in gravity, it is

recorded as gravity anomaly. Similarly, when the leading satellite crosses the high mass

region, it again follows its normal speed by creating a variation in the distance with

following satellite. As the following satellite is still in the high mass and high density

region, so its speed is bit slower the leading satellite. Therefore, in a similar fashion,

GRACE collects the gravity data (anomalies) due to changes in distance induced by

variations in mass on the earth surface. The temporal frequency of GRACE data is 10

daily to monthly however, the data becomes publically available after 1-2 months as lot

of initial data processing is involved.

Having global coverage with reasonable spatio-temporal frequency, the

GRACE is potentially appropriate to apply for basin scale water accounting. It is widely

used as a credible tool for the quantification of groundwater abstraction and recharge

processes globally. Recently, many studies have demonstrated its potential as a

scientific tool for successful monitoring of groundwater resource, its abstraction,

groundwater dynamics, spatio-temporal changes in different components of water

cycle, drought monitoring and assessment of flooding impacts in relation to

groundwater recharge (Famiglietti et al. 2011; Feng et al. 2013; Rodell et al. 2009;

Scanlon et al. 2012; Strassberg et al. 2009; Strassberg et al. 2007; Tiwari et al. 2009).

The GRACE has been extensively used for groundwater depletion in various regions of

the world and famous river basins such as Indus Basin (Jin and Feng 2013; Rodell et

al. 2009; Tiwari et al. 2009), High Plain Aquifer (Strassberg et al. 2009; Strassberg et

al. 2007), Central Valley-California (Famiglietti et al. 2011; Scanlon et al. 2012),

Mississippi River Basin (Rodell et al. 2007), Illinois State (Swenson et al. 2006), Congo

Basin (Lee et al. 2011), North China (Feng et al. 2013), etc.

The situation analysis identifies the gaps to apply the GRACE satellite as a

scientific and cost-effective tool for groundwater resource management in Indus Basin

of Pakistan where the in-situ data and groundwater modeling tools are limited to large

spatial domains.

22

1.5 Problem Statement

The groundwater aquifers provide a cushion during extreme climatic events and

ensure the agricultural sustainability by regulating the irrigation supplies in agrarian

countries like Pakistan. Therefore, the agricultural sustainability is directly linked with

the sustainability of groundwater relating to food security perspective. For sustainable

groundwater resource management, the groundwater abstraction and recharge are the

primarily important parameters of groundwater budgeting. However, the non-

availability of reliable and frequent information in spatio-temporal domains, poses a

big challenge for basin wide accounting and devising appropriate management

strategies in developing countries like Pakistan. The input data paucity, limitations of

groundwater models, complexities of the groundwater system itself and climatic

implications, provides motivation to explore alternate tools. The situation analysis

identifies the gap to apply Gravity Recovery and Climate Experiment (GRACE)

satellite as a scientific and cost-effective tool for groundwater resource management in

Indus Basin of Pakistan where the in-situ data and groundwater modeling tools are

limited to cover large spatial domains. This study evaluates the potential of GARCE

satellite-based methodology as science grade tool for the monthly monitoring of

groundwater storage changes as well as its effectiveness at operational groundwater

management scales like doabs.

1.6 Objectives

The specific objectives of this study are;

a) To evaluate the GRACE satellite integrated VIC model approach for the

estimation of groundwater storage (GWS) at regional to sub-regional scales.

b) To assess the impact of the GRACE based GW storage estimation and

monitoring methodology to enable decision making over conventional

approaches.

c) To design statistical approach for the prediction of 30-180 days groundwater

storage variations and estimate its level of uncertainty to enable decision-

making.

23

CHAPTER 2

Datasets and Methodology

2.1 GRACE Datasets

The GRACE data is globally available as monthly time variable gravity fields.

For the extraction of the GRACE based monthly TWS changes, the CSR Release 05

Level-2 data product called “CSR RL05 L2” is used in this study. The GRACE monthly

gravity field datasets are provided by the NASA PODAAC

(ftp://podaac.jpl.nasa.gov/allData/grace/L2/CSR/RL05/). The Centre for Space

Research at University of Taxes (CSR) is one of the four key GRACE data processing

centres as a part of Science Data System (SDS). The other three centres are, NASA Jet

Propulsion Laboratory (JPL), the German Space Centre (GFZ) and Research Group for

Space Geodesy (GRGS) at French Space Agency (CNES) which the GRACE raw data

is processed (Fig: 2.1).

Figure 2.1: GRACE mission data flow describes the process of data collection and different data

processing levels (adopted from http://www.csr.utexas.edu/grace/)

The GRACE data is basically divided in to three levels. The non-distributable

raw data, which is directly downloaded from the GRACE satellite is labeled as Level-

1A. The 2nd level product is Level-1B, which is further processed form of Level-1 data

ftp://podaac.jpl.nasa.gov/allData/grace/L2/CSR/RL05/

http://www.csr.utexas.edu/grace/

24

to produce monthly gravity field estimates in form of spherical harmonic coefficients.

The 2nd level data is actually produced by combining the mean or static gravity

estimates based on several years. Under CSR RL05 Level-2 data products, there are

three types of monthly datasets available such as GSM, GAC and GAD. The GSM

contains the coefficients for the earth monthly gravity field with the highest coefficients

of degree and order 96. The GAC is just the average monthly solution of gravity fields

whereas, the GAD is an ancillary data product that represents the mean ocean bottom

pressure. The GRACE data (CSR-RL05 Level-2) in the form of monthly gravity fields

or anomalies from 2003-2010 is used in this study.

2.2 Piezometric Datasets

The piezometric water level data is used for the calibration and validation of the

GRACE derived groundwater storages information. This data is collected from SCARP

Monitoring Organization (SMO), Pakistan Water and Power Development Authority

(WAPDA). On the basis of long-term data availability and spatially well distributed,

about 167 piezometric locations have been selected and used covering the whole Upper

Indus Plain (Punjab Province). The water level measurements in the form of depth to

water table (DTW) are collected and available only on bi-annual frequency, which is

pre-monsoon (May-July) and post-monsoon (September-December). The SMO has

installed these piezometers strategically by covering all the canal command areas in

Punjab. There are two main reasons for this biannual data collection. Firstly, the data

collection form these piezometers is manual. Therefore, the data collection at 10-daily

to monthly scale will require lot of resources, which are not easy to manage. Secondly,

the groundwater system is mainly influenced by monsoon system. A considerable

change in the water levels appears while comparing pre-monsoon and post-monsoon

season. Another challenge is that even at biannual scale, the DTW data collection takes

2-3 months to cover whole UIP. Under such situation, those months have been selected

for comparison with the GRACE data during which most of the records were available.

2.3 Variable Infiltration Capacity (VIC) Model Datasets

In this study, the role soil moisture (SM) and surface runoff (SR) information is

very important. This information is required to separate groundwater signal from the

GRACE derived TWS. This information is derived through hydrological modeling by

25

applying the VIC model. The VIC model was developed by Liang et al. (1994). The

VIC simulated monthly SM and SR data from 2003-2010 is used in this study.

2.4 Methodology

The methodology of this study includes mainly two components. The first

component explains the data processing related GRACE gravity fields and then

extraction of TWS anomalies. However, the second component deals with the

hydrological modeling for the simulation of soil moisture information and surface water

fluxes. The detailed flow chart methodology is given at Fig. 2.2.

Under this satellite integrated numerically down-scaling techniques, it is

explained here that GRACE-TWS solution (1°×1° or 300 km × 300 km) has been first

retrieved from its original gravity anomalies by using CSR-RL05 data having 3°×3°

spatial resolution at monthly scale. This has been achieved by involving several

processing steps, filtering techniques and signal restoration. However, the third level of

GRACE-based GWS at 0.1°×0.1° (10 km × 10 km) has been derived by using numerical

down-scaling technique in which the VIC model simulated output of soil moisture and

surface fluxes (0.1°×0.1°) has been used. Finally, GRACE derived GWS anomalies

have been estimated to analyze the temporal changes in groundwater storage underlying

of Upper Indus Plain in Pakistan. This technique is called dynamic numerical

downscaling in with simulation of physical parameters through dynamic hydrological

has been integrated with satellite data to effectively estimate the changes in terms of

groundwater storage anomalies.

26

Figure 2.2: Flow chart methodology for the estimation of groundwater storage anomaly

A ten-step methodology is adopted to process the GRACE gravity anomalies

(from 2003-2014) for the extraction of TWS. The detailed explanation of this ten step

GRACE data processing approach is given under chapter-3. The time series monthly

mass variations anomalies called equivalent water height (EWH) or total water storage

(TWS) are extracted from time variable monthly gravity field estimates. The GARCE

data processing, filtering and smoothing techniques is based on the methods developed

by Guo et al. (2010). The monthly VIC model simulated soil moisture (SM) and surface

runoff fluxes (SR) are used for the separation of groundwater signal. The VIC model

simulations were setup at 0.1˚× 0.1˚, which is approximately 10 km ×10 km whereas;

the GRACE derived TWS anomalies were at 1˚×1˚ scale. Here the numerical

downscaling technique has been adopted for the downscaling of the GRACE derived

groundwater storage (GWS) anomalies. Basically, the VIC simulated SM and SR at

27

0.1˚× 0.1˚ scale has been used as guiding information for the downscaling of the

GRACE TWS available at 1˚×1˚ scale. For this purpose, the GRACE TWS (1˚×1˚) cell

were resampled to the resolution of the VIC to maintain the consistency. It is mentioned

here that before the separation of GWS, SM and SR have been added to make SMR and

then subtracted from long-term average to estimate SMR anomalies. This was helpful

to make the datasets comparable with the GRACE TWS anomalies.

According to Rodell et al. (2009) and Longuevergne et al. (2010), the changes

in TWS is a function of changes in Soil Moisture (SM), Surface Water (SW),

Groundwater Storage (GWS), Snow water Equivalent (SWE) and Biosphere (BIO).

ΔTWS =ΔGWS+ΔSM+ΔSW+ΔSWE+ΔBIO (2.1)

Based on the hydrological characteristics of the study area, it is assumed that

the soil moisture, surface runoff and groundwater abstraction are the dominant

components controlling the hydrology of Upper Indus Plain. Therefore, the equation

2.1 becomes;

ΔGWS =ΔTWS - (ΔSM+ΔSW) (2.2)

Finally, by following equation (2.2), the groundwater storage anomalies have

been extracted. For calibration and validation purpose, the DTW data is used from

2003-2010. Due to the lack of temporal uniformity in piezometric recordings, the

seasonal average method is used for both DTW and the GRACE-GWS. To compare the

GRACE derived groundwater storage anomalies with piezometric data, the depth to

water table information was converted in to water level and then estimated storage

change using average specific yield. The further details pertaining to the calculation of

the GRACE as well as piezomtric groundwater storage anomalies are available as

Appendix-E. According to Bennett et al. (1967), the value of average specific yield is

calculated as 0.14 for UIP which ranges from 0.01 to 0.42. As such, there is no

systematic spatial variability analyzed in the study area. However, the specific yield

varies with lithological changes especially decreases with clay content which is not

having any regular trend. The clay exists in the form of irregular clay lenses having

vertical and lateral heterogeneity (Bennett et al. 1967). Therefore, there is considerable

vertical hydrogeological heterogeneity in the UIP due to the presence of clay lenses.

28

Due to these uncertainties, the average value of safe yield as 0.12 has been used for

calculations on the safe side.

After the conversion of piezometric DTW into groundwater storage anomalies,

the calibration with the GRACE-GWS was performed from 2003-2010 at each doab

scale. Based on the calibration results, a statistical relationship (Figs. 5.27, 5.29, 5.31

& 5.33) has been developed, which is validated over the period 2008-2010 to make

groundwater projection for next 6 months. This is might be helpful for the groundwater

managers to devise appropriate strategies by knowing about the expected changes in

the groundwater system. This technique has been tested at each doab scale (Figs. 5.19,

5.20, 5.23 & 5.24) and its accuracy depends upon the accuracy of the GRACE-GWS.

By following this methodology, two different scenarios have been tested (Table

5.1). Under first scenario, the performance of the GRACE-GWS is evaluated with is-

situ water level observations at actual the GRACE resolution (1˚×1˚ scale) at the scale

of UIP. Whereas in second scenario, the GRACE-GWS anomalies are estimated at

0.1˚× 0.1˚ and then accuracy evaluation has been performed at each doab scale to see

the feasibility of the GRACE-GWS for operational groundwater resource management

in Indus Basin. These doabs are the effective operational scales in Pakistan referring to

sustainable groundwater management in Upper Indus Plain aquifer. For automatic data

processing and raster analysis, the model builder and spatial analyst extensions of Arc

GIS software (version 10.4) have been used. The “asci files” pertaining to TWS

anomaly and the VIC simulations have been converted in to raster format for further

spatial analysis. By using model builder utility, various models have been developed in

Arc GIS for automatic data processing, analysis and extraction of SMR, TWS and GWS

anomalies over UIP as well as doab scales over study area (Appendix-A).

29

CHAPTER 3

GRACE Data Processing and Hydrological Modeling

3.1 Relation Between Surface Mass and Gravity

It is commonly believed that the shape of geoid is the best representation of

earth gravity field (Wahr et al. 1998). Basically, the geoid is an equipotential surface,

which corresponds to mean sea level. The shape of geoid “N” is usually expanded as a

sum of spherical harmonics coefficients (Duan et al. 2009; Wahr et al. 1998), which is

explained as below;

𝑁(𝜃, ∅) = 𝑎 ∑ ∑ �̃�𝑙𝑚𝑙𝑚=0

∞𝑙=0 (cos𝜃)(𝐶𝑙𝑚 cos(𝑚∅) + 𝑆𝑙𝑚 sin(𝑚∅)) -----------------(3.1)

In this equation, a is the radius of the Earth, 𝜃 and ∅ are colatitude and

longitude 𝐶𝑙𝑚 and 𝑆𝑙𝑚 are spherical coefficients, 𝑙 and 𝑚 are the integers such that 0 ≤

𝑚 ≤ 𝑙. Here �̃�𝑙𝑚 is first kind of Legendre associated (normalized) functions, which can

be explained as;

�̃�(𝑥) = 𝑁𝑙𝑚 × 𝑃𝑙𝑚(𝑥) ----------------------------------------------------------------------(3.2)

where 𝑁𝑙𝑚 is,

𝑁𝑙𝑚 = √(2 − 𝛿𝑚0)(2𝑙 + 1)(𝑙−𝑚)!

(𝑙+𝑚)!

Then general form of associated Legendre function will become;

�̃�𝑙𝑚(𝑥) = (−1)𝑚(1 − 𝑥2)𝑚

2𝑑𝑚

𝑑𝑥𝑚 𝑃𝑙(𝑥) is from Legendre function------------------(3.3)

𝑃𝑙(𝑥) =1

2𝑙𝑙!

𝑑𝑙

𝑑𝑥𝑙(𝑥2 − 1)𝑙-------------------------------------------------------------------(3.4)

In case of the GRACE satellite, it provides numerical values for 𝐶𝑙𝑚 and 𝑆𝑙𝑚

variable complete to degree (𝑙) and order (𝑚) of 60°. The time variable changes in

geoid ∆𝑁 (which basically representative the change either change in geoid from one

time to another or as the difference between 𝑁 at one time with static reference) are

induced by redistribution of mass density in the earth. Hence, the changes in geoid (∆𝑁)

cause changes in spherical harmonics ∆𝐶𝑙𝑚 and ∆𝑆𝑙𝑚. According to Chao and Gross

(1987), the ∆𝑁 is caused by density redistribution ∆𝜌(𝑟, 𝜃, ∅);

30

{∆𝐶𝑙𝑚∆𝑆𝑙𝑚

} =3

4𝜋𝑎𝜌𝑎𝑣𝑒(2𝑙+1)∫ ∆𝜌(𝑟, 𝜃, ∅) �̃�𝑙𝑚(𝑐𝑜𝑠𝜃) × (

𝑟

𝑎)

𝑙+2{𝑐𝑜𝑠(𝑚∅)

𝑠𝑖𝑛(𝑚∅)} 𝑠𝑖𝑛𝜃𝑑𝜃𝑑∅𝑑𝑟----------(3.5)

Where r is the radial distance to the point of interval 𝜌𝑎𝑣𝑒 = 5517 kg/𝑚3 and

represents the average density of earth. Clm and Slm are spherical coefficients, �̃�𝑙𝑚 is first

kind of Legendre associated (normalized) functions, surface density (∆𝜌), 𝜌ave is

average density and a is distance in meters (a = 6378137).

Suppose, the earth surface is approximated as spherical shell, H is the thickness

of the layer where ∆𝜌 density redistribution is concentrated. Practically, this layer H

should be thick enough to include; the atmosphere, oceans, ice caps, and below-ground

water storage with significant mass fluctuations. According to Wahr et al. (1998), the

thickness of the layer H is commonly determined by the thickness of atmosphere, which

is considered of the order of 90 km.

The surface density is described as 𝑚𝑎𝑠𝑠/𝑎𝑟𝑒𝑎. Therefore, the change in

surface density (∆𝜌) is defined as radial integral ∆𝜌 through this thin layer;

∆𝜎(𝜃, ∅) = ∫ ∆𝜌(𝑟, 𝜃, ∅)𝑑𝑟 -----------------------------------------------------------------

(3.6)

Under the GRACE now, the maximum degree to which monthly gravity field

solutions are truncated and recoverable time variable gravity signals are concentrated

is up to 90 degree (𝑙 < 𝑙 𝑚𝑎𝑥 = 90). The maximum possible truncated limit and

recoverable time variable signals are concentrated is 𝑙 ≅ 100 (Wahr et al. 1998).

Suppose, H is thin enough such that (𝑙𝑚𝑎𝑥+2)+𝐻

𝑎≪ 1 and (𝑟/𝑎)𝑙+2 then equation

3.5 can be rearranged as;


}𝑠𝑢𝑟𝑓𝑎𝑐𝑒 𝑚𝑎𝑠𝑠

=3

4𝜋𝑎𝜌𝑎𝑣𝑒(2𝑙+1)∫ ∆𝜎(𝜃, ∅) �̃�𝑙𝑚(𝑐𝑜𝑠𝜃) {𝑐𝑜𝑠(𝑚∅)

𝑠𝑖𝑛(𝑚∅)} 𝑠𝑖𝑛𝜃𝑑𝜃𝑑∅-----(3.7)

Now, the Equation 3.7 explains the direct change contributed in geoid as a result

of the gravitation attraction of redistribution of surface mass. During the process of

redistribution of surface mass in which, it loads and deforms the subsurface solid earth

causes an additional contribution to geoid (Wahr et al. 1998). So, Equation 3.7 can be

written as;


}𝑠𝑜𝑙𝑖𝑑 𝑒𝑎𝑟𝑡ℎ

=3𝑘𝑙

4𝜋𝑎𝜌𝑎𝑣𝑒(2𝑙+1)∫ ∆𝜎(𝜃, ∅) �̃�𝑙𝑚(𝑐𝑜𝑠𝜃) {𝑐𝑜𝑠(𝑚∅)

𝑠𝑖𝑛(𝑚∅)} 𝑠𝑖𝑛𝜃𝑑𝜃𝑑∅ -------------(3.8)

31

In Equation 3.8, 𝑘𝑙 represents the load deformation Love number of degree 𝑙

(Chao and Gross 1987). Then, the sum of Equation 3.7 & 3.8 will represent the total

geoid change as explained in Equation 3.9.


} = {∆𝐶𝑙𝑚∆𝑆𝑙𝑚

}𝑠𝑜𝑙𝑖𝑑 𝑒𝑎𝑟𝑡ℎ

+ {∆𝐶𝑙𝑚∆𝑆𝑙𝑚

}𝑠𝑢𝑟𝑓𝑎𝑐𝑒 𝑚𝑎𝑠𝑠

----------------------------------------(3.9)

The relation of ∆𝐶𝑙𝑚 and ∆𝑆𝑙𝑚 with ∆𝜎 can be compacted by expanding ∆𝜎;

∆𝜎(𝜃, ∅) = 𝑎𝜌𝑤 ∑ ∑ �̃�𝑙𝑚𝑙𝑚=0

∞𝑙=0 (cos𝜃)(∆ �̃�𝑙𝑚𝑐𝑜𝑠(𝑚∅) + ∆�̃�𝑙𝑚𝑠𝑖𝑛(𝑚∅))-------(3.10)

where, 𝜌𝑤 is the density of water (𝜌𝑤 = 1000 kg/m3). As the density of water

is included here so ∆Clm and ∆𝑆𝑙𝑚 will become dimensionless. In this Equation 3.10,

∆𝜎/𝜌𝑤 expresses the change in surface mass in term of equivalent water thickness. It

may also be noted that the normalized �̃�𝑙𝑚 satisfies that;

∫ �̃�2𝑙𝑚

𝜋

0(𝑐𝑜𝑠𝜃)𝑠𝑖𝑛𝜃𝑑𝜃 = 2(2 − 𝛿𝑚,0)------------------------------------------(3.11)

The Eq.10 become;

{∆�̂�𝑙𝑚

∆�̂�𝑙𝑚} =

1

4𝜋𝑎𝜌𝑤∫ 𝑑∅ ∫ ∆𝜎

𝜋

0

2𝜋

0(𝜃, ∅)�̃�𝑙𝑚(𝑐𝑜𝑠𝜃) {𝑐𝑜𝑠(𝑚∅)

𝑠𝑖𝑛(𝑚∅)} 𝑠𝑖𝑛𝜃𝑑𝜃 -----------------------(3.12)

The relation between (∆𝐶𝑙𝑚, ∆𝑆𝑙𝑚) 𝑎𝑛𝑑 (∆�̂�𝑙𝑚, ∆�̂�𝑙𝑚) can be derived using Eq.3.7,

3.8, 3.9 & 3.12.


} =3𝜌𝑤

𝜌𝑎𝑣𝑒

1+𝑘𝑙

2𝑙+1{

∆�̂�𝑙𝑚

∆�̂�𝑙𝑚} ------------------------------------------------------------------(3.13)

or conversely,

{∆�̂�𝑙𝑚

∆�̂�𝑙𝑚} =

𝜌𝑎𝑣𝑒

3𝜌𝑤

1+2𝑙

1+𝑘𝑙{∆𝐶𝑙𝑚

∆𝑆𝑙𝑚} ------------------------------------------------------------------(3.14)

Finally, the surface mass density changes can be estimated through the gravity

changes (∆𝐶𝑙𝑚, ∆𝑆𝑙𝑚) supplied by GRACE satellite,

∆𝜎(𝜃, ∅) =𝑎𝜌𝑎𝑣𝑒

3∑ ∑

2𝑙+1

1+𝑘𝑙

𝑙𝑚=0

∞𝑙=0 �̃�𝑙𝑚(cos𝜃)(∆ �̃�𝑙𝑚 𝑐𝑜𝑠(𝑚∅) + ∆�̃�𝑙𝑚 𝑠𝑖𝑛(𝑚∅))--------(3.15)

Eq. 3.15 is the most commonly used for surface mass density with equivalent

water height (EWH). After further simplification of Equation 3.15;

32

∆ℎ(𝜃, ∅) =𝑎𝜌𝑎𝑣𝑒

3𝜌𝑤∑ ∑

2𝑙+1

1+𝑘𝑙

𝑙𝑚=0

∞𝑙=0 �̃�𝑙𝑚(cos𝜃)(∆ �̃�𝑙𝑚𝑐𝑜𝑠(𝑚∅) + ∆�̃�𝑙𝑚𝑠𝑖𝑛(𝑚∅)) --------(3.16)

This is final expression used for the defining the relation between surface mass

density, gravity and water height.

3.2 Processing of Spherical Harmonic Coefficients

The codes developed by Duan et al. (2009) are used for the GRACE data

processing. The details of step by step data processing approach are shown in Fig. 3.1.

Figure 3.1: Step by step methodological approach for the GRACE data processing

3.2.1 Step-0: Rename Data Files

This step is merely to change the name and extension of data file to make them

end with _0.txt. The GRACE L2 monthly GSM-2 data files include the following date

span information;

GSM-2_2002095-2002121_0021_UTCSR_0096_0005

where,

GSM-2 = Data product level 2 containing highest monthly

gravity fields

2002095 = Starting date (95th day of 2002)

2002121 = End date (121st day of 2002)

33

0021 = Number of days with data: 21

UTCSR = University of Taxes, CSR data product

0096 = Maximum degree and order of SHCs

0005 = Number of release

The average of the starting and end dates is used as the date of the solution for

each month (for above example, 107 will be considered as solution date for this file).

The output files have the following format YYYY DDD XXX.txt, where YYYY is the

year, DDD is the median date of data span, and XXX is a user assigned suffix. The

input information is given in the file Rename L2 Files.txt. The first line (cp) is the

command for copying files of the OS. For example, windows: copy, Unix/Linux: cp.

The second line has two integers. The first one is the location of the first digit of year

in the original data file name and the second one is the number of digits of year. The

third line has two integers. The first one is the location of the first digit of the first day

in the original data file name. The second one is the number of digits for the first day.

The fourth line has two integers. The first one is the location of the first digit of the last

day in the original data file name. The second one is the number of digits for the last

day. Whereas, the fifth and sixth lines are string to be included as suffix in output file

names, i.e., XXX and number of data files. The following lines are the data files. One

file name per line. An example of the content of Rename L2 Files.txt is as under;

copy

6 4

10 3

18 3

_0

83

GSM-2_2002102-2002120_0018_UTCSR_0096_0005

GSM-2_2002121-2002139_0019_UTCSR_0096_0005

Rename L2 Files.cpp is the program name used for completing Step-0.

3.2.2 Step-1: Extract SHCs

This step is to process the data file for the extraction of Spherical Harmonics

Coefficients (SHCs) using the code Extract SHCs.cpp. Input and output file names are

assigned in the file Extract_SHCs.txt. The first line is the maximum degree and order.

34

The second line is the total number of data files, and the following are input and output

names for each data file. The following is an example of Extract_SHCs.txt file:

60

83

2002_289_0.txt 2002_289_1.txt

2002_319_0.txt 2002_319_1.txt

The output format is l m c s in each line. Data in output files are in the order m

= 0, 1, 2, and then l = m, m+1, ....

The output of this step is stored in the files as suffix _1.txt.

3.2.3 Step-2 & 3: Geocentre & Truncation

The step-2 is performed to replace degree 1 and 2 terms with Satellite Laser

Ranging (SLR) estimates. The 3rd step is to truncate the SHCs up to degree 60 by

eliminating the degrees from 61 to 96. The reason is that the higher degree and order

coefficients include larger error, which are preferred to be omitted in certain

applications. So, there is a need to truncate the SHCs to a lower maximum degree and

order. For Step-2 &3, Geocentre_J2_CSR.cpp and Truncate SHCs.cpp; codes are used

for data processing. The output file contains the following information;

The first line is the maximum degree of input SHCs. The second line is the

maximum degree of output SHCs. The third line is the number of files of SHCs. The

rest of the lines are the data lines.

3.2.4 Step-4 & 5: Average Calculation and Reference Subtraction

Under Step-4; the GRACE L2 data are normally used for studying mass

changes. So, a reference field is normally subtracted from all the monthly solutions. In

most cases, the reference field is the average in a certain time span and the same long-

term monthly average is calculated. The mean SHCs are calculated from all 140

monthly files from all 140 monthly SHCs. The Step-5 is performed to get the monthly

anomalies of SHCs by subtracting the average (Step-4) from all 140 monthly files. In

this way, Step-4&5 are to get monthly anomalies. The codes, Average_SHCs.cpp and

Subtract_Reference_SHCs.cpp are used to get output files. The output file after Step-5

is named as 2003_107_4.txt.

35

3.2.5 Step-6 & 7: Remove PGR and Decorrelation Filter

Step-6 is performed to remove Post Glacial Rebound (PGR) from signal using

PGR model or Glacial Isostatic Adjustment (GIA) called Paulson_60 (which is only

generated up to 60 degrees). PGR data is available in terms of crustal uplift and relative

sea level (RSL) rise. For PGR, sea surface is assumed equipotential and is referred to

as geoid. The PGR-geoid follows global sea level rise, it differs from the geodetic geoid

by an offset. Actually, in PGR models, RSL changes are equivalent to PGR-geoid

changes with respect to the crust, which are provided even on continent as well. The

geoid change is referred as the sum of crustal uplift and RSL changes.

The purpose of decorrelation filter is to remove the correlated errors in the data.

Based on Duan et al. (2009) the designed filter is very much capable to left portions of

lower degree and order unchanged whereas as the remaining are filtered using a moving

window with polynomial fit high pass filtering technique (Shum et al. 2011). The data

filtering by decorrelation is essentially required to remove the correlated errors

associated with spherical harmonic coefficients (SHCs) of higher degree and order.

These higher degree and order SHCs are affected more by correlated noises than lower

degree and order (Kummerow et al. 1998; Shum et al. 2011; Swenson et al. 2006;

Wouters et al. 2014). These correlated errors are further studied by Duan et al. (2009)

and revealed that the SHCs with same degree and parity are also correlated with each

other. The output fine was resulted as 2003_107_6.txt after Step-7.

3.2.6 Step-8 & 9: Transform SHCs to Mass and Mass to Grids

The Step-8 is to computer Equivalent Water Height (EWH) or Terrestrial

Storage (TWS) anomalies (mm). The data is then converted from SHCs to monthly

mass changes (anomalies). The Step-9 is to transform the mass anomalies in to regular

1˚ × 1˚ grids. It helps to map the GRACE TWS monthly anomalies for further analysis

the temporal changes in terrestrial water storage over any specific region of interest.

The output file of Step-9 is named as 2003_107_8.txt.

For error analysis, the review of literature reveals that GRACE time series

contains both errors of random as well as systematic nature. The random errors are

associated with spectral degree (Wahr et al. 2006) whereas, the systematic errors

increase as a function of spectral order (Swenson and Wahr, 2006). Under such a

scenario, post-processing techniques including (Gaussian Smoothing, De-Correlation

36

and Leakage Reduction) are applied to original GRACE spherical harmonic

coefficients to improve the signal to noise ratio.

3.2.7 Step-10: Gaussian Smoothing and Leakage Reduction

The data smoothing is basically post-processing technique, which is applied

after the extraction of equivalent water height (EWH) or total water storage (TWS)

anomalies. This technique is primarily applied on the GRACE data for accuracy

improvement by reducing the signal noises of various types (Duan et al. 2009; Kusche

2007; Shum et al. 2011). The GRACE L2 data has some North-South stripes, which

could potentially introduce errors at lower latitudes (East-West direction) resulting low

resolution as compared to South-North direction with high resolution. According to

Shum et al. (2011), if a non-isotropic spatial smoothing filter is applied to the processed

data at longitudinal direction than in latitudinal, it could potentially produce better

results by keeping more signal information. As the SHCs are limited to degree and order

60 during the truncation process, it means that the spatial resolution of the field is 3° ×

3° or 330 km by 330 km. Therefore, a non-isotropic filter with radius 300 km is applied

to smoothen the data and leakage reduction (Duan et al. 2009; Guo et al. 2010). The

final output file is produced with name 2003_107_9.txt. After completing all above

mentioned ten step methodology, the output text (.txt) were then converted in to shape

files (.shp) for further data clipping to focus study region (UIP, Pakistan) using Arc GIS

10.3 software. By applying interpolation technique in Arc GIS software, the monthly

raster files were generated to study the changes in TWS over UIP from 2002-2014.

The de-correlation technique based on high pass filter is also applied to remove

correlated errors associated with higher order (Duan et al. 2009; Kusche 2007; Shum et

al. 2011).

3.3 Signal Restoration

The signal restoration is also one of the post processing technique to restore the

GRACE TWS signal, which was dumped during the process of data smoothing through

Gaussian filtering. The process used for signal restoration is termed as scaling factor.

As suggested by Long et al. (2014), the scaling factor for Indus Basin has been

estimated as 1.131 (Table 3). This scaling factor is derived based on the output of six

global hydrological models (GHM) models, which includes; Noah 2.7, VIC, Mosaic,

CLM 2.0, CLM 4.0, and WGHM2.2 (Long et al. 2014). After the calculation of scaling

37

factor, simple arithmetic operation has been applied to achieve the signal restoration by

multiplying the TWS with scaling factor (1.131). Basically, the scaling factor helps to

restore the amplitude of the GRACE signal, which was dumped at the stage of data

smoothing. The signal restoration is essentially required step to ensure the accuracy of

output signal.

3.4 Variable Infiltration Capacity Model (VIC)

The Variable Infiltration Capacity (VIC, version 4.0.6) model has been used to

simulate soil moisture and surface water fluxes to derive groundwater storage from the

GRACE-TWS. The VIC is a macroscale semi distributed hydrological model

developed by (Liang et al. 1994). In previous studies, the VIC has been extensively

used for hydrological forecasting, budgeting of water and energy and assessment of

climate change impacts in many regions of the world such as Ganges, Brahmaputra and

Meghna Basins (Siddique-E-Akbor et al. 2014), China (Wang et al. 2012; Zhang et al.

2014; Zhao et al. 2013) and Red River Basin (Xue et al. 2016). The VIC is sensitive

enough to incorporate sub-grid variability, control soil water storage as well as runoff

generation. As a basic feature of the VIC model, it models land surface as lumped by

considering uniform and flat cells of greater than 1 km (Siddique-E-Akbor et al. 2014).

The main model climatic data inputs include; topography, rainfall (daily to sub-daily),

snow, temperature and wind speed. As an output, the model simulates water balance at

daily to sub-daily time steps at each grid cell scale. Under a recent application of the

VIC model in Ganges–Brahmaputra–Meghna river basins, the study concluded that the

VIC has successfully captured daily runoff and stream flow dynamics (Siddique-E-

Akbor et al. 2014).

For this study, it is anticipated that there is no interconnection between grids

other than river routing and water can only enter in to a grid cell through atmosphere.

Also, the groundwater flow is considered to be relatively small as compared to surface

and near surface flows. There is no groundwater recharge from channel network.

It is important to mention that the above mentioned assumptions are quite

acceptable for hydrological modeling approaches (Siddique-E-Akbor et al. 2014).

Basically, the VIC is a large-scale (i.e, mesoscale; >>1 km; usually ≥10 km) hydrologic

model which allows to ignore the non-channel flow between the neighboring cells. It

means that, “The portions of surface and subsurface runoff that reach the local channel

network within a grid cell are assumed to be very large in comparison with the portions

38

that cross grid cell boundaries into neighboring cells”. Therefore, we can assume that;

there is no interconnection between grids other than river routing. Secondly, as there is

no inter-connection between the grids, and the streamflow routing is performed

separately using a separate routing model. Therefore, water in the land surface model

(i.e., VIC) can only enter a grid cell through the atmosphere. Thirdly, the assumption

that, “groundwater flow is relatively small compared to surface and near-surface flows”

also holds true during the monsoon period.

Fourthly, since the model grid cells are very large in size (i.e., ≥10 km).

Therefore, we can say that, “the portions of surface and subsurface runoff that reach the

local channel network within a grid cell are assumed to be >> the portions that return

into the cell as groundwater recharge. This assumption was only made for only the

simplification of the VIC model. Fifthly, the assumption regarding soil column depth

also holds valid as the hydrological models did not account for water storage variations

in deep unsaturated soil. However, sub-root-zone soil dries only by gravity drainage or

by diffusion to drier layers above. The lack of a drying trend in the root zone indicates

that deep soil-water storage was likewise stable (Rodell et al. 2009). The VIC setup was

calibrated with respect to the observed flow. As long as the soil moisture anomaly plot

is stable (i.e., no increasing or decreasing trend), the depth is acceptable for calculating

GW fluctuation. Sixthly, the two layers of soil were considered in the VIC model setup

with 30 cm and 70 cm depth reasoning that the model was calibrated against streamflow

and the deep soil-water storage was found stable. In other words, the impact of the depth

or number of layers does not become very much significant for the calculation of GW

storage anomalies.

3.4.1 VIC Model Simulation and Calibration

Under present study, the VIC model was setup specifically for Indus Basin at

0.1 degree resolution (10 km) for daily time steps considering total soil thickness of 1

m. Vertically, the soil profile was divided in to two layers where first layers cover depth

up to 0.3 m and second layer extends up to 0.7 m thickness. As pre-requisite input data

requirements of the VIC model, the topographic information was derived from Shuttle

Radar Topographic Mission (SRTM) of 90 meter resolution whereas, the global land

cover classification (GLCC) map was used to incorporate land cover information of

Indus Basin in the model. The spatial resolution of GLCC data (version-1) is

(https://Ita.cr.urgs.gov/GLCC) 400 m. The global soil data product called harmonized

https://ita.cr.urgs.gov/GLCC

39

world soil database (version 1.2) developed by World Food and Agriculture

Organization (FAO) was used for soil properties. The spatial resolution of this soil

product is approximately 1 km (http://www.fao.org/soils-portal/soil-survey/soilmaps-

and-databases/harmonized-world-soil-database-v12/en/). The climatological

information (precipitation and temperature, etc.) is also important input for the VIC

model simulation as forcing datasets. The satellite based rainfall product (32B4RT,

0.25° resolution) derived from Tropical Rainfall Measurement Mission (TRMM) was

used as precipitation data (http://pmm.nasa.gov/dataaccess/downloads/trmm). The

Global Surface Summary of the Day of National Climatic Data Center has been used

for daily temperature information. The World Meteorological Organization (WMO)

maintains this data through their global network of observatories.

After meeting all the input data requirements, the VIC was setup for daily

simulation of soil moisture and surface runoff over nine-year time span (2002-2010).

The first year i.e. 2002 was considered as model warm up period and model calibration

was performed from 2003-2010 against the observed “total annual flow” at different

reaches of Indus River. The simulation results are summarized in Appendix-B whereas

the annual observed river inflows are given in Appendix-C and the detail report is given

in Appendix-F. These calibration locations are basically called rim stations in Pakistan

river flows enter in Pakistan. The name of these calibration locations are; Kalabagh,

Nowshera, Tarbela, Mangla, Marala, Balloki, and Suleimanki as shown in Fig. 3.2.

For calibration purpose, the observed data of total annual flows was collected

from Pakistan Indus River System Authority (IRSA) for the period 2003-2010

pertaining to above mentioned calibration locations. IRSA is the organization who is

responsible for equitable distribution of surface water among provinces according to

their already decided share. Due to observed data limitations, the model calibration was

performed at annual frequency. The details of model calibration are summarized in

Table 3.1. The calibration results show that model performed well having a close

agreement with observed flows at all locations except Baloki and Suleimanki. The

Baloki lies on River Ravi whereas, Suleimanki is on River Sutlej. These two locations

are close to boarder with India and Indian authorities releases very controlled flow

through regulation structures. Therefore, the modeling of surface water at Baloki and

Suleimanki remained very challenging job.

http://www.fao.org/soils-portal/soil-survey/soilmaps-and-databases/harmonized-world-soil-database-v12/en/

http://www.fao.org/soils-portal/soil-survey/soilmaps-and-databases/harmonized-world-soil-database-v12/en/

http://pmm.nasa.gov/dataaccess/downloads/trmm

40

Figure 3.2: Calibration stations, the numbers are the normalized RMSE at each station. The Indus

River and its tributaries are shown with dark blue color where sub-basins of Indus are shown in

different colors

In Indus and its tributaries, the river flows consist of glacier and snowmelt water

along with rainfall contribution. These tributaries experience more flows during

summer period than winter. They run with full swing due to enough snow melt runoff

with monsoon rainwater contribution. Most of the time, flooding is very common from

Table 3.1: Performance of the VIC model over Indus basin

River Name Rim Station Normalized RMSE (%)

(2003-2010)

Nash Sutcliffe

Efficiency

Indus Kalabagh 12.66 0.86

Tarbela 24.32 0.74

Kabul Nowshera 51.74 0.97

Jhelum Mangla 17.4 0.98

Chenab Marala 25.98 0.82

Ravi Baloki 248.41 -4.57

Sutlej Suleimanki 2919.19 -1021.92

41

last few years and Pakistan is experiencing frequent flooding events of different

intensities and affecting different parts of the Country. It is also declared 7th most

vulnerable country with respect to climate change. The monsoon triggered flash

flooding not only result in massive economic loss but also impacts humans in terms of

large death toll. The flood of 2003 and 2007 affected Sindh and Khyber Pakhtunkhwa

(KPK) provinces where district of Thatta and some areas of KPK were affected the

most due to flash flooding. Pakistan has experienced a massive and devastating flooding

event in July, 2010 which has affected major part of the Country including KPK, Punjab

and Sindh provinces. From 2010-2014, frequent flooding is experienced more common

at almost every year but with varying intensity. The performance evaluation parameters

such as Nash–Sutcliffe efficiency and Root Mean Square Error (RMSE) were applied.

The Nash-Sutcliffe Efficiency (NSE) is calculated using the formula (Moriasi et al.

2007).

𝑁𝑆𝐸 = 1 − [∑ (𝑄𝑖

𝑜𝑏𝑠 − 𝑄𝑖𝑠𝑖𝑚)

2𝑛𝑖=1

∑ (𝑄𝑖𝑜𝑏𝑠 − �̅�)

2𝑛𝑖=1

]

where

𝑄𝑖𝑜𝑏𝑠

is the observed discharge value at i time step

𝑄𝑖𝑠𝑖𝑚

is the simulated discharge value at i time step

�̅� is the mean of the observed discharge values

The model resulted with Nash–Sutcliffe efficiency of more than 0.74 where

RMSE ranges from 12% to 50%. Keeping in view the fact that there is always room

available for the improvement of hydrological models in snow dominated regions

(Yang et al. 2013; Yu et al. 2013), the results simulated by the VIC model are

considered acceptable for further inclusion and derivation of groundwater storage

anomalies from the GRACE data. In comparison with the GLDAS (which is regional

model with more global focus), the performance of the VIC in simulating soil moisture

and surface dynamics is better because, it is specifically setup for Indus Basin with high

spatial sensitivity of 1˚ × 0.1˚ grid (under Chapter-4, more detailed discussion is

available referring to the comparison of the VIC with GLDAS derived groundwater

storage anomalies).

For analysis consistency with the GRACE, the monthly VIC simulated soil

moisture and surface runoff fluxes at the scale of 0.1 degree have been used for further

analysis and soil moisture and runoff anomalies were calculated as per already

explained methodology. The calculated soil moisture and runoff anomalies (SMR) were

42

further used for the extraction of GWS anomalies. Figure 3.3 is the example of resultant

SMR anomalies for the month of February, 2003 over Indus Basin at the scale of 0.1˚×

0.1˚ whereas Figure. 3.4 explains the variations in average SMR anomalies from 2003-

2010 at the scale of 1˚ × 1˚. The analysis reveals that maximum variations in average

SMR anomalies (2003-2010) is noticed in the Punjab Province (Upper Indus Plain) of

Pakistan and India, which is attributed to significant variations in precipitation,

topography and lithology. Figure 3.5 represents the average variations in SMR anomaly

more specifically over UIP (0.1˚ × 0.1˚ scale). It indicates that the soil moisture and

surface runoff (SMR anomalies) has changed more rapidly as compared to Bari doab

with least in Thal. In comparison with others two, Chaj and Rechna doabs are under

intensive agriculture and were also exposed to flooding during 2010 flood in Pakistan.

This area is also famous for rice crop, which is irrigated through traditional flood

irrigation method and standing water is maintained in the paddies for a couple of

months. In Thal doab, the reason for less change in SMR anomaly is due to Thal desert.

Most of the area under sand dunes is dependent on rain-fed agriculture. Due to low

rainfall and having rain-fed agriculture, there is less change in groundwater storage in

Thal doab. The topography of the UIP is flat with warm climate and the variations in

soil moisture is the dominant factor for variations in SMR anomaly.

43

Figure 3.3: Variations of SMR anomalies during February 2003 over Indus Basin (0.1˚ × 0.1˚)

Figure 3.4: Variations of average SMR anomalies (2003-2010) over Indus Basin (1˚ × 1˚).

44

Figure 3.5: Variations of average SMR anomalies (2003-2010) over UIP (0.1˚× 0.1˚)

45

CHAPTER 4

Estimation of GWS Variations Over Indus Basin

4.1 Total Water Storage Variations

After applying a number of data processing, filtering and signal restoration

techniques, the resultant equivalent water height (EWH), which is also called total

water storage (TWS) anomalies were mapped at 1˚ × 1˚ scale. The purpose of this

mapping was to analyze the time series spatial variations in TWS over whole Indus

Basin. The mean trend map (2003-2010) shows that anomaly varies from 13.8 to -34.0

mm over Indus Basin. For better understanding, TWS anomalies were classified in to

four classes (Fig. 4.1).

Figure 4.1: Mean trend map of TWS anomalies from 2003-2010 over Indus Basin. The red color

represents highest depletion in total water storage followed by yellowish, light green and cyan colors

The first-class ranges from -34.0 to -15.0 mm where second class varies from

-15.0 to -5.0 mm. The third and fourth classes ranges from -5.0 to + 5.0 mm and 5.0 to

13.8 mm respectively. The anomaly map shows that the variation in total water storage

has happened more rapidly over Punjab (Pakistan and India). The intensity of these

changes varies from -34.0 to -5.0 mm averagely over eight years (2003-2010). The UIP

46

and Indus Basin is covered by anomaly ranges from -34.0 to -5.0 mm and -34.0 to 13.8

mm respectively. Due to this change, it is estimated that the total average water storage

has decreased over UIP (19.5 mm per year) about two times more than whole Indus

Basin (10.1 mm per year) from 2003 to 2010. It indicates that UIP dominates the

hydrology of Indus Basin. This significant decrease in TWS is attributed to extensive

groundwater abstraction for intensive agriculture as these regions are very famous for

agriculture production on both sides of Pakistani border. Further investigation reveals

that the two Southern doabs (Bari and Rechna) are under more stress (-34.0 to -15.5

mm) of this decrease in total storage than Northern (Thal and Chaj).

4.2 Groundwater Storage Variations

The groundwater storage (GWS) anomalies have been extracted by subtracting

the VIC model simulated soil moisture and surface runoff fluxes from the GRACE-

TWS (Eq. 2.2). The time series analysis of GWS anomalies indicate that the

groundwater storage changes dominate the total water storage variations over UIP. Fig.

4.2 provides the comparison of variations in TWS, GWS and SMR (soil moisture and

runoff) from 2003-2010 in the study area (UIP). During the starting period, the soil

moisture is low, which is referred to the impact of severe drought conditions.

Figure 4.2: Comparison of TWS, GWS and SM from 2003-2010 over UIP

From 1998-2001, the major areas of Pakistan have experienced hydro-

meteorological drought, which was even extended further in few regions (Hanif et al.

2013). The significant decrease in groundwater storage from July, 2009 to July, 2010

is attributed to the extensive groundwater abstraction whereas, a flooding induced

-40

-30

-20

-10

0

10

20

30

40

-150

-100

-50

0

50

100

150

SM

R A

no

mal

y (

mm

)

TW

S a

nd

GW

S A

no

mal

y (

mm

) TWS GWS SMR Linear (GWS)

47

recharge impact is quite visible after July, 2010. By the end of July, 2010, the heavy

rain in Pakistan caused massive flooding. Generally, a decreasing trend in groundwater

storage has been analyzed from 2003-2010. However, the groundwater system of Indus

Basin is dominantly influenced by the monsoon rainfall and snowmelt in the upper

catchment of Indus River. The peaks of TWS and GWS cover the monsoon period

(July-September) along with contribution from snowmelt from 2003-2010. The peaks

of 2003, 2005 and 2007 are linked with the climatic variability when above normal

precipitation was received. However, the peak in the year 2010 represents massive

flooding event, which has helped in the replenishment of the underlying groundwater

aquifer in Punjab province.

For accuracy evaluation, the VIC model based monthly GWS anomalies derived

from the GRACE were compared with GLDAS-1 2003-2010 (Fig. 4.3). The

comparison shows a good agreement (Correlation = 0.71) and resulted statistically

indistinguishable difference in groundwater storage trend (Appendix-D). The Global

Land Data Assimilation (GLDAS-1) is the global data product generated by using a

number of hydrological models such as Noah, Mosaic, VIC, CLM, etc. The analysis

reveals that the impact of the selection of land surface model becomes insignificant

while comparing the trends of groundwater storage anomalies however, a modest

quantitative difference may appear in the results of two models. Actually, the GWS

anomaly is a relative term, which refers to changes in groundwater storage of one month

with the previous one. Therefore, the trend analysis also becomes important to get the

insight of the groundwater system in specified course of time, in addition to their

magnitudes of quantities. While comparing the results of two different models, the

quantitative difference in their magnitude depends upon the scale of simulation. In this

case, the VIC model is a regional scale model and its simulations (0.1˚ × 0.1˚ grid scale)

are Indus Basin specific however, GLDAS is a global model with more regional

outputs. Generally, the regional models are assumed to be more accurate than global as

they are simulated at more refined grid scales. Infect, the refinement of grid increases

the sensitivity of a model to accurately capture the important hydrological phenomenon

at specific basin, which otherwise, global models are incapable. Therefore, the results

of the VIC and GLDAS derived GWS anomaly show that both models are in good

agreement while comparing their trends over Indus Basin (Fig. 4.3).

48

Figure 4.3: Comparison of VIC based GRACE-GWS changes (blue) with GLDAS-1 based GRACE-

GWS changes (yellow)

4.3 GWS Calibration Analysis

The calibration is a very important step to ensure the credibility of the produced

results. The GRACE derived groundwater storage anomalies were compared with the

GWS estimated from piezometric DTW fluctuations. The calibration is performed at

seasonal scale due to the limitation of piezometric data, which is only available

biannually (pre-monsoon and post-monsoon) over UIP at 1˚ × 1˚ scale. The seasonal

plot of groundwater storage variations captured by the GRACE and piezometric

network provides an insight about the behavior of groundwater system (Fig. 4.4).

Figure 4.4: Comparison of GRACE-GWS (red color) anomalies with piezometric-GWS (blue color)

over UIP (2003-2010)

Both, the GRACE and piezometric GWS are in good agreement (correlation =

0.58, RMSE = 0.04 m). The results of GWS derived from the GRACE and piezometers

are summarized in Table 4.1. The GRACE has found successful in capturing the trend

and magnitude of groundwater fluctuations at seasonal scale. Fig. 4.4 indicate a

significant depletion in groundwater storage from September 2009 to July 2010, which

is associated with the excessive groundwater whereas, the rising trend (after July, 2010)

is the impacted by recharge through flooding event. Due to excessive rainfall, Pakistan

-90

-60

-30

0

30

60

90

GW

S A

no

mal

y (

mm

) Piezo GWS GRACE GWS

-150

-100

-50

0

50

100

150

GW

S (

mm

)

49

was hit substantial flooding in August 2010. The GRACE has reported seasonal

groundwater storage depletion of about 1.48 km3 per year in comparison with

piezometers (0.39 km3 per year) from 2003-2010 over UIP (Table 4.1). One of the

possible reasons for this difference between data collection mechanism. The GRACE

observation is large aerial whereas the piezometric data is locally influenced point

observation. The local phenomenon such are recharge and neighboring pumping have

a direct impact on piezometric measurements. The inherent limitation of the GRACE

coarse spatial resolution is another reason for this different in depletion rates reported

by the GRACE and piezometers. Infect, the remote sensing is an indirect measure of

spatially average information collection of hydrological variables such as groundwater

storage. The accuracy of the GRACE increases with area coverage and it is more

accurate over large basins with ~200,000 km2 (Swenson et al. 2006). It is very important

to develop clear understanding about the mechanism of the GRACE data collection and

data processing process for the derivation of groundwater storage information. The

GRACE mission orbits the earth at an altitude of 450 km from the surface of earth and

records the variation in gravity induced by changes in density on or over the earth

surface. Despite of the fact that the GRACE is highly sensitive to even small changes

in gravity however, there are a number of factors, which impact the gravity signal in

the atmosphere and cause noises. Although, various filtering and signal restoration

techniques are used to improve the quality of total water storage anomaly, the

challenges are still there for further improvements. The simulation errors introduced by

hydrological models during the derivation of GWS from the GRACE-TWS is factor,

which impact to reduce the GRACE-GWS. So, indirect measurement of GWS from

space and piezometric measurement of water levels on the surface of earth are two

different mechanisms of data collection. These are infect two different quantities, which

are difficult to compare. The comparison with the results of groundwater modeling is

another discussion. Their accuracy is impacted due to input data limitations,

complexities of the groundwater system itself and climatic implications.

It is estimated that UIP has lost a stock of about 11.84 km3 fresh groundwater

storage in just 8 years of time (2003-2010) through extensive groundwater abstraction

for anthropogenic activities (Table 4.2). Figure 4.5 shows the variations in groundwater

stock over UIP during the study period. The projected scenario (2011-2014) indicates

a decreasing trend in groundwater stock, which highlights further loss of fresh

groundwater storage. This projected scenario is developed based on the relation

50

between TWS and GWS anomalies from 2003-2010. This trend is just indication about

the expected changes in groundwater stock, which may vary based on the inclusion of

actual data pertaining to model simulations from 2011-2014. In comparison with other

regional studies especially conducted in India subcontinent (Bhanja et al. 2017), the

results of current study also in an agreement referring to an overall trend of groundwater

depletion. However, only trends may be compared but not the depletion rates as these

vary from one area to another depending upon the patterns of groundwater recharge and

abstraction.

51

Table 4.1: Calculation of groundwater storage variations over UIP

Time Period

GRACE

GWS

Seasonally

(mm)

GRACE

GWS

Seasonally

(m)

Thal

doab

Area

(km2)

GRACE

GWS

Seasonally

(km3)

Piezo DTW

Seasonally

(m)

Average

Depth to

Bedrock

(m)

Piezo

GLC

(m)

Average

Piezo

GLC

(m)

Piezo

GLA

(m)

Piezo

GSA

(m)

Thal Piezo

GWS

Seasonally

(km3)

Piezometric

GSA (mm)

August, 2003 84.45 0.08

109418.3

6

9.24 5.73

400.00

394.27

394.16

0.10 0.012 1.35 12.38

December,

2003

85.57 0.09 9.36 5.60 394.40 0.24 0.027 3.14 27.58

July, 2004 -3.43 0.00 -0.38 5.96 394.04 -0.11 -0.014 -1.49 -14.71

December,

2004

-24.11 -0.02 -2.64 5.96 394.04 -0.12 -0.015 -1.58 -15.55

May, 2006 -8.09 -0.01 -0.89 6.05 393.95 -0.21 -0.026 -2.77 -26.46

December,

2006

-12.42 -0.01 -1.36 5.48 394.52 0.36 0.041 4.71 41.89

June, 2007 26.03 0.03 2.85 5.63 394.37 0.21 0.024 2.79 24.33

December,

2007

-1.02 0.00 -0.11 5.43 394.57 0.41 0.048 5.37 47.98

June, 2008 -22.77 -0.02 -2.49 5.72 394.28 0.12 0.013 1.62 13.66

May, 2009 13.83 0.01 1.51 5.96 394.04 -0.12 -0.015 -1.52 -14.99

November,

2009

-52.97 -0.05 -5.80 5.91 394.09 -0.07 -0.009 -0.89 -9.27

May, 2010 -87.35 -0.09 -9.56 6.54 393.46 -0.70 -0.084 -9.16 -84.84

December,

2010

-22.71 -0.02 -2.48 5.85 394.15 -0.01 -0.002 -0.09 -2.00

RMSE between the GRACE-GWS and Piezo-GWS from 2003-2010 (mm) 0.04

Correlation (r2) between the GRACE-GWS and Piezo-GWS from 2003-2010 0.58

GRACE-GWS Based Depletion Rate from 2003-2010 (km3/year) 1.48

Piezo-GWS Based Depletion Rate from 2003-2010 (km3/year) 0.39

GLC = Groundwater Level Changes

GLA = Groundwater Level Anomaly

GSA = Groundwater Storage Anomaly

52

Figure 4.5 Groundwater stock variations over UIP from 2003-2014

4.4 Flooding Analysis

Despite of devastation impacts of flooding events, it also helps to replenish the

groundwater system especially in the alluvial aquifers. After 2010, Pakistan is

commonly experiencing the flooding events almost every year during summer

monsoon. These flooding events helps in the overhauling of groundwater system due

to exhaustive pumping of groundwater through over one million tube-wells (Bureau of

Statistics 2012). The flooding event of 2010 falling in the study domain is also studied

to analysis its impact on groundwater.

As discussed earlier, the heavy rainfall at the end of July, 2010 caused massive

flooding situation in Pakistan. By the end of August (September – December, 2010),

the analysis show that groundwater storage has been increased considerably. This rapid

increase in groundwater storage is due to the flooding induced recharge. Definitely, the

importance of seepage from irrigation system and return flow from agricultural fields

cannot be denied. But this unusual change indicates the happening of any unusual

phenomenon with lot of recharge, which is of course flood. Over the period 2011-2014,

the predictive scenario indicates decreasing trend with further groundwater depletion

of about 0.58 km3 per year, which means that the fresh groundwater stock of about 2.32

km3 is expected to be lost from 2011-2014 over UIP (Table 4.1). The role of second

major flooding event of 2014 is envisaged to be very important as it will help in

decreasing the depletion rate from 1.48 km3 to 0.58 km3 per year (2011-2014). Under

business as usual scenario (2003-2010), it is envisaged that the groundwater storage of

about 3.59 km3 has been added as recharge to the groundwater system. This is very

-16

-12

-8

-4

0

4

8

12

16

Jan

-03

Jun-0

3

No

v-0

3

Ap

r-04

Sep

-04

Feb

-05

Jul-

05

Dec

-05

May

-06

Oct

-06

Mar

-07

Au

g-0

7

Jan

-08

Jun-0

8

No

v-0

8

Ap

r-09

Sep

-09

Feb

-10

Jul-

10

Dec

-10

May

-11

Oct

-11

Mar

-12

Au

g-1

2

Jan

-13

Jun-1

3

No

v-1

3

Ap

r-14

Sep

-14

∆G

WS

(K

m³)

53

encouraging situation in the context of groundwater sustainability. The groundwater

depletion rates along with recharge calculations are summarized in Table 4.2.

Table 4.2: Summary of groundwater depletion and recharge calculations

Description Year

Mean

Depletion

Rate (mm/yr.)

UIP Area

(km2)

GWS

Depletion

Rate

(km3/yr)

Total Loss of

GWS (km3)

(Dep. Rate *

No of Years)

GRACE-GWS 2003-2010 13.50 109418.36 1.48 11.84

Piezo-GWS 2003-2010 3.60 109418.36 0.39 3.15

GRACE-GWS 2011-2014 5.30 109418.36 0.58 2.32

Net GWS

Change /

Recharge

Between 2010

and 2014 8.20 109418.36 0.90 3.59

54

CHAPTER 5

Integration of Satellite Gravimetry with Physical Modeling

Tools

5.1 Groundwater Monitoring through Ground Observational Network

Traditionally, groundwater is monitored through piezometric network covering

all the canal command areas in Punjab, which are maintained by SMO (WAPDA). As

discussed earlier, these piezometers are operated manually and data is collected bi-

annually. Figure 5.1 show the piezometric network of groundwater monitoring in UIP.

These are the selected locations from the mesh of monitoring sites, which are used

under this study for the calibration of the GRACE-GWS. Basically, those monitoring

sites are selected where long-term groundwater fluctuation data was available during

the study period from 2003-2010.

Figure 5.1: Piezometric network of water level monitoring in UIP. The reddish dots are the water table

measurement locations used for calibration purpose

Recently, efforts have been started to install automatic well loggers in the

critical areas where the groundwater is depleting at much higher rate (e.g. Bari doab,

Figs. 5.2 & 5.3). These efforts are still at the discussion and planning stage. The water

table fluctuations are the measure of seasonal changes in groundwater storage. The

monsoon system plays a dominant role in raising the water levels through groundwater

55

recharge. Generally, the major groundwater abstractions happens during summer

season when groundwater pumping increases to meet the irrigation requirement for rice

crop in UIP.

Fig. 5.2 shows the variations in DTW over UIP during the year 2010 is the

average of bi-annual or seasonal (pre and post monsoon) changes in groundwater

system. This map is developed to study the spatial changes in DTW and identification

of hotspots in the context of groundwater variability. It indicates that the area of lower

Bari doab is under severe depletion (>18 meter) of water table where the depth of water

table has reached to 22.8 meter. As a result of overexploitation, the groundwater aquifer

is under stress, which covers the districts of Multan, Khanewal and Lodhran. The

available piezometric data have been analyzed to study the seasonal variations

specifically in these three districts from 2005 to 2010. These seasonal variations have

been further analyzed to interpret the long-term annual trends in groundwater system.

Table 5.1 summarizes the piezometric analysis of groundwater depletion over Lower

Bari doab area where the intensity of groundwater depletion is high and the area is

likely to come under groundwater mining.

The depletion is calculated as 0.44 meter per year in Multan, Khanewal and

Lodhran (LMK) districts over the period from 2005-2010 (Fig. 5.3). It indicates that

the water table has been depleted about 2.6 meter overall in these three districts during

these six years. However, the magnitude of depletion varies from one district to another.

The maximum depletion rate of 0.74 meter per year has been estimated in Multan

followed by Lodhran (0.37) and Khanewal (0.20) respectively (Fig. 5.3). Due to

Table 5.1: Summary of piezometric analysis of groundwater depletion in Lower

Bari doab drea

Year Depth to water table Variations (m)

Multan Lodhran Khanewal LMK

2005 12.40 16.08 10.78 13.09

2006 12.49 16.49 11.13 13.37

2007 12.71 16.50 11.18 13.46

2008 14.96 17.08 11.45 14.50

2009 15.17 17.50 11.51 14.72

2010 15.52 17.97 11.88 15.12

Average Depletion (m/year) 0.74 0.37 0.20 0.44

Total Depletion from 2005-2010 (m) 2.62

56

comparatively low flows and induced recharge from Sutlej River, the area coverage

under severe groundwater depletion is more in Lodhran. As per river flows recorded by

Pakistan Indus River System Authority (IRSA), the average flow in Ravi (1.17 million

acre feet) is double than Sutlej (0.53 million acre feet) from 2001 to 2010. Being the

divisional capital and big city in Southern Punjab (Lower Bari doab), the rapid

expansion in urbanization is the major cause of high depletion in Multan district.

According to Punjab Development Statistics (2012), the population of Multan and

Lodhran in the year 2010 is reported as 3.96 million and 1.49 million respectively.

Figure 5.2: Variations in average depth to water table over UIP in 2010.

57

Figure 5.3: Average depth to water table variations in Lodhran, Multan and Khanewal from 2005-

2010. LMK (yellow bar) is annual average trend of groundwater depletion in three districts.

5.2 Groundwater Modeling

The groundwater modeling is a very effective tool and Visual ModFlow

(VMOD) is commonly applied model for groundwater modeling globally (Zhou and Li

2011). Recently, Khan et al. (2016a) developed a regional groundwater model to

simulate the groundwater flow patterns and estimate water balance in UIP. The VMOD

was applied at individual doabs with grid scale of 2.5 km x 2.5 km with vertical aquifer

division into three layers with varying depths. The first layer extends up to 50 meter

whereas the thickness second layers was fixed as 200 meter and the third layer covers

the remaining part of the aquifer by extending up to bedrock (400 m). The reason for

setting up the first two layers as 50 and 200 meters is the fact that almost all pumping

in UIP is either happening from these layers of aquifer or envisaged to meet future

requirements. As each doab is bounded by two rivers therefore, the rivers were assumed

as horizontal hydraulic boundaries. The cell inside the doab boundary were marked as

active where the remaining area was considered as no flow boundary. The aquifer

characteristics and parameters were derived from USGS of pumping test data (Bennett

et al. 1967).

0

2

4

6

8

10

12

14

16

18

20

2005 2006 2007 2008 2009 2010A

nnual

Aver

age

DT

W (

m)

Multan Lodhran Khanewal LMK Linear (LMK)

58

After inputting the other necessary datasets in the model such as, precipitation,

surface recharge, which is seepage from irrigation system, return flow from agricultural

fields, surface water discharges etc., the model was run for steady state calibration of

hydraulic heads by considering the year 1984. Then, the model was simulated for flow

dynamics under different stress periods such as 1991, 1996, 2004, and 2009 at each

doab. The calibration resulted with good agreement between model simulated and

measure hydraulic heads. This study concluded that the areas of Lower Bari (Multan,

Khanewal, and Lodhran) and Chaj (Sargodha) and few parts of Rechna doab (Narowal,

Sheikhupura, Toba Tek Singh and Jhang) are under groundwater mining conditions.

As the groundwater modeling is out of the scope of current study so, the output

of Khan et al. (2016a) has been used for further analysis, comparison with the GRACE

and studying the dynamics of UIP aquifer (Fig. 5.4).

Figure 5.4: Doab scale annual average variations in groundwater simulated with Visual ModFlow

over UIP from 2000-2010

For more detailed analysis of these changes, the simulations of Visual ModFlow

(VMOD) have been breakdown in to three individual graphs covering Bari and Rechna,

Chaj and Thal doabs. The purpose of these graphs is easy understanding of the temporal

changes being simulated by VMOD which are helpful to for comparison with GRACE

derived GWS. The comparison of Figs 5.5, 5.6 and 5.7 demonstrate that the major

144

148

152

156

160

164

168

172

176

180

184

188

192

2000 2003 2004 2005 2006 2007 2008 2009 2010

Sim

ula

ted

Hyd

rauli

c H

ead

(m

)

Thal Doab Bari Doab Chaj Doab Rechna Doab

59

groundwater storage changes in the form depletion have been more significant and

intensified (1.7 m) over Bari doab during 2000-2010.

Figure 5.5: ModFlow simulated annual average variations in groundwater over Bari and Rechna doabs

from 2000-2010

Figure 5.6: ModFlow simulated annual average variations in groundwater over Chaj doab from 2000-2010

Figure 5.7: ModFlow simulated annual average variations in groundwater over Thal doab from 2000-2010

172

173

174

175

176

177

178

2000 2003 2004 2005 2006 2007 2008 2009 2010

Sim

ula

ted

Hyd

rauli

c H

ead

(m

)

Bari Doab Rechna Doab

188.4

188.6

188.8

189.0

189.2

189.4

189.6

189.8

190.0

2000 2003 2004 2005 2006 2007 2008 2009 2010

Sim

ula

ted

Hyd

rauli

c H

ead

(m

)

148.0

148.4

148.8

149.2

149.6

150.0

2000 2003 2004 2005 2006 2007 2008 2009 2010

Sim

ula

ted

Hyd

rauli

c H

ead

(m

)

60

5.3 Satellite GWS Doab Scale Estimation

The time series analysis of changes in groundwater storage provides

understanding of long-term groundwater system behavior. The spatial variations in

groundwater storage helps to understand the seasonal to annual changes, assess the

spatial patterns of groundwater use, identify the critically under-stress areas due to over-

exploitation and quantify the groundwater recharge. Figures 5.8 to 5.15 highlights the

annual average changes in groundwater storage over UIP from 2003-2010 at 0.1˚ x 0.1˚

scale. It is analyzed that in some regions, the groundwater has decreased where the

other regions are found with increased storage. These changes in groundwater storage

are basically induced by the variations in recharge and abstraction rates. Overall, a

decreasing trend in groundwater storage has appeared, which is quite understandable in

relation with increased irrigation requirements. The spatial analysis of annual change

detection reveals that the groundwater storage has been changed more rapidly over Bari

and Rechna doabs in comparison with Chaj and Thal over the period 2003-2009 (Fig.

5.16). These changes have been further aggravated while analyzing change from 2003-

2010 (Fig. 5.17). The aquifer in Bari doab area is under severe stress whereas the

condition in Rechna doab is moderate. It is analyzed that the groundwater depletion is

taking place in the areas of Lower Bari doab (including Lahore), some parts of Rechna

(Toba Tek Singh and parts of the Jhang districts), Chaj (Sargodha district) and Thal

doabs are comparatively safer. In Lower Thal doab, the GRACE has reported

groundwater depletion, which is a bit contradictory with observation measurements.

The impact of flooding event is evident in Fig. 5.18, which shows that the groundwater

storage has been increased in the adjacent areas of River Jhelum and Chenab from July-

August, 2010. The identification of areas of flooding induced recharge is valuable

information for their protection from urbanization.

In comparison with piezometric data, the calibration efficiencies of GWS with

down scaling (0.1˚ × 0.1˚) and without down scaling (1˚ × 1˚) approaches have been

evaluated at each doab. The doab-wise results are summarized in Table 5.2 by inferring

the correlation of the GRACE-GWS with piezometric GWS against these two

approaches. These correlations have been estimated using model builder tool in GIS

software. The results suggest that numerical down scaling approach has performed

better, which is very effective for the GRACE based operational groundwater resource

management in Indus Basin.

61

Table 5.2: Comparison of numerical downscaling results at different grid scale

Grid Scale Year Correlation (r2) Results

Bari doab Rechna doab Chaj doab Thal doab

1˚ × 1˚ 2003-2010 0.92 0.56 0.09 -0.13

0.1˚ × 0.1˚ 2003-2010 0.93 0.65 0.15 -0.10

Figure 5.8: Annual average groundwater storage variations in 2003 over UIP. Dark red color shows

negative change representing depletion in groundwater storage

62

Figure 5.9: Annual average groundwater storage variations in 2004 over UIP


63



64



65


Figure 5.16: Annual average groundwater storage variations from 2003-2009 over UIP

66

Figure 5.17: Annual average groundwater storage variations from 2003-2010 over UIP

Figure 5.18: Change in groundwater storage from July-August, 2010 over UIP

67

5.4 Integrated Groundwater Management

For doab scale analysis of groundwater storage variations, the GRACE-GWS

anomalies have been extracted and analyzed individually at each doab and then

compared with piezometric estimation of groundwater storage changes. The

groundwater modeling results are used for the validation of both GARCE and

piezometric estimations of groundwater storage change. It is important to know that

VMOD simulations were performed at annual scale by selecting different stress periods

at a grid of 2.5 km × 2.5 km. Having the limitations of coarse spatial resolution of the

GRACE, the comparison of trends (increasing or decreasing) in groundwater storage is

more appropriate way for the validation of GWS changes inferred from these methods.

The detailed doab scale calculations of groundwater storage depletion along with

correlation and root mean square error (RMSE) are summarized in Tables 5.3 to 5.4

and 5.7 to 5.8.

In Bari and Rechna doabs, the GRACE has reported groundwater storage

depletion of about 0.38 and 0.21 km3 per year respectively (Tables 5.3 & 5.4). The

piezometric estimations are also found in close agreement (correlation = 0.93 for Bari

and 0.65 for Rechna) with the GRACE of about 0.54 km3 per year in Bari and 0.16 km3

per year in Rechna doabs from 2003-2010. Figures. 5.19 & 5.20 show the comparison

of groundwater storage trends inferred from the GRACE and piezometric data in Bari

and Rechna doabs. Both techniques indicate evidence of groundwater depletion, which

are also validated by VMOD output (Fig. 5.5). This depletion of groundwater storage

is due to the over-exploration of groundwater than recharge to meet the irrigational

requirements (Basharat and Tariq 2013). In this comparison, the data of 31 (Bari) and

56 (Rechna) piezometric locations have been used based on long-term availability

during the study period and good spatial coverage. It is mentioned here that the

Northwest Indian region close to Pakistani border is also under severe groundwater

depletion (Chen et al. 2004; Rodell et al. 2009; Tiwari et al. 2009) so, the point of

discussion here is that the GRACE signal may be contaminated over Bari doab. The

detailed investigations on this issue clarify that the strong correlation (0.93) of GRCE-

GWS with Piezo-GWS justify that the GRACE-GWS is reliable. This fact is also

reported and supported by Long et al. (2014) that Pakistani region (Bari doab) along

with Indian boarder are under severe groundwater depletion. The case of Chaj and Thal

doabs are found a bit different. As per GRACE-GWS, Chaj and Thal are also

68

experiencing average groundwater depletion at the rate of about 0.06 km3/year and 0.25

km3/year respectively (Tables 5.7 & 5.8). The same was validated by groundwater

modeling (VMOD) results by reporting an overall decreasing trend whereas, a

disagreement is observed with piezometric data (Table 5.7). The possible reason of this

disagreement is identified as insufficient piezometric data availability of only 35

locations with no proper spatial coverage of Upper Chaj (Gujrat and Mandi Bahauddin

districts) areas. The limitation was the sporadic nature of piezometric records with low

frequency during the study period 2003-2010). Mostly, the piezometers are either

choked or damaged due to which the historical data of only few piezometers is available

and used in this study. During the period from June-2007 to June-2009, this

disagreement was found maximum where piezometric data has shown a significant

increase contrary to the GRACE and VMOD (Fig. 5.23). In Lower Thal, reasons of

disagreement are the GRACE’s limitation of inherent coarse spatial resolution (1˚x 1˚)

and elongated shape of Thal doab forming a narrow strip. In this area, the GRACE

signal may be contaminated due to the impact of groundwater depletion in adjoining

area of Bari doab (Khanewal and Multan). Under such conditions, the GRACE is unable

to capture the appropriate trend with good accuracy. This doab scale analysis reveals

that the groundwater storage variations are more frequent over Bari and Rechna doabs

than others two. Over Bari and Rechna, persistent and significant groundwater

depletion is prevalent whereas, sub-doab scale intermixed trends of groundwater

depletion and recharge are more visible in Chaj and Thal doabs. Averagely, Chaj doab

is safer due to excessive recharge from rivers, irrigation system and rainfall, small area

and sandy strata (Bennett et al. 1967; Greenman et al. 1967).

The changes in groundwater stock provide the insight of groundwater system

response against its anthropogenic usage. These changes are also indirect measure of

aquifer resilience and its sustainability. The time series of groundwater stock changes

are presented in Figs. 5.21, 5.22, 5.25 & 5.26. However, the doab scale groundwater

stock changes are summarized in Tables 5.5 & 5.6 and 5.9 & 5.10. It is estimated that

that the aquifer underlying of Bari, Rechna, Chaj and Thal doab has lost groundwater

stock of about 3.06, 1.69, 0.51 and 1.96 km3 over a period of eight years (2003-2010)

respectively. This highlights that Bari doab aquifer is under the versge of fast depletion

among others. This situation indicates that immediate intervensions pertaining to water

69

conservation and aquifer recahrge are required to be adopted otherwise, severe

consequence may be faced in terms of groundwater mining and aquifer sustainability.

Figure 5.19: Comparison of the GRACE along with Piezometric derived variations in groundwater

storage over Bari doab from 2003-2010


storage over Rechna doab from 2003-2010

Figures 5.19 and 5.20 show that the GRACE has effectively captured the trends

of groundwater storage changes in two doabs (Bari and Rechna), which are in

agreement with piezometric measurements. This is very interesting to see that the

GRACE based dynamic numerical downscaling technique found suitable for the regular

monitoring of groundwater system behavior, which could aid in devising appropriate

management strategies required for sustainable resource management. Furthermore, the

trends of groundwater storage variations captured by GRACE are also comparable with

piezometric data despite of having different data collection mechanism. The frequent

changes in trends of piezometric plots are attributed with localized phenomenon of

groundwater recharge and pumping which are not prominent in GRACE data due to

having more regional picture where local phenomenon become more normalized.

-120

-80

-40

0

40

80

120G

WS

Ano

mal

y (

mm

)GRACE GWS Reg GRACE GWS Piezo GWS

-80

-40

0

40

80

GW

S A

no

mal

y (

mm

)

GRACE GWS Reg GRACE GWS Piezo GWS

70

Table 5.3: Calculation of groundwater storage variations over Bari doab

Time Period GRACE

GWS (mm)

GRACE

GWS

Regression

(mm)

GRACE

GWS

Seasonally

(m)

Bari

doab

Area

(km2)

GRACE

GWS

Seasonally

(km3)

Piezo

DTW

Seasonally

(m)

Average

Depth to

Bedrock

(m)

Piezo

GLC

(m)

Average

Piezo

GLC

(m)

Piezo

GLA

(m)

Piezo

GSA

(m)

Piezo

GWS

Seasonally

(km3)

Piezometric

GSA (mm)

Cal

ibra

tio

n

August, 2003 66.62 - 0.07

29585.07

1.97 8.40

400

391.60

390.83

0.77 0.09 2.74 92.45

December,

2003

72.25 -

0.07 2.14 8.41 391.59 0.75 0.09 2.67 90.30

July, 2004 7.58 - 0.01 0.22 8.94 391.06 0.23 0.03 0.82 27.70

December,

2004

12.31 -

0.01 0.36 9.13 390.87 0.03 0.00 0.12 4.12

May, 2006 -6.70 - -0.01 -0.20 9.34 390.66 -0.17 -0.02 -0.61 -20.78

December,

2006

1.51 -

0.00 0.04 8.75 391.25 0.41 0.05 1.46 49.34

June, 2007 1.34 - 0.00 0.04 9.19 390.81 -0.02 0.00 -0.08 -2.55

December,

2007

-0.90 -

0.00 -0.03 9.04 390.96 0.13 0.02 0.46 15.66

Val

idat

ion

June, 2008 -23.12 -9.25 -0.02 -0.68 9.56 390.44 -0.39 -0.05 -1.39 -46.91

May, 2009 -9.20 2.40 -0.01 -0.27 9.56 390.44 -0.40 -0.05 -1.41 -47.65

November,

2009

-44.18 -49.65 -0.04 -1.31 9.54 390.46 -0.37 -0.04 -1.32 -44.46

May, 2010 -65.22 -127.29 -0.07 -1.93 10.04 389.96 -0.88 -0.11 -3.12 -105.46

December,

2010

-19.92 -5.68 -0.02 -0.59 9.26 390.74 -0.10 -0.01 -0.35 -11.76



GRACE-GWS Based Depletion Rate from 2003-2010 (km3/year) -0.38

Piezo-GWS Based Depletion Rate from 2003-2010 (km3/year) -0.54




71

Table 5.4: Calculation of groundwater storage variations over Rechna doab

Time Period

GRACE

GWS

(mm)

GRACE

GWS

Regression

(mm)

GRACE

GWS

Seasonally

(m)

Rechna

doab

Area

(km2)

GRACE

GWS

Seasonally

(km3)

Piezo

DTW

Seasonally

(m)

Average

Depth to

Bedrock

(m)

Piezo

GLC

(m)

Averag

e Piezo

GLC

(m)

Piezo

GLA

(m)

Piezo

GSA

(m)

Piezo

GWS

Seasonally

(km3)

Piezometric

GSA (mm)

Cal

ibra

tio

n

August, 2003 55.06 - 0.06

31204.20

1.72 5.31

400

394.69

394.38

0.30 0.04 1.13 36.32

December,

2003

46.44 -

0.05 1.45 5.25 394.75 0.37 0.04 1.39 44.70

July, 2004 -8.99 - -0.01 -0.28 5.90 394.10 -0.29 -0.03 -1.08 -34.47

December,

2004

-27.36 -

-0.03 -0.85 5.68 394.32 -0.06 -0.01 -0.23 -7.41

May, 2006 -4.19 - 0.00 -0.13 5.82 394.18 -0.20 -0.02 -0.77 -24.56

December,

2006

-16.27 -

-0.02 -0.51 5.35 394.65 0.27 0.03 1.00 32.07

June, 2007 24.31 - 0.02 0.76 5.47 394.53 0.15 0.02 0.56 17.95

December,

2007

-1.50 -

0.00 -0.05 5.38 394.62 0.24 0.03 0.90 28.80

Val

idat

ion

June, 2008 -13.43 -20.98 -0.01 -0.42 5.66 394.34 -0.04 0.00 -0.14 -4.57

May, 2009 13.46 4.04 0.01 0.42 5.84 394.16 -0.23 -0.03 -0.84 -27.07

November,

2009

-33.64 4.12 -0.03 -1.05 5.69 394.31 -0.07 -0.01 -0.26 -8.49

May, 2010 -46.55 -74.26 -0.05 -1.45 6.18 393.82 -0.56 -0.07 -2.10 -67.44

December,

2010

-8.28 -22.19 -0.01 -0.26 5.50 394.50 0.12 0.01 0.44 14.17




Piezo-GWS Based Depletion Rate from 2003-2010 (km3/year) -0.16




72

Table 5.5: Estimation of groundwater stock changes over Bari doab from 2003-2010

Description

Bari doab Year

Mean

Depletion

Rate (mm/yr)

UIP Area

(km2)

GWS

Depletion

Rate

(km3/yr)

Total Loss of

GWS (km3)

(Depletion Rate

× No of Years)

GRACE-GWS 2003-2010 12.96 29,585.07 0.38 3.06

Figure 5.21: Seasonal changes in groundwater stock over Bari doab from 2003-2010

Table 5.6: Estimation of groundwater stock changes over Rechna doab from 2003-2010

Description

Rechna doab Year

Mean

Depletion

Rate (mm/yr)

UIP Area

(km2)

GWS

Depletion

Rate

(km3/yr.)

Total Loss of

GWS (km3)

(Depletion Rate

× No of Years)

GRACE-GWS 2003-2010 6.78 31,204.19 0.21 1.69

Figure 5.22: Seasonal changes in groundwater stock over Rechna doab from 2003-2010

-3

-1

1

3

GW

S C

han

ges

(km

3)

-4

-2

0

2

4

GW

S C

han

ges

(km

3)

73


storage over Chaj doab from 2003-2010


storage over Thal doab from 2003-2010

It is important to mention here that the variation in groundwater trends between

calibration and validation periods is envisaged mainly due to climatic variability and shape of

these two (Chaj and Thal) doabs. It is important to mention here that there was a severe drought

in Pakistan, which has extended from 1998-2001. Over this period, the study area has received

below normal rainfall, which has triggered the accelerated pumping of groundwater. On the

other hand, a massive flooding event has happened in 2010, which has facilitated the

replenishment of the groundwater system. These climatic variabilities have played a major role

in changing the groundwater regime between calibration and validation periods. Interestingly,

these events have affected Chaj and Thal at large being bounded by most flooded Rivers

(Jhelum and Chenab). The second important factor is their almost elongated shape forming a

narrow strip due to which groundwater depletion and recharging phenomenon quickly

influences the overall groundwater behavior. However, the hydrological conditions in the rest

of two doabs (Bari and Rechna) are quite different where the impact of climatic variability is

not much significant due to either less rainfall, reduced river flows that are being controlled by

India and their shape. The drought and flooding events has caused variations in trends during

calibration and validation periods otherwise, trends are of regular nature.

-100

-60

-20

20

60

100

GW

S A

no

mal

y (

mm

)

GRACE GWS Reg GRACE GWS Piezo GWS

-80

-60

-40

-20

0

20

40

60

80

GW

S A

no

mal

y (

mm

) GRACE GWS Reg GRACE GWS Piezo GWS

74

Table 5.7: Calculation of groundwater storage variations over Chaj doab

Time Period

GRACE

GWS

(mm)

GRACE

GWS

Regression

(mm)

GRACE

GWS

Seasonally

(m)

Thal

doab

Area

(km2)

GRACE

GWS

Seasonally

(km3)

Piezo DTW

Seasonally

(m)

Average

Depth to

Bedrock

(m)

Piezo

GLC

(m)

Average

Piezo

GLC

(m)

Piezo

GLA

(m)

Piezo

GSA

(m)

Thal Piezo

GWS

Seasonally

(km3)

Piezometric

GSA (mm)

Cal

ibra

tio

n

August,

2003

47.52 -

0.05

13,620.21

0.65 4.05

400

395.95

396.33

-0.38 -0.05 -0.62 -45.58

December,

2003

34.84 -

0.03 0.47 3.85 396.15 -0.17 -0.02 -0.28 -20.64

July, 2004 -16.05 - -0.02 -0.22 4.05 395.95 -0.38 -0.05 -0.62 -45.33

December,

2004

-38.63 -

-0.04 -0.53 4.22 395.78 -0.54 -0.07 -0.89 -65.35

May, 2006 1.00 - 0.00 0.01 4.06 395.94 -0.39 -0.05 -0.63 -46.59

December,

2006

-16.95 -

-0.02 -0.23 3.34 396.66 0.34 0.04 0.55 40.22

June, 2007 33.56 - 0.03 0.46 3.41 396.59 0.27 0.03 0.44 32.11

December,

2007

-8.46 -

-0.01 -0.12 2.95 397.05 0.73 0.09 1.19 87.41

Val

idat

ion

June, 2008 -10.12 57.11 -0.01 -0.14 3.18 396.82 0.49 0.06 0.80 58.88

May, 2009 23.19 70.68 0.02 0.32 3.56 396.44 0.11 0.01 0.18 13.39

November,

2009

-28.51 22.68 -0.03 -0.39 3.67 396.33 0.00 0.00 0.01 0.56

May, 2010 -44.57 15.00 -0.04 -0.61 4.38 395.62 -0.71 -0.09 -1.16 -85.22

December,

2010

-6.73 64.56 -0.01 -0.09 3.04 396.96 0.63 0.08 1.04 76.15








75

Table 5.8: Calculation of groundwater storage variations over Thal doab

Time Period

GRACE

GWS

(mm)

GRACE

GWS

Regression

(mm)

GRACE

GWS

Seasonally

(m)

Thal

doab

Area

(km2)

GRACE

GWS

Seasonally

(km3)

Piezo DTW

Seasonally

(m)

Average

Depth to

Bedrock

(m)

Piezo

GLC

(m)

Averag

e Piezo

GLC

(m)

Piezo

GLA

(m)

Piezo

GSA

(m)

Thal Piezo

GWS

Seasonally

(km3)

Piezometric

GSA (mm)

Cal

ibra

tio

n

August, 2003 48.64 - 0.05

33,488.8

6

1.63 3.84

400

396.16

396.40

-0.24 -0.03 -0.96 -28.55

December,

2003 50.91 - 0.05 1.70 3.73 396.27 -0.13 -0.02 -0.53 -15.79

July, 2004 -5.74 - -0.01 -0.19 3.88 396.12 -0.28 -0.03 -1.11 -33.16

December,

2004 -25.93 - -0.03 -0.87 3.66 396.34 -0.05 -0.01 -0.22 -6.44

May, 2006 0.54 - 0.00 0.02 3.79 396.21 -0.19 -0.02 -0.77 -22.86

December,

2006 -10.32 - -0.01 -0.35 3.40 396.60 0.20 0.02 0.80 23.96

June, 2007 20.87 - 0.02 0.70 3.38 396.62 0.22 0.03 0.89 26.70

December,

2007 -1.82 - 0.00 -0.06 3.24 396.76 0.36 0.04 1.45 43.37

Val

idat

ion

June, 2008 -7.05 -16.89 -0.01 -0.24 3.54 396.46 0.06 0.01 0.24 7.15

May, 2009 18.34 -61.44 0.02 0.61 3.66 396.34 -0.06 -0.01 -0.24 -7.15

November,

2009 -32.74 -10.20 -0.03 -1.10 3.51 396.49 0.10 0.01 0.38 11.46

May, 2010 -48.62 25.81 -0.05 -1.63 3.95 396.05 -0.35 -0.04 -1.40 -41.95

December,

2010 -20.37 36.61 -0.02 -0.68 3.24 396.76 0.36 0.04 1.45 43.27


Correlation (r2) between the GRACE-GWS and Piezo-GWS from 2003-2010 -0.10






76

Table 5.9: Estimation of groundwater stock changes over Chaj doab from 2003-2010

Description

Chaj doab Year

Mean

Depletion

Rate

(mm/yr)

UIP Area

(km2)

GWS

Depletion

Rate

(km3/yr.)

Total Loss of

GWS (km3)

(Depletion

Rate × No of

Years)

GRACE-GWS 2003-2010 4.71 13,620.21 0.06 0.51

Figure 5.25: Seasonal changes in groundwater stock over Chaj doab from 2003-2010

Table 5.10: Estimation of groundwater stock changes over Thal doab from 2003-2010

Description

Thal doab Year

Mean

Depletion

Rate

(mm/yr)

UIP Area

(km2)

GWS

Depletion

Rate (km3/yr.)

Total Loss of

GWS (km3)

(Depletion Rate

× No of Years)

GRACE-GWS 2003-2010 7.34 33,488.86 0.24 1.96

Figure 5.26: Seasonal changes in groundwater stock over Thal doab from 2003-2010

For the purpose of groundwater storage predictions, the doab scale analysis has

been divided in to two parts; calibration (2003-2007) and validation periods (2008-

2010). Based on the regression approach, the relationship between GRCAE-GWS with

-3

-2

-1

0

1

2

3

GW

S C

han

ges

(km

3)

-2

-1

1

2

GW

S C

han

ges

(km

3)

77

piezometric-GWS during calibration period has been developed for each doab (Figs.

5.27, 5.29, 5.31 & 5.33). By using these regression equations, the projected GWS has

been estimated from 2008-2010 and then validated with piezometric data (Tables 5.11

to 5.14). For the period 2008-2010, the validation analysis show that projected GWS

have favorable agreement between projected the GRACE-GWS and Piezo-GWS over

Bari and Rechna doabs (Figs. 5.19 & 5.20) whereas; the validation results are not

encouraging over the rest of doabs (Figs. 5.23 & 5.24). For effective management, the

accuracy of projected scenarios is very critical. For accuracy evaluation, standard errors

(SE) method has been used and SE have been estimated at each doab. The standard

errors have been calculated using the following equation;

𝑆𝐸 = 𝑆𝐷 √𝑁⁄

where;

SE = Standard Error

SD = Standard Deviation (between regression based GWS during validation

period with piezometric data)

N = No. of Data Readings

Basically, the standard error is the measure of uncertainty and is dependent on

two parameters; standard deviation and number of data readings. The variations in SE

are shown Figs. 5.28, 5.30, 5.32 and 5.34. The maximum magnitude of SE is reported

in Chaj (32 mm) followed by Thal (21 mm), Bari (16 mm) and Rechna (11 mm) doabs.

The high magnitude of SE and poor correlation indicates the unsuitability of GWS

projections for Chaj and Thal doabs (Figs. 5.32 & 5.34). However, the average SE for

Bari and Rechna doabs are calculated as ±8 mm and ±7 mm respectively with favorable

correlation, which found suitably appropriate for 3-6 monthly future projection. The

results of predicted scenarios reveal a decreasing trend, which is expected during next

six months (January-June, 2011) over Bari and Rechna doabs. Such type of projections

indicating the behavior of groundwater system with 6 months (180 days) ahead, and are

useful to help the groundwater managers in the perspective of sustainable groundwater

management. The same statistical approach has been used for Chaj and Thal doabs but,

results are not found satisfactory due to disagreement of Piezo-GWS with the GRACE-

GWS for the period 2003-2007.

78

Table 5.11: Calculation of standard error during validation period over Bari doab

Period

GRACE

GWS

(mm)

Piezo

GWS

(mm)

Standard

Deviation

S.

Error

Regression

Based GWS

(mm)

Piezo

GWS

(mm)

Standard

Deviation

S.

Error

Jun-08 -23.12 -46.91 16.82 8 -9.25 -46.91 26.62 12

May-09 -9.19 -47.64 27.18 12 2.40 -47.64 35.39 16

Nov-09 -44.17 -44.45 0.19 0 -49.64 -44.45 3.66 2

May-10 -65.21 -105.45 28.45 13 -127.29 -105.45 15.43 7

Dec-10 -19.91 -11.76 5.76 3 -5.67 -11.76 4.30 2

Figure 5.28: Variations in standard error over Bari doab during projected period (January-June, 2011)

12

16

2

7

2

-150

-125

-100

-75

-50

-25

0

25

50

Jun-08 Sep-08 Dec-08 Mar-09 Jun-09 Sep-09 Dec-09 Mar-10 Jun-10 Sep-10 Dec-10

Reg

ress

ion B

ased

GW

S D

uri

ng

Val

idat

ion P

erio

d (

mm

)

y = 0.0002x3 - 0.0156x2 + 0.1669x + 5.4147

R² = 0.9721

-20

-10

0

10

20

30

40

50

60

70

80

-40 -20 0 20 40 60 80 100

GR

AC

E A

no

mal

y (

mm

)

Piezometric Anomaly (mm)

Figure 5.27: Correlation between the GRACE and piezometric groundwater storage variations over

Bari doab during calibration period (2003-2007)

79


Rechna doab during calibration period (2003-2007)

Table 5.12: Calculation of standard error during validation period over Rechna doab

Period

GRACE

GWS

(mm)

Piezo

GWS

(mm)

Standard

Deviation

Standard

Error

Regression

Based

GWS

(mm)

Piezo

GWS

(mm)

Standard

Deviation

S.

Error

Jun-08 -13.42 -4.56 6.26 3 -20.97 -4.56 11.60 5

May-09 13.46 -27.07 28.66 13 4.04 -27.07 21.99 4

Nov-09 -33.63 -8.48 17.78 8 4.12 -8.48 8.91 2

May-10 -46.55 -67.44 14.77 7 -74.26 -67.44 4.82 11

Dec-10 -8.28 14.16 15.87 7 -22.18 14.16 25.70 5

Figures 5.28 and 5.30 demonstrate the variations in stand errors over Bari and

Rechna doabs. These graphs reveal that GRACE derived GWS are more sensitive to

capture change in groundwater system during validation period over a region when the

phenomenon of either groundwater recharge or depletion are significant enough.

Resultantly, the magnitude of the standard error is small.

y = 1E-06x5 - 2E-05x4 - 0.0022x3 + 0.0425x2 + 1.2941x - 15.502

R² = 0.5589

-40

-30

-20

-10

0

10

20

30

40

50

60

-40 -30 -20 -10 0 10 20 30 40 50

GR

AC

E A

no

mal

y (

mm

)

Piezometric Anomaly (mm)

5

104

2

11

-100

-80

-60

-40

-20

0

20

40


Reg

ress

ion

Bas

ed G

WS

Du

rin

g

Val

idat

ion

Per

iod

(m

m)

Figure 5.30: Variations in standard error over Rechna doab during projected period (January-June, 2011)

80

Table 5.13: Calculation of standard error during validation period over Chaj doab

Period

GRACE

GWS

(mm)

Piezo

GWS

(mm)

Standard

Deviation S. Error

Regression

Based GWS

(mm)

Piezo

GWS

(mm)

Standard

Deviation

S.

Error

Jun-08 -10.12 58.87 48.79 22 57.11 58.87 1.24 1

May-09 23.19 13.39 6.92 3 70.67 13.39 40.50 18

Nov-09 -28.50 0.55 20.55 9 22.68 0.55 15.64 7

May-10 -44.56 -85.22 28.74 13 15.00 -85.22 70.86 32

Dec-10 -6.73 76.14 58.60 26 64.56 76.14 8.19 4

y = 2E-07x5 + 2E-06x4 - 0.0016x3 - 0.0516x2 + 1.6726x + 77.674

R² = 0.6694

-250

-200

-150

-100

-50

0

50

100

150

-80 -60 -40 -20 0 20 40 60 80 100

GR

AC

E G

WS

(m

m)

Piezometric GWS (mm)

1

18

732

4

-20

0

20

40

60

80

100

Reg

ress

ion B

ased

GW

S

Duri

ng V

alid

atio

n P

erio

d

(mm

)

Figure 5.32: Variations in standard error over Chaj doab during projected period (January-June, 2011)


Chaj doab during calibration period (2003-2007)

81


Thal doab during calibration period (2003-2007)

Table 5.14: Calculation of standard error during validation period over Thal doab

Time Piezometric

GWS (mm)

GRACE

GWS

(mm)

Standard

Deviation

Standard

Error

Regression

Based

GWS

(mm)

Observed

GWS

(mm)

Standard

Deviation

S.

Error

Jun-08 -7.04 7.15 10.03 4 -16.89 7.15 17.00 8

May-09 18.34 -7.15 18.02 8 -61.44 -7.15 38.38 17

Nov-09 -32.74 11.46 31.25 14 -10.20 11.46 15.32 7

May-10 -48.62 -41.94 4.71 2 25.81 -41.94 47.91 21

Dec-10 -20.37 43.26 44.99 20 36.60 43.26 4.70 2

Figure 5.34: Variations in standard error over Thal doab during projected period (January-June, 2011)

The concept of integrated groundwater management is to apply more than one

technique or tools together to study the different dimensions of groundwater resource.

y = -4E-06x5 - 7E-05x4 + 0.0114x3 + 0.1742x2 - 6.0155x - 63.831

R² = 0.5225

-150

-100

-50

0

50

100

-40 -30 -20 -10 0 10 20 30 40 50

GR

AC

E G

WS

(m

m)

Piezometric GWS (mm)

8

17

7

212

-100

-80

-60

-40

-20

0

20

40

60


Reg

ress

ion

Bas

ed G

WS

Du

rin

g

Val

idat

ion

Per

iod

(m

m)

82

The spatio-temporal analysis of groundwater system behavior has necessitated applying

an integrated approach for both groundwater monitoring and management perspective.

The earlier discussion has elaborated in detail about the opportunities and challenges

associated with different monitoring tools (modeling, in-situ measurements and remote

sensing) in practice for groundwater management. The analysis of the GRACE

groundwater storage variations, piezometric water table fluctuations and VMOD

groundwater system behavior, indicate that Bari doab and parts of Rechna, Chaj and

Thal doabs are experiencing groundwater depletion. More specifically, the groundwater

sustainability is at risk in Bari doab due to unbalance between recharge and abstraction.

Due to low rainfall and low flows in eastern Rivers, the over abstraction disturbances

the groundwater balance. The Bari doab (Fig. 5.14) in general and especially, the areas

of Khanewal, Lodhran and Multan (Fig. 5.3) need immediate attention with appropriate

mitigation strategies where foremost required suggested measure is controlled

abstraction with continuous monitoring.

In this situation, GARCE is found useful for continuous monthly tracking of

groundwater storage whereas, the piezometers network may be helpful at seasonal to

annual scale. There is also need for detailed modeling to devise region specific and

appropriate management strategies. The modeling approach is essentially required at

annual scale for better understanding of surface water and groundwater interactions by

incorporating the climatic implications. Secondly, it is also important to adopt water

conservation approaches and especially, education of farming community could play a

major role by changing their minds to conserve water. Currently, the most of useable

water is wasted at farm scale due to traditional flood irrigation practice through which

unnecessarily farmers are irrigation many times than the actual requirements of those

crops. The rainwater harvesting is another area, which needs to be harvested for

recharge enhancement. In Rechna doab, the situation is relatively better than Bari doab

where enough recharge is available from multiple sources (Rivers, irrigation system,

rainfall). The analysis show that the floods also play their role in recharging the Rechna

doab. However, the tube well density in Rechna doab is also higher (0.104 tube wells

per hectare) where huge groundwater abstraction is happing through 0.33 million tube

wells (Bureau of Statistics 2012). Keeping in view this over-exploitation, the

groundwater depletion is expected to become more critical in the lower Rechna doab in

future (Khan et al. 2008). The controlled abstraction is suggested with the adoption of

83

water conservation techniques for sustainable groundwater management. The efforts

are also required to increase the groundwater recharge by artificial techniques.

There is continuous need of groundwater monitoring and the GRACE is

recommended for monthly monitoring in an integrated way along with VMOD and

piezometric datasets. Actually, all these three tools will complement each other for

accuracy improvement. Having small area and sufficient recharge from adjacent rivers,

Chaj is comparatively safer except the lower parts where considerable depletion is

observed. Similarly, Thal doab is also safe except in few areas of upper Thal where the

water table depletion has been observed. Therefore, a careful groundwater monitoring

is needed along with water conservation practice. The urbanization challenge may

impact the recharge so, the protection of recharge areas may be considered. Due to a

number of challenges (small area, coarse resolution, sporadic piezometric datasets,

intermixed phenomenon and low accuracy), the GRACE is not suitable to apply for

monitoring purposes in Chaj and Thal doabs. The term intermixed phenomenon is

referred to a place where both groundwater recharge and depletion happen

simultaneously.

5.5 GRACE – A Spatial Decision Support Tool

To devise appropriate management strategies, the role of decision support

system is critically important to facilitate the managers and policy makers in

understanding or assessing the severity of the complex issues and assist them in

developing the appropriate mitigation strategies. Being hidden resource, the spatial

variability in groundwater dynamics is more complex and needs integration of various

techniques and tools. In this perspective, the GRACE has proven its practical ability

over Bari and Rechna doabs for adoption as decision support tool. The demonstration

of the GRACE for 3-6 monthly projections (Fig. 5.20 & 5.21) of groundwater storage

anomalies is a good opportunity for its practical adoption and further development as

decision support system. Therefore, its integration with GIS is a good example for

further development of a decision support system. Such application is envisioned, not

only to serve as information management system but, maximizing its ability to perform

various operations such as query, calculation, scenario building, etc. The coupling of

supplementary information from other remote sensing sensors/products and ground

measurements potentially help improve the system efficiency through overlaying

capabilities.

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5.6 Tracking Groundwater from Space

5.6.1 Opportunities

The global coverage with free and frequent data availability are the key feature

of remote sensing technology such as the GRACE. The GRACE has enabled basin-

wide hydrological studies and as well as evaluation of global aquifers. In past, it was a

big challenge due to the scarcity of observed or measured data. The second major

feature is its ability to measure the complete water cycle, which otherwise was very

difficult. The rest of the satellite sensors are parameter specific missions like, GPM

(Global Precipitation Measurement Mission), SMAP (Soil Moisture Active Passive,

TRMM (Tropical Rainfall Measurement Mission), altimetry (water levels

measurement), etc. The GRACE has empowered the scientific community with analysis

liberty. The hydrologist have now more flexibility and liberty either they want to work

on any specific component/parameter of water cycle or basin scale budgeting by

analyzing the water cycle as a whole. Thirdly, the trans-boundary applications is

another added advantage. The application of the GRACE in combination with satellite

altimetry and precipitation products, potentially a good opportunity for hydrologist to

study trans-boundary issues especially in developing countries. The data sharing is a

big issue in developing countries. The upstream countries do not share accurate and

timely information with countries at downstream who are at the verge of vulnerability

to extreme and devastating climatic implications. The fourth major advantage is the

integration of satellite data with hydrological modeling. Both the satellite and

hydrological models are complimented from each other. The satellite data is used as

input for hydrological simulations as well as calibration of modeling outputs in data

deficit regions or ungauged basins. Whereas, the calibrated modeling simulations are

used for the calibration of satellite driven applications.

5.6.2 Challenges

Besides opportunities, the challenges are also there, which limits the societal

benefits of the GRACE as an effective tool for groundwater resource management in

developing countries in general and Indus Basin specifically. The foremost of them is

its inherent limitation of coarse spatial resolution, which not only effects the accuracy

of results but also its applicability at small spatial scales (Rodell et al. 2009). There is

always a tradeoff between accuracy and spatial coverage. The GRACE data latency is

another challenge, which hampers its real time applicability. The launching of the

85

GRACE-FO mission in May, 2018 is anticipated to manage this issue having higher

spatial resolution. The GRACE data becomes publicly available after 1-2 months from

the time of its data collection. The data processing takes time due to system

complexities through which, it collects the gravity anomalies. The data processing

centers (CSR, GFZ, and JPL) release the GRACE data in the form of spherical harmonic

coefficients after initial processing of gravity anomalies. It is a critical concern from

the user end (groundwater managers, policy makers) because; the instant information

is required for timely implementation. The GRACE mission team has to overcome this

issue for its wide applicability. However, the GRACE driven time series information is

good to get the historical picture of groundwater system.

The second major concern is the non-availability of the GRACE data processing

tools in public domain and involve technical complexities. As the gravimetry is a

complex science, the derivation of total water storage information from gravity field

involves a lot of processing. For this purpose, relevant professionals and expertise are

required to be developed for organizational scale adoptability. The role of NASA

applied science team is important for the promotion of the GRACE technology through

upscaling of capacity building programs. The facilitation in terms of capacity building

efforts and provision of relevant tools in public domain potentially help to accelerate

the GRACE integrated applications for societal benefits.

86

CHAPTER 6

Conclusion and Recommendations

6.1 Conclusions

Generally, the GRACE derived spatial variations in groundwater storage helps

to understand the seasonal to annual changes, assess the spatial patterns of groundwater

use, identify the critically under-stress areas with over-exploitation areas and quantify

the groundwater recharge from various sources along with supplementary information.

More specifically, the analysis and detailed deliberations derive the following

conclusions;

1. The analysis of average trend of total water storage and subsequent changes in

groundwater storage anomalies suggest that the variations over UIP are more

rapid as compared to other parts of Indus Basin in Pakistan. The TWS has

decreased about two times more over UIP (19.5 mm per year) than the whole

Indus Basin (10.1 mm per year) from 2003 to 2010. It reveals that UIP dominates

the hydrology of Indus Basin due to extensive groundwater abstraction for

anthropogenic purposes.

2. The comparison of the VIC with GLDAS-1 reveals that the impact of the selection

of land surface model becomes insignificant while comparing the trends of

groundwater storage anomalies however, a modest quantitative difference has

appear in the results of two models. The results of the VIC simulations indicated

that the selection of appropriate model and basin specific modeling is much useful

rather than using global modeling outputs.

3. The GRACE has found successful (correlation with observed data = 0.58) in

capturing both, the trend and magnitude of groundwater fluctuations averagely at

seasonal to annual scale over UIP. The spatial analysis of annual change detection

has revealed that the groundwater storage has been changed more rapidly over

Bari and Rechna doabs in comparison with Chaj and Thal over the period 2003-

2010.

4. While analysis the local variations at individual doabs, the results suggest that

numerical down scaling (at 0.1˚ × 0.1˚) approach has performed better and

effective as compared to actual GWS at 1˚ × 1˚ grid scaling. Out of four doabs,

the GRACE found more capable in Bari and Rechna doabs and has successfully

87

captured seasonal groundwater storage trends in good agreement (correlation =

0.93 for Bari and 0.65 for Rechna) with piezometric data. The GRACE has

reported groundwater depletion of about 0.38 and 0.21 km3 per year in Bari and

Rechna doabs respectively. Whereas, a disagreement between the GRACE and

piezometric data is reported in Chaj and Thal doabs may be due to sporadic nature

of piezometric records with low frequency during the study period. However, the

GRACE based depletion trends are quite verified by VMOD for both Chaj and

Thal doabs.

5. The groundwater aquifer in Bari doab area is under severe stress whereas the

condition in Rechna doab is moderate whereas, Chaj and Thal doabs are

comparatively safer. The persistent and significant groundwater depletion is

prevalent in Bari and Rechna doabs whereas, sub-doab scale intermixed trends of

groundwater depletion and recharge are more visible in Chaj and Thal doabs.

6. Furthermore, the groundwater depletion is taking place in the areas of Lower Bari

doab (Multan, Lodhran, Khanewal including Lahore), some parts of Rechna

(Toba Tek Singh and parts of the Jhang districts) and Chaj (Sargodha district).

The Chaj and Thal doabs are comparatively safer. In Lower Thal doab, the

GRACE has measured groundwater depletion, which is contradictory with

observational measurements and ground conditions (small strip with enough

recharge from bounding rivers, which are at a short distance). It is due to the

limitations of the GRACE in the form of its legacy of being coarse spatial

resolution satellite.

7. The integrated approach comprising of satellite, groundwater modeling and

groundwater measurement network is found effective at doab to Indus Basin

scales, for appropriate monitoring and management of groundwater resource.

8. While investigation, it is estimated that UIP has lost a stock of about 11.84 km3

fresh groundwater storage in just 8 years of time (2003-2010) through extensive

groundwater abstraction. The projected scenario (2011-2014) indicated further

loss of fresh groundwater storage due to increasing dependence on groundwater.

It is found that the role of flooding events in the replenishment of groundwater is

very observant in UIP due to its favorable lithology. In Pakistan, the flooding

events of 2010 and 2014 has facilitated in reducing average depletion rates from

88

13.5 (2003-2010) to 5.3 mm (2011-2014) per year over UIP. Thereof, it is

expected that the groundwater storage of about 3.59 km3 will be added as recharge

to the groundwater system during the period 2011-2014.

9. The evaluation of statistical approach for future projection resulted an average

standard error (SE) of 8 mm and 7 mm in Bari and Rechna doabs respectively

with favorable correlation and found suitably appropriate for 3-6 monthly future

projection. However, this technique is not found appropriate for Chaj and Bari

doabs due to disagreement with Piezo-GWS over the calibration period.

10. As a decision support tool, the GRACE has demonstrated well for 3-6 monthly

projections, which would hold true for 2-3 months over UIP. It is revealed that

the applicability of decision support tool is more valid over Bari and Rechna

doabs.

6.2 Recommendations

Based on the results and discussion, the summary of the recommendation and

future directions are listed as below;

1. Keeping in view the inherent coarse resolution, the accuracy of the GRACE

derived GWS is very much depended on the accurate simulations of soil moisture

and surface water fluxes through hydrological modeling. To bridge the in-situ data

paucity, the evaluation of the potential of satellite-based soil moisture products

such as SMAP is considered important for further utilization in the derivation of

groundwater storage anomalies.

2. The synthesis of results suggests that the identified critical areas of Lower Bari

(Multan, Khanewal, Lodhran including Lahore) and few regions of Rechna (Toba

Tek Singh, Chiniot, Jhang, Narowal) need immediate attention. The controlled

abstraction, continuous monitoring of groundwater levels, water conservation and

artificial groundwater recharge (both surface and rainwater) practices are

recommended from implementation in these areas. In these areas, the detailed

(local scale) groundwater modeling is importantly required and would be helpful

for devising more appropriate groundwater management strategies.

3. For better calibration of satellite remote sensing (GRACE) and hydrological

models, the ground observation network (piezometers) is required to be further

89

strengthen through the installation of automatic groundwater loggers. It would

help to automatically record water level datasets with high temporal frequency

(10 daily or monthly), which would be useful for accurate calibration of satellite

products and modeling simulation.

4. The projection of groundwater storage could be improved during the regime

transition phase from non-Monsoon to Monsoon seasons by supplementing the

information of groundwater pumping and rainfall patterns.

5. There is also need to enhance the spatial resolution of the GRACE satellite for

better accuracy through integration with other appropriate satellite datasets. For

this purpose, Synthetic Aperture Radar and satellite altimetry could be the

potential products for spatial downscaling of the GRACE signal.

6. The study of relationship between climatic variables with groundwater storage

changes is important to analyze the future scenarios in the perspective of climatic

impacts on water resources.

90

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List of Publications

1. Iqbal, N., Faisal, H., Hyongki L., and Gulraiz A. (2016). Satellite gravimetric

estimation of groundwater storage variations over Indus Basin in Pakistan. IEEE

JSTAR, 9(8), 3524–3534. doi:10.1109/JSTARS.2016.2574378.

2. Iqbal, N., Faisal, H., Hyongki L., and Gulraiz A. (2017). Integrated groundwater

resource management in Indus Basin using satellite gravimetry and physical modeling

tools. Environmental Monitoring and Assessment, Vol, 189(3), pp. 1-16.

doi:10.1007/s10661-017-5846-1.

3. Iqbal, N., and Gulraiz A. (2017). Application of remote sensing technology for

groundwater resource management in Pakistan: Opportunities and Challenges.

Environmental Earth Sciences (In review).

Seminar Presentations

1. Seminar on “GRACE Applications for Groundwater Resource Management in

Indus basin”, February 24, 2017, Centre of Excellence in Water Resource Engineering,

University of Engineering and Technology, Lahore, Pakistan.

2. Seminar on “Application of Satellite Remote Sensing for the Estimation of

Groundwater Storage Variations in Indus Basin”, February 16, 2017, Department

of Earth Sciences, Quaid-e-Azam University, Islamabad, Pakistan.

3. Seminar on “Groundwater Resource Assessment and Management”, March 18,

2016, Punjab Irrigation Academy, Lahore, Pakistan.

4. Seminar on “Groundwater Resource Management in Pakistan Using Satellite

Gravimetry and Physical Modeling Tools”, November 03, 2015, Department of Civil

and Environmental Engineering, University of Washington, Seattle, USA.

Conference and Workshop Participation

1. “Sub-Regional Experts Meeting on Groundwater Management”, August 3-4, 2017.

The United Nations Educational, Scientific and Cultural Organization (UNESCO),

Islamabad-Pakistan.

2. “Asia-Pacific Regional Space Agency Forum (APRSAF-23) and Space

Application for Environment (SAFE) Workshop”, November 14-16, 2016. Japan

Aerospace Exploration Agency (JAXA), Manila-Philippine.

96

3. “Globalizing Societal Application of Scientific Research and Observations from

Remote Sensing: The Path Forward", June 23-25, 2015. National Aeronautics and

Space Administration (NASA), Tacoma-USA.

97

Reprints of Publications

99

Appendix

Appendix-A: Examples of Model Builder Tool for Data Processing and Analysis in Arc

GIS Software

i. ASCII to Raster File Conversion

ii. Calculation of monthly TWS Anomalies or UIP

Appendix-B: VIC Simulation Results Over Indus Basin (2002-2010)

Year Annual Simulated Stream Flow (MAF)

Marala Mangla Nowshehra Tarbela

2002 15.86 20.72 27.11 52.80

2003 21.74 28.25 33.44 51.03

2004 15.85 21.49 26.42 42.55

2005 17.49 20.97 36.86 51.73

2006 19.98 27.65 26.77 46.89

2007 15.50 20.75 33.79 36.31

2008 17.31 23.15 29.66 42.07

2009 12.99 16.69 32.28 37.02

2010 19.13 25.13 42.94 59.18

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Appendix-C: Observed Annual River Inflows (MAF)

Period Chenab at Marala Jhelum at Mangla Kabul at Nowshera Indus at Tarbela

2001-2002 18.90 11.85 12.38 48.09

2002-2003 23.45 17.40 14.58 56.22

2003-2004 25.86 22.67 18.90 63.63

2004-2005 21.32 18.46 17.07 51.57

2005-2006 25.13 23.19 27.98 65.53

2006-2007 27.71 23.21 20.05 65.04

2007-2008 20.57 17.70 24.02 57.41

2008-2009 19.82 19.25 17.93 55.98

2009-2010 17.85 21.04 22.80 56.04

2010-2011 25.81 25.74 28.92 72.26

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Appendix-D: Estimation of groundwater storage variations derived from GLDAS and VIC

Period UIP GLDAS GWS (mm) UIP VIC GWS (mm)

Jan-03 6.62 28.80

Feb-03 49.47 83.39

Mar-03 82.92 117.46

Apr-03 52.58 75.31

May-03 52.12 77.76

Jul-03 52.76 78.34

Aug-03 85.80 130.10

Sep-03 83.69 128.88

Oct-03 55.33 89.34

Nov-03 34.85 62.92

Dec-03 31.76 61.12

Jan-04 25.49 46.90

Feb-04 38.78 54.62

Mar-04 14.72 24.01

Apr-04 -7.94 -3.38

May-04 -22.94 -26.99

Jun-04 -38.59 -59.09

Jul-04 -40.70 -60.10

Aug-04 -13.87 -23.00

Sep-04 15.14 30.53

Oct-04 -41.42 -48.40

Nov-04 -25.77 -23.32

Dec-04 -49.18 -56.37

Jan-05 -17.50 -25.85

Feb-05 36.75 39.12

Mar-05 78.25 95.28

Apr-05 63.44 78.46

May-05 47.12 53.69

Jun-05 38.72 44.27

Jul-05 68.88 87.71

Aug-05 55.10 80.06

Sep-05 46.71 68.77

Oct-05 22.62 39.09

Nov-05 10.48 26.82

Dec-05 -15.01 -5.72

Jan-06 -14.50 -8.21

Feb-06 -9.88 -5.02

Mar-06 12.29 15.53

Apr-06 4.05 -1.48

May-06 -25.49 -41.29

Jun-06 -19.25 -32.03

Jul-06 -16.47 -28.31

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Aug-06 29.79 40.86

Sep-06 19.81 35.22

Oct-06 -25.07 -27.62

Nov-06 -37.26 -49.39

Dec-06 -14.65 -25.67

Jan-07 -6.00 -11.45

Feb-07 20.51 16.54

Mar-07 76.82 93.29

Apr-07 39.27 44.31

May-07 10.48 12.40

Jun-07 5.76 1.07

Jul-07 31.54 33.90

Aug-07 27.89 45.95

Sep-07 10.84 23.49

Oct-07 -19.79 -15.47

Nov-07 -47.28 -50.67

Dec-07 -43.78 -43.34

Jan-08 -19.07 -20.39

Feb-08 5.44 5.84

Mar-08 -0.39 3.48

Apr-08 -18.14 -33.97

May-08 -28.72 -48.78

Jun-08 -22.75 -42.82

Jul-08 18.15 22.64

Aug-08 57.64 81.89

Sep-08 38.62 59.04

Oct-08 12.34 28.72

Nov-08 -11.62 -1.90

Dec-08 -14.79 -12.66

Jan-09 -2.06 -8.22

Feb-09 20.61 20.43

Mar-09 28.30 27.55

Apr-09 34.82 29.19

May-09 10.82 0.18

Jun-09 -26.70 -49.16

Jul-09 -40.28 -75.56

Aug-09 -19.92 -39.73

Sep-09 -2.26 -13.12

Oct-09 -36.23 -50.58

Nov-09 -66.27 -89.69

Dec-09 -59.53 -76.24

Jan-10 -57.67 -71.37

103


Jan-10 -57.67 -71.37

Feb-10 -43.53 -58.14

Mar-10 -44.87 -72.27

Apr-10 -82.15 -120.21

May-10 -86.86 -125.85

Jun-10 -80.95 -117.35

Jul-10 -42.96 -82.54

Aug-10 40.40 39.64

Sep-10 42.21 59.42

Oct-10 5.21 13.06

Nov-10 -31.76 -36.82

Dec-10 -33.47 -34.35

Jan-10 -57.67 -71.37

Feb-10 -43.53 -58.14

Mar-10 -44.87 -72.27

Apr-10 -82.15 -120.21

May-10 -86.86 -125.85

Jun-10 -80.95 -117.35

Jul-10 -42.96 -82.54

Aug-10 40.40 39.64

Sep-10 42.21 59.42

Oct-10 5.21 13.06

Nov-10 -31.76 -36.82

Dec-10 -33.47 -34.35

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Appendix-E: Calculation Procedure for Groundwater Storage Anomalies

a. Estimation of the GRACE based Groundwater Storage Anomalies (km3)

GSEG = GAG * Area (UIP)

where;

GSEG = GRACE Groundwater Storage Anomalies (Volume in km3)

GAG = GRACE Groundwater Anomalies (Height in m)

Area (UIP) = Area of Upper Indus Plain (109,418.35 km2)

b. Calculations Procedure for Piezometric based Groundwater Storage Anomalies

Groundwater Level Change

GLCP = DTB – DTW

where;

GLCP = Piezometric Groundwater Level Changes (m)

DTW = Depth to Water Table (m)

DTB = Depth to Bedrock (Average DTB for Upper Indus Plain = 400 m)

Groundwater Level Anomalies

GLAP = GLCPM – GLCP

where;

GLAP = Piezometric Groundwater Level Anomalies (monthly in meters)

GLCPM = Long term Mean of Piezometric Monthly Groundwater Level Changes

(m)

GLCP = Piezometric Groundwater Level Changes (m)

Groundwater Storage Anomalies

GSAP = GLAP * SY

where;

GSAP = Piezometric Groundwater Storage Anomalies (m)

GLAP = Piezometric Groundwater Level Anomalies (m)

SY = Average Specific Yield (For Upper Indus Plain SY = 0.12)

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Groundwater Storage Estimation (km3)

GSEP = GSAP * Area (UIP)

where;

GSEP = Piezometric Groundwater Storage Anomalies (Volume in km3)

GSAP = Piezometric Groundwater Storage Anomalies (m)

Area (UIP) = Area of Upper Indus Plain (109,418.35 km2)

satellite gravimetric applications for groundwater

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