Application of Semi-Distributed Model for Hydrological
Assessment under Changing Climate – A Case Study of
Panjkora River Catchment
By
Haseeb Zafar
(000000171802)
DEPARTMENT OF WATER RESOURCES ENGINEERING AND
MANAGEMENT
NUST INSTITUTE OF CIVIL ENGINEERING
SCHOOL OF CIVIL AND ENVIRONMENTAL ENGINEERING
NATIONAL UNIVERSITY OF SCIENCE AND TECHNOLOGY
ISLAMABAD, PAKISTAN
(2019)
Application of Semi-Distributed Model for Hydrological
Assessment under Changing Climate – A Case Study of
Panjkora River Catchment
By
Haseeb Zafar
(000000171802)
A Thesis submitted in partial fulfillment
of the requirements for the degree of
Master of Science
in
Water Resources Engineering and Management
DEPARTMENT OF WATER RESOURCES ENGINEERING AND
MANAGEMENT
NUST INSTITUTE OF CIVIL ENGINEERING
SCHOOL OF CIVIL AND ENVIRONMENTAL ENGINEERING
NATIONAL UNIVERSITY OF SCIENCE AND TECHNOLOGY
ISLAMABAD, PAKISTAN
(2019)
ii
This is to certify that the
Thesis entitled
Application of Semi-Distributed Model for Hydrological
Assessment under Changing Climate – A Case Study of
Panjkora River Catchment
Submitted by
Haseeb Zafar
(00000171802)
Has been accepted in partial fulfillment of the requirements
Towards the award of the degree of
Master of Science in Water Resources Engineering and
Management
Dr. Shakil Ahmad
Assistant Professor
NUST Institute of Civil Engineering (NICE)
School of Civil & Environmental Engineering (SCEE)
National University of Sciences & Technology, Islamabad
iii
DEDICATED TO
My Father
iv
THESIS ACCEPTANCE CERTIFICATE
Certified that final copy of MS/MPhil thesis written by Mr Haseeb Zafar, Registration
No. 00000171802, of MS WRE&M 2016 Batch (NICE) has been vetted by
undersigned, found completed in all respects as per NUST Statutes/Regulations, is free
of wrongful plagiarism, errors, and mistakes and is accepted as partial fulfillment for
award of MS/MPhil degree. It is further certified that necessary amendments as
pointed out by GEC members of the scholar have been incorporated in the said thesis.
Signature___________________________
Name of Supervisor Dr. Shakil Ahmad
Date:
Signature (HoD)_______________________
Date:
Signature (Dean/Principal)_______________
Date:
v
ACKNOWLEDGMENTS
With the name of Almighty Allah, without help of Whom, nothing is possible and it
was only with the patience and knowledge HE bestowed upon me that I am writing the
acknowledgements for my Master’s Thesis.
I would like to extend my whole hearted gratitude towards my supervisor Dr. Shakil
Ahmad (NICE, NUST) for all the times he supported me technically and morally. It
was humble of him to spend his valuable time to teach and guide me throughout this
research.
I will always be in debt to the rest of my thesis guidance committee members, Dr.
Hamza Farooq Gabriel (NICE, NUST), Dr. Sajjad Haider (NICE, NUST) Dr. M.
Azmat (IGIS,NUST) and Sir. Zakir Hussain (PARC, Islamabad), for their willingness
to serve as members of the thesis committee. Their valuable suggestions for editing
and improving my thesis were quite valuable.
I want to acknowledge my father, Mr. Zafar Hussain, for all his guidance and support
throughout my life and it was only to follow him that I became a Civil Engineer and
went for Masters in Water Resources Engineering and Management.
I would also like to mention the support of my friends, especially Mr. Mohsin Raza
and Mr. Waqas Ahmed for supporting me unconditionally at all times.
Lastly I would like to thank all those I have not mentioned the names of, for there
tremendous help throughout my Masters, especially in research phase. Specifically Mr.
Junaid Aziz (IGIS, NUST) who helped me technically and morally, especially for the
use of GIS in my research
(Haseeb Zafar)
vi
ABSTRACT
The accuracy of daily stream flow predictions in watershed is an immense challenge
for un-gauged area particularly to changing climate due to quality of climate records.
In current study, various gridded meteorological datasets of Asian Precipitation Highly
Resolved Observational Data Integration Towards Evaluation (APHRODITE),
Tropical Rainfall Measuring Mission (TRMM), Precipitation Estimation from
Remotely Sensed Information using Artificial Neural Networks (PERSIANN) and
Climate Forecast System Reanalysis (CSFR) were used. Soil and Water Assessment
Tool (SWAT) was applied to simulate and project the streamflows for Panjkora River
watershed which is located in North-West of Pakistan. SWAT after calibration
depicted better result with APHRODITE for precipitation and CFSR for minimum and
maximum temperatures. The monthly simulated streamflows were calibrated from
2006-2009 (4 years) and validated for 2010-2012 (3 years). The model performance
was evaluated using Coefficient of Determination (R2) and Nash-Sutcliffe Simulation
Coefficient (NS), showed acceptable values of 0.70 and 0.58 for calibration period and
0.71 and 0.67 for validation period respectively, both laying within acceptable ranges.
Further, validated model was used to study projected streamflows end of 21st century
using bias corrected Himalayan Adaptation, Water and Resilience (HI-AWARE)
General Circulation Models (GCMs) dataset of both Representative Concentration
Pathways i.e. RCP4.5 and RCP8.5 scenarios, showing high and medium scenarios.
Daily minimum temperatures (Tmin) are projected to experience a general increase for
the coming years. 2025s experience highest increase in the summers with an increase
vii
of 1.9°C with RCP 4.5. Winter and spring have an increase of 1.3°C and 0.9°C
respectively with RCP 8.5. For 2055s, summer again has the highest increase with
3.3°C, 2.7°C for winter, and 2.4°C for spring, all with RCP 8.5. For the last part of this
century, with an increase of 5.4°C, summer again has the highest increase. Spring and
winter have an increase of 4.4°C and 4.3°C respectively, all with RCP 8.5. Daily
maximum temperatures (Tmax) are projected to follow a similar trend, with the
maximum increase of 2.0°C in the autumn of 2025s for RCP 4.5, second to an increase
of 1.2°C for spring and 0.5°C for the winter of the same time period with RCP 8.5.
Moreover for 2055s, spring season is expected to experience the maximum increase of
3.2°C with RCP8.5. Autumn season also had a substantial increase of 3.0°C with RCP
4.5 pushing winter season to third place with an increase of 2.6°C with RCP 8.5.
Towards the end of this century, 2085s had similar case, with an increase of 5.6°C,
4.8°C, and 4.6°C for spring, winter and autumn season respectively, all for RCP 8.5.
Currently the area experiences its highest precipitation in winter season with a monthly
average of 107.8 mm. Moreover winter and autumn precipitation is expected to
increase and a decline is projected for the remaining part of the year. In the near future
i.e. 2025s, highest decrease will be in summer (31.7 mm) with RCP 8.5 and an
increase of 22.4 mm in the winters. however projected changes in 2055s show a
different trend, although maximum decrease (38.7 mm) stays in summer with RCP4.5,
the maximum increase (22.6 mm) shifts in autumn with RCP 8.5. And by the end of
21st century, a decrease of 41.1 mm is expected in summer with RCP4.5 and an
autumn increase of 29.2 mm with RCP 8.5. Forecast of monthly flows shows temporal
shift in peak flows as the area currently experiences its peak in April but from 2055s
viii
onwards, it is projected to be shift in March. The winters of 2025s will experience the
highest increase of 28.17 Cumecs with RCP 8.5 while the same reflects a decrease in
summers as 9.41 Cumecs. During the 2055s, winter flows are expected to rise by 45.72
Cumecs in comparison to a summer and spring decrease of 14.11 Cumecs and 33.01
Cumecs respectively (RCP 8.5). 2085s reflect a similar scenario of flows, increasing
up to 63.62 Cumecs for winters and a decrease of 55.93 Cumecs in spring (RCP 8.5).
ix
Table of Contents
THESIS ACCEPTANCE CERTIFICATE .............................................................. 4
LIST OF ABRIVIATIONS .................................................................................... 14
LIST OF TABLES .................................................................................................. 16
LIST OF FIGURES ................................................................................................ 18
Chapter 1 .................................................................................................................. 1
INTRODUCTION .................................................................................................... 1
1.1 General ............................................................................................................. 1
1.2 Problem Statement ............................................................................................ 4
1.3 Objective ........................................................................................................... 5
1.4 Scope ................................................................................................................ 5
1.5 Research Hypothesis ......................................................................................... 6
1.6 Organization of Thesis ...................................................................................... 7
Chapter 2 .................................................................................................................. 8
LITERATURE REVIEW ........................................................................................ 8
2.1 General ............................................................................................................. 8
2.2 Description of SWAT........................................................................................ 8
2.3 Curve Number................................................................................................. 10
2.4 Antecedent Moisture Condition ....................................................................... 10
x
2.5 Terms used for Climate Change Assessment ................................................... 11
2.5.1 General Circulation Model (GCM) ........................................................... 11
2.5.2 Coupled Model Inter–Comparison Project (CMIP) ................................... 11
2.5.3 Representative Concentration Pathways (RCPs)........................................ 11
2.5.4 Bias Correction ......................................................................................... 12
2.5.5 Delta Technique ........................................................................................ 13
2.6 SWAT CUP .................................................................................................... 14
2.7 Previous Studies .............................................................................................. 15
2.7.2 Hydrological Assessment using SWAT ........................................................ 15
2.7.3 Use of Gridded Hydro-Meteorological Datasets in SWAT............................ 16
2.7.4 Climate Change impact assessment using SWAT and GCMs data ................ 17
2.8 Efficiency Criteria ........................................................................................... 17
2.8.1 Nash–Sutcliffe Coefficient (NS) ............................................................... 18
2.8.2 Coefficient of Determination..................................................................... 18
2.8.3 Root Mean Squared Error (RMSE) ........................................................... 19
Chapter 3 ................................................................................................................ 20
METHODOLOGY ................................................................................................. 20
3.1 Study Area ...................................................................................................... 20
3.2 Satellite Datasets ............................................................................................. 22
xi
3.2.1 Land-cover ............................................................................................... 22
3.2.2 Soil Dataset............................................................................................... 24
3.2.3 DEM ......................................................................................................... 22
3.2.4 Hydro-Climate datasets ............................................................................. 25
3.2.4.1 Temperature ........................................................................................... 25
3.2.4.1.1 CFSR .................................................................................................. 25
3.2.4.2 Precipitation ........................................................................................... 26
3.2.4.2.1 PERSIANN ......................................................................................... 27
3.2.4.2.2 TRMM ................................................................................................ 28
3.2.4.2.3 APHRODITE ...................................................................................... 29
3.2.5 RCPs Climate Dataset ............................................................................... 30
3.3 Methodology ............................................................................................... 32
3.3.1 Preprocessing............................................................................................ 32
3.3.2 Processing................................................................................................. 33
3.3.3 Future Assessment .................................................................................... 33
Chapter 4 ................................................................................................................ 35
RESULTS AND DISCUSSIONS ........................................................................... 35
4.1 Calibrated Parameters...................................................................................... 35
4.2 Flows .............................................................................................................. 38
xii
4.2.1 TRMM ..................................................................................................... 38
4.2.2 PERSIANN .............................................................................................. 39
4-4 APHRODITE .............................................................................................. 40
4.6 Bias Correction ............................................................................................... 41
4.7 Maximum temp ........................................................................................... 43
4.8 Minimum temp ............................................................................................ 46
4.9 Precipitation ................................................................................................ 49
4.10 Snowmelt................................................................................................... 52
4.11 Flows ......................................................................................................... 55
1.12 Discussion ..................................................................................................... 61
Chapter 5 ................................................................................................................ 64
CONCLUSIONS AND RECOMMENDATIONS ................................................. 64
5.1 Conclusion ...................................................................................................... 64
5.2 Recommendations ........................................................................................... 65
References ................................................................................................................ 66
xiii
LIST OF ABRIVIATIONS
AMIP
Atmospheric Model Inter–comparison Project
APHRODITE
Asian Precipitation - Highly-Resolved Observational Data
Integration Towards Evaluation
ASTER GDEM
Advance Space–borne Thermal Emission and Reflection
Radiometer Global Digital
AWS
Automatic Weather Station
CFSR
Climate Forecast System Reanalysis
CMIP5
Couple Models Inter Comparison Phase 5
CN
Curve Number
GIS
Geographic information System
HI–AWARE
Himalayan Adaptation, Water and Resilience
IRIS
Indus River Irrigation System
IGB
Indus, Ganges and Brahmaputra
IPCC
Intergovernmental Panel on Climate Change
IWT
Indus Waters Treaty
MODIS
Moderate Resolution Imaging Spectroradiometer
NS
Nash Sutcliff coefficient
PCRWR
Pakistan Council of Research in Water Resources
PERSIANN
Precipitation Estimation from Remotely Sensed Information using
Artificial Neural Networks
PES
Pakistan Economic Survey
xiv
PIDE
Pakistan Institute of Development Economics
RCPs
Representative Concentration Pathways
RMSE
Root Mean Square Error
RS
Remote Sensing
SCS
Soil Conservation Service
SWAT
Soil and Water Assessment Tool
SWAT-CUP
Soil and Water Assessment Tool Calibration/Uncertainty Programs
TRMM
Tropical Rainfall Measurement Mission
UIB
Upper Indus Basin
xv
LIST OF TABLES
Table 2.1: Description of RCPs………………………………………………….……12
Table 3.1 Coordinates of Study Area Monitoring Point……………………………...20
Table 3.2.1 Land Cover in study area…………………………………………………22
Table 3.2.2 Soil Types over the study area……………………………………………23
Table 3.2.4.1 Location of virtual Temperature weather station………………………26
Table 3.2.4.2.1 Positions of PERSIANN weather stations……………………………27
Table 3.2.4.2.2 Positions of TRMM weather stations……………………...…………28
Table 3.2.4.2.3 Positions of APHRODITE weather stations………………………….29
Table 3.2.5 Description of GCMs …………………………………………………..30
Table 4.1 Parameters and their values for SWAT …………………………………..37
Table 4.5 Efficiency of Precipitation datasets…………………………...……………41
Table 4.6a Maximum Daily Temperature…………………………………………….42
Table 4.6b Minimum Daily Temperature……………………………………………..43
Table 4.6c Average daily precipitation………………………………………….…….43
Table 4.7.1 Monthly Maximum Temperature in Degree Celsius……………………..44
Table 4.7.2 Monthly Average Maximum Temperature……………………………….45
Table 4.7.3 Monthly Average Maximum Temperature…………………...…………..45
Table 4.7.4 Monthly Maximum Temperature in Degree Celsius ……………..……...46
Table 4.7.5 Seasonal Maximum Temperature in Degree Celsius …………...………46
xvi
Table 4.8.2 Monthly Minimum Temperature in Degree Celsius …………………….49
Table 4.8.3 Seasonal Minimum Temperature in Degree Celsius …….………………49
Table 4.9.3 Monthly Precipitation …………………………………...……………….52
Table 4.9.4 Seasonal Precipitation …………………..………………….……………52
Table 4.10.3 Difference in Monthly Snowmelt (mm)………………..……………….55
Table 4.10.4 Difference in Seasonal Snowmelt (mm)…………...……………………55
Table 4.11.6 Table of Monthly Flows……………………………………...…………60
Table 4.11.7 Table of Seasonal Flows……………………………………...…………60
Table 4.11.8 Table of Deviance of Monthly Flows from Base Period……………..…61
Table 4.11.9 Table of Deviance of Seasonal Flows from Base Period……..………...61
xvii
LIST OF FIGURES
Figure 1.1 Map of Pakistan showing rivers ……………………………...…...………..2
Figure 1.2: Indus Basin Irrigation System of Pakistan…………………………………3
Figure 1.3: Global Change in (a) Temperature and (b) Precipitation…………………..5
Figure 3.1 Location of study area …………………………………………………….21
Figure 3.2.1 Land Cover map…………………………………………………………22
Figure 3.2.2 Soil Type distribution over the area……………………………………..23
Figure 3.2.3 Slope Map of the area…………………………………………………...24
Figure 3.3 Schematic Diagram of Methodology ……………………………………32
Figure 4.2 Simulation via TRMM…………………………………………………….39
Figure 4.3 Simulation via PERSIANN……………………………………………….40
Figure 4.4 Simulation via APHRODITE…………..…………………………………41
Figure 4.7.1 Monthly Maximum Temperature………………………………………..44
Figure 4.7.2 Monthly Average Maximum Temperature ……………………………..45
Figure 4.7.3 Seasonal Average Maximum Temperatures…………………………….45
Figure 4.8.1 Monthly Minimum Temperatures……………………………………….47
Figure 4.8.2 Monthly Average Maximum Temperature ……………………………..48
Figure 4.8.3 Seasonal Average Maximum Temperatures ……...…………………….48
Figure 4.9.1 Monthly Precipitation in millimeters………...………………………….50
Figure 4.9.2 Monthly Precipitation in millimeters ……………………….…………51
xviii
Figure 4.10.1 Monthly Snowmelt ……………….…………………………….……..53
Figure 4.10.2 Seasonal Snowmelt…………….………………………………………54
Figure 4.11.1 Monthly Flows of GCMs……………………………...……………….57
Figure 4.11.2 Average Monthly Flows………………………….………………….....58
Figure 4.11.3 Average Seasonal Flows………………………………………….....…58
Figure 4.11.4 Percentage Change in Monthly Flows………………..…………….….59
Figure 4. 11.5 Percentage Change in Seasonal Flows ………………………………..59
xix
1
Chapter 1
INTRODUCTION
1.1 General
Water is second only to breathable air, when it comes to comparing resources essential
for human survival. The fact that ancient man in the prehistoric times settled along the
banks of rivers show that mankind realized this in prehistoric times. The Indus
Civilization, one of the most ancient, inhibited along the banks of Indus River. As
partition of Indian Sub-continent, Indus River became part of Pakistan (Fig 1.1).
Pakistan is ranked as sixth largest populated (population ≈200 million) country in the
world (PES, 2017) with increasing rate of 1.86% per annum. While its economy is
highly dependent upon agriculture and more than 90% of available water is being used
by the agriculture sector (PIDE, 2007), a considerable population (≈40 %) is
associated with agriculture activities (PIDE, 2007).
The Indus Water Treaty of 1960 divided the regional rivers between the two states of
Pakistan and India, with India given rights of the three Eastern Rivers (Ravi, Sutlej &
Beas) and the three Western Rivers (Indus, Jhelum & Chenab) were allocated to
Pakistan. Pakistan was allowed to use the waters from these Western Rivers to irrigate
its lands in its east, which led to the birth of Indus River Irrigation System (IRIS) (Fig
1.2).
2
Fig 1.1 Map of Pakistan showing rivers (Source: Tahir et al. (2011b)
Indus River originates from the Tibetan Plateau and initially flows west towards the
Northern Areas of Pakistan and then starts its long journey southwards into the
Arabian Sea, irrigating the lands of Pakistan on its way. The total catchment area
expands into four countries; Pakistan, India, China and Afghanistan, covering up an
area of approximately 966000KM 2, with more than 60% of the area in Pakistan (Yang
Y., 2014).
3
Figure 1.2: Indus Basin Irrigation System of Pakistan (Source: PCRWR, 2012)
IRIS includes 2 mega dams, 8 link canals and 5 barrages, to irrigate the Punjab Eastern
lands due to lack of water in the Eastern Rivers (WAPDA, 2013). Three major
reservoirs, namely, Mangla Dam, Tarbela Dam, and Chashma Reservoir were
constructed to fulfill the basic water requirements of the country. The former two also
produce the healthier portion of the nation’s Hydro-Electricity. These reservoirs are
4
losing the storage capacities mainly due to sedimentation and will lose up to 37%
(5.96MAF) of their capacity by 2025 (Wapda, 2013).
The agricultural country of Pakistan has almost 77 MA (Million Acre) of cultivable
land, but 20.3 MA of this is still lying useless due to insufficient availability of water.
The rest of 56.7 MA gets it better share from the irrigation canal system. The virgin
uncultivated land can be used for agriculture if it is brought under irrigation network,
which is, after having sufficient water storage capacity (Abid, 2012).
1.2 Problem Statement
Water is the source of all life on planet Earth, and its sustenance depends on better
understanding and management of water resources. Anthropogenic activities have led
to Climate Change, and therefore changes in precipitation and temperature patterns are
likely that will considerably affect the hydrology of catchments in the high altitude
region (IPCC, 2014)(Fig 1.3). Moreover the young state of Pakistan lacks a wide
network of Automatic Weather Stations (AWS) throughout the country, consequently
leading to the use of hydrological models. Finally there are human limitations of
understanding the nature and practical limitations of fine, accurate observation and
computation, all adding up the complications to achieve the goal of hydrological
modeling and climate change impact studies. This study was carried out as an attempt
to apply a semi-distributed model to Panjkora River basin at Koto, and to project the
water availability under changing climate enabling better water resource management
in the future.
5
Figure 1.3: Global Change in (a) Temperature and (b) Precipitation (Source: IPCC 2014)
1.3 Objective
This study has two main objectives. First is to apply a semi-distributed watershed
based hydrological model to simulate the stream flows at Panjkora River at Koto. And
second to project stream flows under the impact of changing climate.
1.4 Scope
The scope of this study is limited to the use of a semi-distributed hydrological model
for the study area, and use the validated model to study future water availability under
changing climate. To check model efficiency, statistical evaluators of R square, Nash
Sutcliff (NS), and Root Mean Square Error (RMSE) be applied. Global Circulation
Models (GCMs) present in Couple Model Inter Comparison Phase 5 (CMIP5),
developed by (Lutz, 2016), will be used to incorporate effects of climate change till the
end of this century (year 2100.) Remote sensing data for land use, soil type and slopes
will be reclassified as per SWAT requirements to model the physical characteristics of
6
study area. As there are no weather stations in the area to provide with weather data,
global datasets for precipitation and daily minimum and maximum temperatures will
be used.
1.5 Research Hypothesis
To achieve mentioned objectives, the following framework was followed to carry out
research and to obtain results leading to conclusions.
1. The gauging station at Koto for Panjkora River was chosen for identification of
catchment area and modeling.
2. Obtaining and/or generating inputs;

DEM

Land use (FAO)

Soil (FAO)

Precipitation (CFSR, APHRODITE, TRMM)

Temperature (CFSR)
3. Hydrological modeling using ArcSWAT, calculating daily flow values.
4. Using SWAT-CUP for sensitivity analysis and calibration.
5. Acquiring observed values to check efficiency.
Future water availability was assessed by incorporating bias corrected Indus, Ganges
and Brahmaputra (IGB) future climate GCMs dataset for RCPs scenarios.
7
1.6 Organization of Thesis
The thesis consists of 5 chapters, including an outline of which in mentioned herewith
Chapter 1 presents the introduction of the study area that includes, problem statement,
objectives of study, study area, research hypothesis and scope of the study.
Chapter 2 has mainly focused on the literature review.
Chapter 3 comprises of detail study area and methodology
Chapter 4 presents the results and discussions
Chapter 5 comprises of summary of the study, conclusions and recommendations
8
Chapter 2
LITERATURE REVIEW
2.1 General
Natural phenomenons are complex, intertwined and practically impossible to
completely understand. The physics involved in any natural system is too vast for the
human brain to comprehend in totality, where as we try to simplify nature to study it in
the hope of using it to our favor. The system of water, from clouds to rains to surface
water, is one of the most complex, if not the most complex systems of all. Mankind
has historically vigorously tried to study and understand this complicated system, as
basic survival instinct of life as we know it depends most on water, second to oxygen.
To achieve such a system enabling to forecast and ensuring for the survival, many
approaches and models have been applied. Almost all these models basically have
mathematical based approach as being able to recreate nature for experimentation is
beyond our capabilities at this evolutionary stage.
2.2 Description of SWAT
A 30 year modeling experience of USDA ARS (United States Department of
Agriculture
Agricultural Research Service) has led to the development of
SWAT(Gassman P. W., 2007) The Arc SWAT is an Arc GIS interface of SWAT
model (Arnold J.G., 1998) It is a continuous, semi-distributed, process based river
basin model developed to help in decision making process in water resources(Arnold,
2012). Developed by to study large and heterogeneous watersheds, with varying land-
9
use and soils, under developing land management practices over large period of times
(Arnold J.G., 1998)
Being a semi distributed model, the whole watershed is divided into sub-basins. These
sub-basins are sub-catchments of the whole watershed which drain into the final subbasin locating the control point. These sub-basins are further fragmented into HRUs. A
HRU (Hydrologic Response Unit) is an area in the sub-basin having similar land use,
soil type, and slope. HRU definition divides the area into parts which will have same
hydrologic response to hydrologic conditions.
These hydrologic conditions are weather driven, and factors like daily precipitation,
min/max temperature, relative humidity, wind speed and solar radiation govern these
conditions and their relative response. To study response, SWAT needs input of these
factors, which can be from recorded stations or developed from global climate
datasets.
Natural hydrologic processes synthesized by SWAT(Arnold, 2012) include;










Canopy storage
Surface Runoff
Infiltration
Evapotranspiration
Lateral flow
Tile drainage
Redistribution of water within soil profile
Consumptive use
Return flow
Recharge by seepage
10
The mathematical base and governing equations are provided in the SWAT theoretical
documentation ((http://swatmodel.tamu.edu) and in (Arnold J.G., 1998).
2.3 Curve Number
A parameter that combines soil type and land use to estimate runoff potential, based on
the Hydrologic Soil Group (HSG), land use and condition. It ranges between between
0 and 100. The greater the curve number, greater the potential for Runoff RO.
Impervious areas and water surfaces are assigned curve numbers of 98-100.
2.4 Antecedent Moisture Condition
Antecedent Moisture condition is the preceding relative moisture of the pervious
surfaces prior to the rainfall event. This is referred as Antecedent Runoff Condition
(ARC) and is considered to be low when there has been little preceding rainfall and
high when there has been considerable preceding rainfall prior to the modeled rainfall
event.
Three AMC conditions considered for dry and wet conditions are;

AMC(I) : Dry conditions

AMC(III) : Wet conditions

AMC(II) : Average moisture conditions
For modeling purposes, we consider watersheds to be AMC(II), which is essentially
an average moisture condition.
Typical curve numbers for AMC(II) are given by various tables (Neitsch SL, 2005).
11
2.5 Terms used for Climate Change Assessment
2.5.1 General Circulation Model (GCM)
A type of climate model which represents as interaction between and the movement of
planetary atmosphere, mathematically. GCMs are used to forecast weather and predict
the impacts of climate change (IPCC, 2014).
2.5.2 Coupled Model Inter–Comparison Project (CMIP)
CMIP is a part of Atmospheric Model Inter Comparison Project (AMIP) which works
as the correspondent for global coupled ocean–atmosphere general circulation models
(GCMs). The most recent phase of this project is CMIP5.
2.5.3 Representative Concentration Pathways (RCPs)
RCPs are the greenhouse gas concentration (not emissions) trajectories adopted by
IPCC for AR5 in 2014. CMIP5 provides four RCPs, named RCP 8.5, 6.0, 4.5 and 2.6
with the numbers denoting a rough estimate of the radiative forcing by the end of 21st
century, with 8.5 showing that the radiative forcing by the end of this century will
reach 8.5W/m2, and similarly for others (IPCC AR5 2014)
12
Table 2.1: Description of RCPs by IPCC
Scenario
Characteristics
RCP 2.6
An extremely low scenario that reflects
aggressive greenhouse gas reduction and
sequestration efforts.
RCP 4.5
A low scenario in which greenhouse
emissions stabilize by mid-century and fall
sharply thereafter.
RCP 6.0
A medium scenario in which greenhouse
gas emissions increase gradually until
stabilizing in the final decades of 21st
century.
RCP 8.5
A high scenario that assumes continued
increase in the greenhouse gas emissions
until the end of 21st century.
2.5.4 Bias Correction
Outputs of climate models always have some systematic biases (Portal, 2015)which
may be due to:

Numerical structures

Inadequate spatial resolution

Lake of understanding of climate system processes

Basic physics and thermodynamic processes
13
These biases need to be removed before using the output data. It is assumed that
although the output is biased, it follows the same trend as true values do, therefore bias
correction is done to shift the data from being biased to corrected. There are several
methods available for bias correction including:

Multiple linear regression

Local intensity scaling

Delta change approach

Quantile mapping
2.5.5 Delta Technique
For this study, Delta Technique was chosen to remove biasedness in the GCM output
data. The equations used in delta method are described below
𝑉𝑡𝑢𝑛𝑒𝑑= 𝑉𝑜𝑏𝑠/𝑉𝑟𝑒𝑓
→ (2.2)
𝑆𝑡𝑢𝑛𝑒𝑑=𝑆𝑜𝑏𝑠/𝑆𝑟𝑒𝑓
→ (2.3)
𝐸𝑠 = (𝑉𝑝𝑟𝑜𝑗−𝑉𝑟𝑒𝑓).𝑆𝑡𝑢𝑛𝑒𝑑
→ (2.4)
𝑝𝑟𝑜𝑗 = 𝐸𝑠 + (𝑉𝑟𝑒𝑓.𝑉𝑡𝑢𝑛𝑒𝑑)
→ (2.5)
𝑉𝑜𝑏𝑠 is the observed climatology, 𝑉𝑟𝑒𝑓 is the reference climatology for the
GCM/RCM baseline, 𝑉𝑡𝑢𝑛𝑒𝑑 is the adjusted factor for mean climate, 𝑆𝑜𝑏𝑠 is the
standard deviation of monthly observed data set, 𝑆𝑟𝑒𝑓 is the standard deviation of
GCM/RCM, 𝑆𝑡𝑢𝑛𝑒𝑑 is the signal to noise ratio, 𝑉𝑝𝑟𝑜𝑗 is the particular projected
month that needs correction, 𝐸𝑠 is the signal enhance or signal dampened for particular
14
projection month and 𝐸𝑝𝑟𝑜𝑗 is the bias corrected climatic variable for particular month
(Burhan A, 2015; Hay L. E., 2000; Maraun, 2016).
2.6 SWAT CUP
Developed by Eawag (2009), is a semi-automatic tool for sensitivity analysis and
calibration. Default output of Arc SWAT acts as input for SWAT-CUP. SWAT CUP
offers multiple options for auto-calibration like SUFI2, GLUE and ParaSol. SUFI 2
was used for this study as it is suggested by many researchers including a
comprehensive study (Arnold, 2012). (Shimelis G. Setegn, 2008) suggests the same as
GLUE requires extensive computation and ParaSol does not consider error in model
structure. It is suggested to divide the time period available in to two time periods, for
calibration and validation. First a sensitivity analysis is required and then the resulting
sensitive parameters are iterated within user provided ranges for calibration. It is
suggested to go for 300-500 iteration to close in on the results.
15
2.7 Previous Studies
2.7.2 Hydrological Assessment using SWAT
The advancements in remote sensing and GIS technologies have led to development
and use of spatial and physical conditions based hydrological models. The biggest
hurdle is the unavailability of data, especially in developing countries. The Researcher
believes that hydrological modeling will evolve and get better only with improvement
in remote sensing techniques. ASTER GDEM and FAO Soil data was used whereas
landuse was developed from Landsat imagery. Global Gridded dataset of UK-CRU
was used for hydro-climatology and reached to the conclusion that CN2, SOL, AWC
and ESCO are the most sensitive parameters. The R square and NS checks turned out
to be 0.8.(Fadil, 2011). (Arnold, 2012) did a comprehensive study on SWAT’s use and
calibration an it was found that monthly time step for model outputs is preferred as
daily prediction system produced poorest results. The most widely used analysis for
checking calibration and validation are the statistical methods of R square and NSE.
As there is still confusion about what validation is, there remains a gap of guidelines
for separating data into two parts for calibration and validation, however it is
suggested that both should have a cyclicity of dry and wet seasons. SWAT CUP is a
tool developed by Eawag which can be used for calibration via automated or semiautomated methods. SUFI-2 is a semi-automated approach towards calibration as it
allows user to manually adjust parameters and their ranges in between auto-calibration
runs.
16
SWAT was applied for hydrological simulation at Barinallah Watershed (India) by
(Khare, 2014). The watershed has similar features to this study’s with a hilly area
where dominant landuse is forest and most of the soil is loam. R square and NS came
out to be 0.93 and 0.89 using local meteorological data for weather stations.
In Pakistan, Simly Dam catchment was modeled by (Shimaa, 2015) using hydro-met
data from Pakistan Meteorological Department (PMD), FAO soil data, and Landsat
image for land cover. Most sensitive parameters were CN2, ESCO and GW_REVAP,
similar to most of the studies using SWAT. R square and NS were calculated to be
0.93 and 0.85 respectively.
2.7.3 Use of Gridded Hydro-Meteorological Datasets in SWAT
Developing countries like Pakistan suffer from the issue that there are not enough
weather stations covering the region, therefore use of global gridded hydrometeorological datasets is essential. Similar to this study APHRODITE, CFSR,
PERSIANN and TRMM datasets were used by (Thom, 2017) for Srepok River
catchment in Vietnam. With an R square of 0.75 and 0.9 for APHRODITE and
TRMM, it was concluded that these two show potential. A large scale study was
conducted by (Abbaspour, 2015) for whole of Europe using Modis Landuse, FAO soil
data and Gridded dataset for hydro-meteorology over different rivers. R square values
ranged from 0.3-0.8 and NS from 0.2-0.9.
17
2.7.4 Climate Change impact assessment using SWAT and GCMs
data
There is an unequivocal effect of climate change on precipitation patterns, reinforcing
the need of using hydrological models (Fohrer, 2005). SWAT has been used to study
the effects of changing climate over various watersheds, (Gassman P. W., 2007)
conducted a comprehensive study about SWAT’s historical development and
applications and reports of various applications of SWAT using GCMs data. GCMs
data always have some kind of bias; therefore it needs to be corrected. Delta method is
widely used for correction, (Moradkhani, 2010) used SWAT and GCMs data for
climate change impact study for Lower Tualatin River Basin and used delta technique
for bias correction. R square and NS came out to be 0.66 and 0.41 respectively.
Similarly (Manoj Jha, 2006) used GCMs and SWAT and reached R square and NS of
0.82 and 0.81 respectively. SWAT has been applied for climate change analysis over
numerous basins in USA (Fohrer, 2005).
2.8 Efficiency Criteria
The results of hydrological models are seldom capable of representing nature
completely, therefore their efficiency needs to be checked before using them for any
decision making. Statistical correlation techniques are used to check the correlation
between model outputs and observed data.
18
2.8.1 Nash–Sutcliffe Coefficient (NS)
Nash–Sutcliffe Coefficient is the most widely used technique to find correlation for
checking the efficiency of hydrological models (Krause P., 2005). It is governed by
equation 2.8.1;
𝑁𝑆 = 1 −
∑
∑
(
)
(
 (2.8.1)
)
Where, 𝑄𝑜 is the mean observed discharges, Qm is modeled discharge at time t, Qot is
observed discharge at time t.
*Nash–Sutcliffe efficiency can range from −∞ to 1.
2.8.2 Coefficient of Determination
This is calculated by taking the correlation coefficient and then squaring it check the
correlation (Krause P., 2005)It checks how close is the relation to “y = x” relation. It is
calculated using equation 2.8.2,
𝑅 =
(
) (
(
)(
) }{
*Coefficient of determination value range from 0 to 1
)
(
) }
 (2.8.2)
19
2.8.3 Root Mean Squared Error (RMSE)
The RMSE is a quadratic scoring rule which measures the average magnitude of the
error (Chai, 2014). Equation 2.8.3 is used to calculate RMSE,
𝑅𝑀𝑆𝐸 =
*RMSE can range from 0 to ∞.
∑
(𝑚𝑜𝑑𝑒𝑙𝑖 − 𝑜𝑏𝑠𝑒𝑟𝑣𝑒𝑑𝑖)
 (2.8.3)
20
Chapter 3
METHODOLOGY
3.1 Study Area
Panjkora River lies in North-West of Pakistan. Water of Panjkora River makes its way
by joining Swat River near Chakdara in Khyber Pakhtunkhua (KP) and then Kabul
River near Charsadda (KP) finally flowing into Indus River.
The devastating flood of year 2010 that hit Pakistan is also proposed to have initiated
from this area as result of a cloud burst, making this area sensitive and important for
hydrological studies. Panjkora River not only provides sustenance to its local habitat,
but is planned for Koto Hydro Power Project by PEDO (Pakhtunkhua Energy
Development Organization.) The river births by the precipitation in the area and then
flows south to Upper and Lower Dir Districts of KP, irrigating all the areas in its way.
This study’s concern area was limited to the watershed delineated for gauging station
at Koto, KP.
To run successful simulation and assess model efficiency, observational data of
streamflows was acquired from PEDO, which are recorded at a bridge near the city of
Koto, with coordinates mentioned in Table 3.1.
Table 3.1 Coordinates of Study Area Monitoring Point
Station
Koto
River
Longitude
Latitude
Panjkora 71.87333334 34.86654518
21
Figure 3.1 Location of study area
The elevation in the watershed ranges from 5630m at the highest to 764 at the lowest,
resulting to an average elevation of 2584m, so this area comprises of high peaks and
low valleys. The dominant land use is of green area, mostly having forests and grass
lands and the prevailing soil type has clayey texture. The watershed experiences
annual precipitation of approximately 91 mm and temperatures remaining moderately
cold (as per APHRODITE and CFSR global datasets). The entire catchment covers an
area of 3847.47 Km2.
The basin moves along the political boundary between Pakistan and Afghanistan and
never into Afghanistan.
22
3.2 Datasets
3.2.1 Digital Elevation Model (DEM)
The first step in for using SWAT is to delineate the watershed that is to identify all the
area where precipitation leads to flow at the control point. This is done by providing a
DEM and identifying the user outlet point, SWAT then computes the entire boundary
of catchment area, secondly identify individual streams and their sub basins.
The
Advance
Thermal
Spaceborne
Emission
and
Reflection Radiometer Global
Digital
Elevation
Model
(ASTER GDEM) available at
30×30 m resolution was used for
the
delineation
of
Koto
catchment and extraction of
physical parameters such as
elevation and slope to delineate
the watershed.
Figure 3.2.3 Slope Map of the area
23
3.2.2 Land-cover
Land Cover data is essential for semi-distributed models like SWAT as it helps to
incorporate physical characteristics of the watershed into the model. International
Centre for Integrated Mountain Development (ICIMOD) has developed and classified
remote sensed data of land use for the year 2010 which is available at
(http://geoapps.icimod.org). ICIMODs classification of land cover is not completely in
sync with SWAT inputs therefore it was reclassified using ArcGIS.
Table 3.2.1 Land Cover in study area
ID
Land use
Area
(%)
FRSE
Forest-
30.41
Evergreen
FRST
Forest-Mixed
11.19
FRSD
Forest-
0.45
Deciduous
RNGE
Range-Grasses
35.71
ALFA
Alfalfa
1.75
ID
Land use
Area
(%)
AGRR
Agricultural
7.15
Land-Row
Crops
AGRL
Agricultural
0.56
Land-Generic
BARR
Barren
10.41
WATR
Water
2.36
Figure 3.2.1 Land Cover map
24
3.2.3 Soil Dataset
To study the relation between precipitation and streamflow, soil data is crucial to
compute losses. SWAT requires soil classification of the study area to calculate these
losses. So Food and Agriculture Organization (FAO) Global soil dataset for Asia was
utilized. This data is available through FAO/UNESCO portals at 1:5000,000 scale. The
data was downloaded from http://www.waterbase.org and then extracted for the study
area.
Table 3.2.2 Soil Types over the study area
SEQN
SNAM (FAO)
AREA (%)
3503
I-B-U-2c-3503
83.09
3673
Be73-2c-3673
14.98
3870
Xh18-bc-3870
1.93
The default SWAT database’s user soil table
does not incorporate Asian soils, therefore it
was updated with Asian soils.
Figure 3.2.2 Soil Map
25
3.2.4 Hydro-Climate datasets
SWAT requires two climatic parameters for flow computation;

Precipitation in mm/day

Temperature in terms of daily minimum and maximum in degree Celsius.
As the study area has no on ground weather stations, so this study used gridded global
precipitation and temperature datasets to carry out the computations. Daily streamflow
data record was obtained from PEDO at Koto control point.
3.2.4.1 Temperature
3.2.4.1.1 CFSR
Climate Forecast System Reanalysis (CFSR) with an approximately 38 Km 2 grids was
selected as input for daily minimum and maximum temperatures. Grids were selected
to cover the area in and around the watershed to obtain a vast network of virtual
weather stations. Texas Agricultural and Meteorological University (TAMU) offers an
interface (https://globalweather.tamu.edu) for the selection of said grid and generates
SWAT input files using the CFSR database. A total of 23 points were chosen covering
the catchment area, shown in Table 3.2.4.1.
26
Table 3.2.4.1 Location of virtual Temperature weather station
Point Latitude
Longitude
Elevation
Point
Latitude
Longitude
Elevation
1
34.501
71.25
694
13
35.126
71.875
1487
2
34.813
71.25
2124
14
35.438
71.875
4118
3
35.126
71.25
3012
15
35.75
71.875
4614
4
35.438
71.25
3050
16
34.501
72.188
1133
5
35.75
71.25
3820
17
34.813
72.188
1618
6
34.501
71.563
935
18
35.126
72.188
2729
7
34.813
71.563
862
19
35.438
72.188
2231
8
35.126
71.563
2484
20
35.75
72.188
5125
9
35.438
71.563
2447
21
34.501
72.5
732
10
35.75
71.563
4728
22
34.813
72.5
1350
11
34.501
71.875
460
23
35.126
72.5
1693
12
34.813
71.875
1190
3.2.4.2 Precipitation
Multiple available datasets were studied to select the most appropriate one for the
purpose of this study, which include the following:
1. CFSR
2. PERSIANN
3. TRMM
4. APHRODITE
These datasets were chosen due to their reported performance (Thom, 2017) and
availability. Especially TRMM and APHRODITE for Asian regions (Lutz, 2015). The
datasets have different global grid positions and sizes so were extracted independently.
27
Precipitation
All the datasets used for precipitation were in grid format, whereas SWAT requires its
input as point data in text format, and therefore the supplementary steps were
implemented to generate SWAT input files for precipitation. Almost all the
precipitation datasets are in .netcdf format and extraction is required at defined points.
This was done by firstly identifying which grids on dataset cover the study area. Then
an imaginary weather station is established at the center holding the value provided for
that grid. Finally the data is extracted for the points using ArcGIS and then converting
the output time series into text files for SWAT input.
3.2.4.2.1 PERSIANN
Precipitation Estimation from Remotely Sensed Information using Artificial Neural
Networks (PERSIANN) provides daily precipitation values from 60S to 60N latitudes
at 0.25 Deg grids. Downloaded from https://catalog.data.gov, grids covering the study
area were identified. For time period (2001-2010), values were extracted and then
processed to be used for SWAT analysis. Seven virtual climate stations were
established, with their position mentioned in Table 3.2.4.2.1.
28
Table 3.2.4.2.1 Positions of PERSIANN weather stations
Point
Latitude
Longitude
Elevation
Point
Latitude Longitude
Elevation
(m)
(m)
1
35.5
72
3723
5
35.5
71.75
1608
2
35
71.75
2216
6
35.25
71.75
3782
3
35.75
72
2529
7
35.25
72
1570
4
35.25
71.5
1779
3.2.4.2.2 TRMM
Tropical Rainfall Measurement Mission (TRMM) provides precipitation at 3 hourly
and daily time steps at 0.25 deg. spatial resolution. The daily dataset was selected as 3
hourly requires large computation time and power. Data was available up to 2012 so a
slot of 2001-2012 was selected for this study. Four virtual weather stations were
established, as below;
Table 3.2.4.2.2 Positions of TRMM weather stations
Point
Latitude
Longitude
Elevation (m)
1
35.125
71.625
998
2
34.625
72.125
863
3
35.125
71.625
2484
4
35.125
72.125
2394
29
3.2.4.2.3 APHRODITE
Asian Precipitation - Highly-Resolved Observational Data Integration Towards
Evaluation (APHRODITE) is a daily precipitation dataset. Which uses not only
satellite data but also corrects it using ground station observations, therefore
comparison between performance of TRMM and APHRODITE concluded that the
latter is the more dependable dataset (Duncan J. M. A., 2012). This dataset was
selected to carry out this study due to the following reasons;

Longest time period available for comparison.

Best results when comparing output flows with observed flows, using statistical
analysis.
A total of nine virtual weather stations covering the watershed were established for this
study.
Table 3.2.4.2.3 Positions of APHRODITE weather stations
Point
Latitude
Longitude
Elevation
Point
Latitude
Longitude
(m)
Elevation
(m)
1
35.125
71.625
2484
6
35.375
71.125
2229
2
35.625
71.875
1526
7
35.125
71.125
2394
3
35.375
71.875
3944
8
34.875
71.125
2177
4
35.125
71.875
1549
9
35.625
71.375
3943
5
35.625
71.125
4287
30
3.2.5 RCPs Climate Dataset
Himalayan Adaptation, Water and Resilience (HI–AWARE) project offers reference
climate dataset (i.e. daily precipitation and mean air temperature) for the Indus,
Ganges and Brahmaputra (IGB) River Basins. Lutz et al. (2016) scrutinized eight (8)
GCM
runs
[inmcm4_r1i1p1,
CanESM2_r3i1p1
(RCP8.5);
CMCC–CMS_r1i1p1,
BNU–ESM_r1i1p1,
bcc–csm1–1_r1i1p1,
inmcm4_r1i1p1,
CMCC–
CMS_r1i1p1, CSIRO–Mk3–6–0_r4i1p1 (RCP4.5)] from 163 GCM runs obtained from
Coupled Model Intercomparison Project Phase 5 (CMIP5), for the IGB on the basis of
extreme projections. The datasets downscaled on the basis of Representative
Concentration Pathways (RCPs) under HI–AWARE project, were obtained to study
the projected changes in hydrological conditions for study area. The projected
precipitation and temperature dataset for aforementioned General Circulation Models
(GCMs) downscaled at 5×5 km grid size were obtained from HI–AWARE project.
Further, detailed description of the aforementioned dataset used in current study is
given by Lutz et al.(2016).
GCMS
Table 3.2.5 Description of GCMs
RCP 4.5
RCP 8.5
BNU–ESM_r1i1p1

cold, wet
inmcm4_r1i1p1

cold, dry
CMCC–CMS_r1i1p1

warm, wet
CSIRO–Mk3–6–0_r4i1p1

warm, wet
inmcm4_r1i1p1

cold, dry
CMCC–CMS_r1i1p1

warm, dry
bcc–csm1–1_r1i1p1

cold, wet
CanESM2_r3i1p1

warn, wet
31
The same points established for daily minimum and maximum temperature with CFSR
and for daily precipitation with APHRODITE were selected to extract projected data
from the GCMs. This consideration is crucial as GCMs require bias correction. This
was carried out for all the individual points. This step is further explained in chapter
3.3.3.1.
32
3.3 Methods
Figure 3.3 Schematic Diagram of Methodology
3.3.1 Preprocessing
3.3.1.1 Temperature
Global Tmin (daily minimum temperature) and Tmax (daily maximum temperature)
data is made available by TAMU. It uses CFSE database to extract gridded data for an
area specified by user.
3.3.1.2 Precipitation
Generating precipitation inputs was the biggest hurdle in this study. Some datasets
were not easily available and even after availability, required a cumbersome amount of
preprocessing, which included;

Identifying the centers of the available grids to establish virtual weather station.

Extracting data at those points from a large number of netcdf files.

Converting the extracted data into SWAT input formats.
33
3.3.2 Processing
CFSR was selected for temperature and APHRODITE was selected for precipitation,
because of their results under statistical analysis. The time period available was
divided in to two parts, for calibration and validation. Years 2006-2009 were selected
as calibration period and 2010-2012 were selected for validation period. With the data
correctly converted into SWAT input, the model was run to calculate flows for the
calibrated period. These results were then corrected using a semiautomatic tool
SWAT-CUP and manually. During calibration the most sensitive parameters were
identified and then calibrated manually to achieve results closet to observed values.
These were then checked using statistical analysis methods of;

R square

Nash Sutcliff

Root Mean Square Error
Once these tools showed satisfactory results, the corrected parameters were then used
for the validation period.
3.3.3 Future Assessment
3.3.3.1 Bias Correction of Global Climatic Dataset
IGB climate dataset, of eight GCMs models downscaled at 5km × 5km grid size for
Upper Indus basin was selected for future climate impact study. Detail methodology of
selection of eight GCMs is described by Arthur et al. (2016).
34
For the base period of 30 years (2081–2010), the IGB dataset (i.e. daily precipitation
and temperature) was extracted, then the baseline (APHRODITE + CFSR) climatic
dataset (climate station data) were compared with the IGB climatic dataset to observe
uncertainties. Since, the large uncertainties were found in IGB climatic dataset in
comparison with base period, therefore bias correction of IGB gridded climatic dataset
was performed on daily time step using the delta technique to derive corrected baseline
(GCMs) climatic dataset for future decadal periods (2025s, 2055s, 2085s) climate.
This technique for the bias correction has been applied and discussed with detail by
several researchers (Teutschbein, 2012 and Burhan et al., 2015). The future decadal
climatic dataset were corrected by using correction factor driven from baseline
(APHRODITE and CFSR) and baseline (GCMs) dataset during base period.
3.3.4 Future streamflow projection
The corrected datasets of precipitation and daily minimum and maximum temperatures
for up to the year 2100 were then input in to SWAT to simulate and assess water
availability till the end of this century. For this purpose, decadal time steps were
chosen. So the whole time period (2011-2100), was divided in to three parts;
1. 2025s (2011-2040)
2. 2055s (2041-2700)
3. 2085s (2071-2100)
All the assessments were then carried out over these time slots over monthly and
seasonal scales.
35
Chapter 4
RESULTS AND DISCUSSIONS
4.1 Calibrated Parameters
SWAT calculates flow and other hydrological parameters over different parameters.
For this study SCS Curve number method and Hargreaves method was selected, which
work on 26 parameters to simulate flow. These parameters need to be calibrated to
refine the results and bring the results close to observed flow values. This step requires
first the identification of sensitive parameters and then changing their values
iteratively, checking the results simultaneously. A total of 600 iterations were
performed in two steps of 300 each.
Previous studies show that APHRODITE dataset leads best results in Asian regions,
therefore a time period of 2006-2009 was selected and calibration period for this study.
This time period was selected because it is common in all of the datasets, consequently
making it easier to compare them. A total of 600 iterations were performed for this
study, using the semi-automated calibration and sensitivity analysis tool, SWAT CUP.
36
Table 4.1 Parameters and their values for SWAT
Parameter
Description
Range
Minimum
CN2
SCS Runoff Curve
Fitted Value
Maximum
±20% the default value
36.13
10
30
26.56
1700
2000
1869.5
Number
GW_ Delay
Ground water
delay(days)
GWQMN
Thresh hold depth of
water in shallow aquifer
for return flow to
occur(mm)
APLHABF
Baseflow Alpha factor
0
1
0.07
RCHRG_DP
Deep aquifer percolation
0
0.5
0.004167
200
500
233.5
0.5
1
0.609
factor
REVAPMN
Thresh hold depth of
water in shallow aquifer
for return “revap” to
occur. (mm)
EPCO
Plant uptake
compensation factor
CN2,GW_DELAY,GWQMN were most sensitive
The fitted values then input into SWAT using manual calibration and all the
precipitation datasets were input to simulate flows simultaneously. The time periods
used for TRMM, PERSIANN and APHRODITE were Seven years (2006-2012), Five
years (2006-2010), and Seven years (2006-2012) respectively. Simulated flows from
these datasets were evaluated via different statistical techniques mentioned earlier in
the document.
37
PERSIANN simulated the poorest results with R square of 0.426 and NS of -0.25,
TRMM was second with R square of 0.5 and NS of 0.25, and APHRODITE was the
best with R square of 0.7 and NS of 0.61. As APHRODITE provided with the most
acceptable results, it was the chosen for further climate change assessments.
38
4.2 Simulated Stream Flows
4.2.1 TRMM
Tropical Rainfall Measuring Mission (TRMM) precipitation data was available up to
2012. It was extracted and run for seven years excluding the five year warm up period
(Fig 4.2). Efficiency checks of R square and NS came out to be 0.5 and 0.25
respectively.
Figure 4.2 Simulation via TRMM
Although TRMM lead to good results, it failed to map the peaks accurately. The peaks
in did map are temporally offset to some duration and almost failed completely to
simulate base flow values. This may be because it relies completely on satellite data as
compared to APHRODITE which uses observed weather station data to correct itself.
39
4.2.2 PERSIANN
Precipitation Estimation from Remotely Sensed Information using Artificial Neural
Networks (PERSIANN) dataset for precipitation is available up to 2010, and was run
for five years excluding a five year warm up period (Fig 4.3). Efficiency checks of R
square and NS came out to be 0.42 and -0.25 respectively.
Fig 4.3 Simulation via PERSIANN
PERSIANN produced the worst results for this study, and this was not surprising as no
study suggests good results from PERSIANN for Asian regions. It not only failed to
simulate peak flows but didn’t even struggle towards the base flow values.
40
4.3 APHRODITE
Asian Precipitation - Highly-Resolved Observational Data Integration Towards
Evaluation (APHRODITE) data is available up to 2015. Its time period was divided
into two parts, calibration and validation, 2006-2009 and 2010-2012 respectively (Fig
4.4). R square, NS and RMSE came out to be 0.70, 0.58, 40.61 for calibration and
0.71, 0.67, 33.42 for validation respectively.
Fig 4.4 Simulation via APHRODITE
APHRODITE showed the best results among selected precipitation datasets (Table
4.5), therefore it, with CFSR for daily minimum and maximum temperatures, was used
for BIAS Correction of future datasets to conduct future water availability assessment.
Another reason for this selection was a 30 year base period available for both of them
to aid in bias correction step.
41
Table 4.5 Efficiency of Precipitation datasets
Parameter
Time period
R2
N.S.
APHRODITE
Calibration period
2006-2009
0.70
0.58
APHRODITE
Validation period
2010-2012
0.71
0.67
TRMM
Persiann
2006-2012
0.5
0.25
2006-2010
0.42
-0.25
4.4 Bias Correction
GCMs data need to be corrected to remove any bias. A base period of 30 years (19812010) was used for bias correction using delta technique, and the correction factors
were then incorporated to calculate future values. A comparison between corrected and
uncorrected data is shown in tables (4.6a, 4.6b and 4.6c)
Table 4.6a Bias correction of Maximum Monthly Temperature
Tmax
Uncorrected
Corrected
Time Period
Degree
Celsius
2025s
2055s
2085s
Legend
42
Table 4.6b Bias correction of Minimum Monthly Temperature
Tmin
Uncorrected
Corrected
Time Period
Degree
Celsius
2025s
2055s
2085s
Legend
Table 4.6c Bias correction of Monthly precipitation
Corrected
Precipitation Uncorrected
millimeters
Time Period
2025s
2055s
2085s
Legend
43
4.5 Maximum temp
The findings of this research show an expected increase in the daily maximum
temperature (Fig 4.7.1-3). This is probably because as concentration of CO2 increases,
the increasing greenhouse effect leads to an increase in temperature.
Results have been compiled to show the difference of RCP 4.5 and 8.5 independently
in comparison to base period.
Average Daily Maximum Temperature
Time
Period
Degree Celsius
Tmax
2025s
2055s
2085s
Legend
Figure 4.7.1 Monthly Maximum Temperature in Degree Celsius
44
Figure 4.7.2 Monthly Average Maximum Temperature
For 2025s, highest increase in daily maximum temperature is forecasted in autumn
season (2°C) and lowest in winter (0.12°C) with RCP 4.5. However for 2055s and
2085s, highest increase is in spring season (3.21°C and 5.52°C respectively with RCP
8.5) and lowest in winter (1.13°C and 2.2°C respectively with RCP 4.5). Tables’
4.7.4&5 show the absolute values of forecasted daily maximum temperatures for the
coming decades.
Figure 4.7.3 Seasonal Average Maximum Temperature
45
Table 4.7.4 Monthly Maximum Temperature in Degree Celsius
Month
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Base
Period
3.449
4.660
9.313
15.637
21.858
26.716
27.979
26.950
23.778
17.187
11.180
6.046
Maximum Temperature
2025s
2055s
4.5s
8.5s
4.5s
8.5s
3.467 3.661 4.617 5.798
4.163 5.062 5.204 7.210
9.898 10.571 10.835 12.946
16.154 17.226 17.068 19.193
21.935 23.114 22.858 25.029
27.000 27.394 28.471 29.637
28.019 27.827 29.445 30.020
26.929 26.550 28.165 28.118
24.419 23.477 25.579 25.118
19.512 18.237 20.443 19.777
12.870 11.794 13.816 13.308
6.426 6.233 7.346 7.896
2085s
4.5s
8.5s
5.487 7.934
6.517 9.675
11.769 15.110
18.072 21.229
24.015 27.252
29.420 32.385
30.466 32.873
28.644 30.525
26.206 27.301
21.026 21.839
14.658 15.663
8.487 10.060
Table 4.7.5 Seasonal Maximum Temperature in Degree Celsius
Month
Base
Period
Winter 5.867
Spring 21.403
Summer 26.236
Autumn 14.184
Maximum Temperature
2025s
2055s
4.5s
8.5s
4.5s
8.5s
5.988
6.382
7.001
8.463
21.696
22.578 22.799 24.619
26.456
25.951 27.729 27.752
16.191
15.015 17.130 16.543
2085s
4.5s
8.5s
8.065 10.695
23.836 26.955
28.439 30.233
17.842 18.751
46
4.6 Minimum temp
Similar to daily maximum temperatures, daily minimum are also observed to be
increasing, with the highest percentage increase in winters (Fig 4.8.1). For the same
reasons mentioned for Tmax, Tmin is also projected to increase towards the end of 21 st
century.
Results have been compiled to show the difference of RCP 4.5 and 8.5 independently
in comparison to base period.
Average Daily Minimum Temperature
Time
Period
Degree Celsius
Tmax
2025s
2055s
2085s
Legend
Figure 4.8.1 Monthly Minimum Temperature in Degree Celsius
47
Figure 4.8.2 Monthly Average Maximum Temperature
For 2025s, highest increase in daily minimum temperature is forecasted in summer
season (1.9°C) and lowest in spring (0.05°C) with RCP 4.5. For 2055s and 2085s,
highest increase is in summer season (3.32°C and 5.38°C respectively with RCP 8.5)
and lowest in spring (0.76°C and 1.38°C respectively with RCP 4.5). Tables’ 4.8.2&3
show the absolute values of forecasted daily minimum temperatures for the coming
decades.
Figure 4.8.3 Seasonal Average Maximum Temperature
48
Table 4.8.2 Monthly Minimum Temperature in Degree Celsius
Month
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Base
Period
-5.742
-4.666
-0.580
4.503
9.559
13.567
14.767
13.299
10.296
5.717
1.169
-3.111
Minimum Temperature
2025s
2055s
2085s
Avg
Avg
Avg
Avg
Avg
Avg
4.5s
8.5s
4.5s
8.5s
4.5s
8.5s
-4.878 -4.545 -4.198 -3.226 -3.610 -1.505
-4.125 -3.207 -3.228 -1.747 -2.517 -0.415
0.908
1.412
1.254
2.757
1.698
4.505
5.107
5.894
5.769
7.614
6.209
9.407
9.448 10.383 10.045 11.836 10.729 13.723
13.214 14.048 14.106 15.648 14.839 17.695
16.105 16.578 16.995 18.020 17.666 19.949
15.959 15.656 16.871 17.216 17.360 19.069
12.065 11.385 13.112 13.092 13.655 15.472
6.260
5.699
7.161
7.270
7.255
9.641
1.367
0.986
2.067
2.320
2.336
4.292
-2.397 -2.403 -1.817 -1.052 -0.752
0.671
Table 4.8.3 Seasonal Minimum Temperature in Degree Celsius
Month
Winter
Spring
Summer
Autumn
Base
Period
-3.525
9.210
12.787
3.443
Minimum Temperature
2025s
2055s
2085s
Avg
Avg
Avg
Avg
Avg
Avg
4.5s
8.5s
4.5s
8.5s
4.5s
8.5s
-2.623 -2.186 -1.997 -0.817 -1.295
0.814
9.256 10.108
9.973 11.699 10.592 13.608
14.710 14.540 15.659 16.109 16.227 18.163
3.814
3.343
4.614
4.795
4.796
6.967
49
4.7 Precipitation
This study forecasts that precipitation will decrease by the end of this century (Fig
4.9.1). Climate change is expected to change the precipitation patterns. 2025s are
projected to have a decrease of 31.7 mm in summers and increase of 22.38 mm in
winters. Similarly for 2055s and 2085s summer precipitation show a decrease of 38.66
mm and 41.15 mm respectively. Winters show an increase of 8.32 mm and 10.72 mm
respectively.
Tables’ 4.9.3&4 show the absolute values of projected precipitation for the coming
decades.
Precipitation
Time
Period
Precipitation (mm)
2025s
2055s
2085s
Legend
Fig 4.9.1 Monthly Precipitation in millimeters
50
Figure 4.9.2 Monthly Precipitation in millimeters
Figure 4.9.3 Seasonal Precipitation in millimeters
51
Table 4.9.3 Monthly Precipitation
Month
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Base
Period
76.7
145.9
150.7
127.2
108.9
68.7
136.1
114.9
66.9
41.8
55.4
58.0
Precipitation in millimeters
2025s
2055s
4.5s
8.5s
4.5s
8.5s
104.4
131.8
200.0
135.4
76.8
44.4
98.4
71.4
61.8
65.0
51.4
79.7
95.4
151.6
194.1
124.0
62.8
45.4
97.9
77.5
47.4
69.7
56.1
79.7
102.6
138.0
146.1
111.3
55.9
51.9
89.9
66.2
45.8
62.7
64.6
71.4
98.7
147.4
143.9
108.0
50.5
57.4
91.2
69.0
43.0
64.7
77.8
74.6
2085s
4.5s
8.5s
111.5
139.6
128.1
93.7
44.6
61.3
73.7
60.6
60.2
59.6
65.2
80.9
111.0
155.1
123.4
81.1
49.3
76.7
86.6
58.8
68.6
80.3
75.4
84.7
Table 4.9.4 Seasonal Precipitation
Month
Winter
Spring
Summer
Autumn
Base
Period
107.8
101.6
106.0
48.6
Precipitation in millimeters
2025s
2055s
4.5s
8.5s
4.5s
8.5s
129.0
130.2
114.5
116.1
85.5
77.4
73.0
72.0
77.2
74.3
67.3
67.7
58.2
62.9
63.6
71.3
2085s
4.5s
115.0
66.5
64.8
62.4
8.5s
118.5
69.0
71.3
77.8
52
4.8 Snowmelt
The study area has high altitudes and winter temperatures below zero. With most of its
precipitation in winters, most likely due to Western Disturbances, a lot of precipitation
in winters is in the form of snowfall. When the temperatures rises as the season
changes, or during the day, this snow melts and contributes to runoff, therefore it is
essential to incorporate it in analysis. SWAT provides the amount of snow/ice melting
in the time period in millimeters, and as the time step for this study was monthly, so
the results are millimeters of snowmelt in a month.
Figure 4.10.1 Monthly Snowmelt in millimeters
53
Figure 4.10.2 Seasonal Snowmelt in millimeters
There is no snowmelt in the summers during base period and future. A trend worth
mentioning is the spring increase in snowmelt during 2025s but huge decrease in
2055s and 2085s. Winters for all the decadal periods are expected to an increased
snowmelt, due to the increasing precipitation and temperatures. There is not much
difference in the snowmelt of autumn season and the values stay low through. (Table
4.10.2-3) Show the monthly and seasonal difference in snowmelt of each RCP from
base period.
54
Table 4.10.3 Difference in Monthly Snowmelt (mm)
Month
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Difference in Monthly Snowmelt
2025s
2055s
4.5s
8.5s
4.5s
8.5s
4.5s
8.5s
-10.2
-25.5
41.4
26.0
-1.2
-2.4
0.0
0.0
0.0
0.7
-4.3
-7.9
-3.0
14.6
16.6
-64.5
-13.9
-2.4
0.0
0.0
0.0
2.5
-0.5
-6.8
8.0
8.3
-25.7
-79.8
-14.8
-2.4
0.0
0.0
0.0
0.8
-0.9
-0.2
-11.9
-26.0
40.4
33.2
-0.8
-2.4
0.0
0.0
0.0
1.7
-2.6
-6.6
-8.8
-8.7
49.1
-39.4
-14.0
-2.4
0.1
0.0
0.0
0.8
-2.4
-6.5
-4.3
-1.9
37.9
-60.6
-13.1
-2.4
0.0
0.0
0.0
1.5
-0.4
-4.6
2085s
Table 4.10.4 Difference in Seasonal Snowmelt (mm)
Season
Winter
Spring
Summer
Autumn
Difference in Seasonal Snowmelt
2025s
2055s
4.5s
8.5s
4.5s
8.5s
4.5s
8.5s
-0.6
7.5
0.0
-1.8
5.4
-26.9
0.0
1.0
-2.4
-32.4
0.0
-0.1
-1.0
10.0
0.0
-0.5
6.3
-18.6
0.0
-0.8
6.8
-25.4
0.0
0.5
2085s
55
4.9 Flows
As the observed and simulated flows from APHRODITE were not identical, the future
simulations were compared to both of them. Both of them show a general decrease in
flow in observed till the end of this century (Fig 4.7.1). It is also observed that winter
and autumn flows will increase whereas the flow for the rest of the year will decrease
(Fig 4.7.4). Although the winter increase by greater percentage compared to the
percentage decrease in summer, it is worth noting that the absolute decrease in summer
is quite more than the winter increase, therefore yearly flows will decrease by about
14.13% at least and 22.65% at most by the 2085s. Tables 4.7.7-12 show the absolute
and percentage change in flows at monthly and seasonal time steps.
Another observation of this study is that comparison with both observed and
APHROITE simulated flows show a shift in the occurring of the peak flow. For the
base period, peak flow is observed in April but by it end of this century, it has shifted
to March (Fig 4.7.3).
Results have been compiled to show the difference of RCP 4.5 and 8.5 independently
in comparison to base period.
56
Flows
Time
Period
Flows in Cumecs
2025s
2055s
2085s
Legend
Figure 4.11.1 Monthly Flows of GCMs
57
Figure 4.11.2 Average Monthly flows
Figure 4.11.3 Average Seasonal Flows
58
Monthly Percentage Difference
Percentage difference of Monthly Flows
Time Period
2025s
2055s
2085s
Legend
Figure 4.11.4 Percentage Change in Monthly Flows
Percentage difference of Seasonal Flows
Seasonal Percentage Difference
Time Period
2025s
2055s
2085s
Legend
Fig 4.11.5 Percentage Difference of Seasonal flows
59
Table 4.11.6 Table of Monthly Flows
Flow in Cumecs
Month
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Base Period
16.3
44.1
135.3
217.3
176.6
129.3
100.1
83.7
56.6
46.2
27.1
19.8
2025s
2055s
2085s
4.5s
8.5s
4.5s
8.5s
4.5s
8.5s
24.7
58.5
214.9
303.7
172.7
105.6
89.2
69.3
64.8
54.9
31.2
26.2
25.6
67.2
208.5
299.8
166.4
98.9
85.6
71.7
54.9
51.7
31.6
26.9
27.8
83.9
226.2
221.2
144.8
93.8
82.4
64.7
52.4
48.5
31.4
24.6
29.0
101.6
238.2
198.9
137.5
87.7
79.7
67.4
51.0
51.4
40.1
29.6
36.6
129.9
219.1
183.0
125.4
81.4
64.2
53.3
56.3
44.8
31.6
26.6
50.2
171.5
207.4
156.7
118.4
80.2
71.1
58.6
61.7
62.1
51.0
40.9
Table 4.11.7 Table of Seasonal Flows
Flow in Cumecs
Month
Winter
Spring
Summer
Autumn
Base Period
53.9
174.4
80.1
36.6
2025s
2055s
2085s
4.5s
8.5s
4.5s
8.5s
4.5s
8.5s
81.1
194.0
74.5
43.0
82.0
188.3
70.7
41.7
90.6
153.3
66.5
39.9
99.6
141.4
66.0
45.8
103.0
129.9
58.0
38.2
117.5
118.4
63.8
56.5
60
Table 4.11.8 Table of Deviance of Monthly Flows from Base Period
Month
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
2025s
4.5s
8.5s
8.4
9.2
14.4
Flow in Cumecs
2055s
4.5s
2085s
8.5s
4.5s
8.5s
11.5
12.6
20.3
33.9
23.1
39.8
57.5
85.8
127.4
79.6
73.3
90.9
102.9
83.8
72.1
86.4
82.5
3.9
-18.4
-34.2
-60.5
-3.8
-10.2
-31.8
-39.0
-51.1
-58.2
-23.6
-30.4
-35.5
-41.6
-47.9
-49.1
-10.9
-14.5
-17.7
-20.4
-35.8
-29.0
-14.3
-11.9
-19.0
-16.2
-30.3
-25.1
8.2
-1.8
-4.3
-5.7
-0.3
5.1
8.7
5.5
2.3
5.2
-1.4
15.9
4.1
4.5
4.3
13.0
4.5
23.9
6.4
7.1
4.9
9.8
6.8
21.1
Table 4.11.9 Table of Deviance of Seasonal Flows from Base Period
Flow in Cumecs
Month
2025s
2055s
2085s
4.5s
8.5s
4.5s
8.5s
4.5s
8.5s
Winter
27.2
28.2
36.8
45.7
49.2
63.6
Spring
19.6
14.0
-21.1
-33.0
-44.4
-55.9
Summer
-5.7
-9.4
-13.6
-14.1
-22.2
-16.3
Autumn
6.4
5.0
3.3
9.1
1.5
19.9
61
4.10 Discussion
This research was carried out in two parts, first hydrological assessment of Panjkora
River at Koto site and second was to project stream slows under changing climate. A
semi-distributed watershed model was selected to simulate the hydrology. The model
(SWAT) was calibrated for the years 2006-2009 and validated for 2010-2012,
resulting in R square of 0.71 and NS of 0.67 for the latter part. It was observed that the
model was quite dependable but unable to accurately simulate the peaks. This must be
noted that as there are no weather stations in the area, this research resolved to the use
of Global Gridded datasets for precipitation and daily minimum and maximum
temperatures. So the results of the model are highly dependent upon the accuracy of
these climate datasets.
For the second part of this research, HI-AWARE climate change datasets (CMIP5) for
RCP 4.5 and 8.5 were selected. These datasets were corrected for any biasedness and
then input to study water availability till the end of current century (2100).
An overall increase in maximum daily temperatures (Tmax) is observed in the future,
with the maximum increase of 2.01 °C in the autumn of 2025s for RCP 4.5, second to
an increase of 1.18 °C for spring and 0.51 °C for the winter of the same time period
with RCP 8.5. Moreover for 2055s, spring season is expected to experience the
maximum increase of 3.22 °C with RCP8.5. Autumn season also had a substantial
increase of 2.95 °C with RCP 4.5 pushing winter season to third place with an increase
of 2.59 °C with RCP 8.5. Towards the end of this century, 2085s had similar case, with
an increase of 5.56 °C, 4.82 °C, and 4.57 °C for spring, winter and autumn season
62
respectively, all for RCP 8.5. Daily minimum temperatures (Tmin) have a similar
trend, with a general increase for the coming years. 2025s experience their highest
increase in the summers with an increase of 1.92 °C with RCP 4.5. Winter and spring
have an increase of 1.37 °C and 0.9 °C respectively with RCP 8.5. For 2055s, summer
again has the highest increase with 3.32 °C, 2.7 °C for winter, and 2.49 °C for spring,
all with RCP 8.5. For the last part of this century, with an increase of 5.37 °C, summer
again has the highest increase. Spring and winter have an increase of 4.4 °C and 4.33
°C respectively, all with RCP 8.5.
One of the impacts expected of climate change is the change in precipitation patterns
(IPCC, 2014). Analysis of future precipitation in the study area showed similar results.
Although the annual precipitation is expected to decrease, and shift in pattern is
observed. Currently the area experiences its highest precipitation in winter season with
a monthly average of 107.83 mm. Also winter and autumn precipitation is expected to
increase and a decline is observed rest of the year. In the near future i.e. 2025s, highest
decrease will be in summer (31.70 mm) with RCP 8.5 and an increase of 22.38 mm in
the winters. 2055s won’t be much different with a decrease of 38.66 mm in summer
with RCP4.5 and increase of 22.65 mm in autumn with RCP 8.5. And by the end of
21st century, a decrease of 41.14 mm is expected in summer with RCP4.5 and a
autumn increase of 29.23 mm with RCP 8.5.
The winters of 2025s will experience the highest increase of flows (28.17 Cumecs)
with RCP 8.5 while the same reflects a decrease in summers as 9.41 Cumecs. During
the 2055s, winter flows are expected to rise by 45.72 Cumecs in comparison to a
63
summer and spring decrease of 14.11 Cumecs and 33.01 Cumecs respectively (RCP
8.5). 2085s reflect a similar scenario of flows increasing up to 63.62 Cumecs for
winters and a decrease of 55.93 Cumecs in spring (RCP 8.5).
64
Chapter 5
CONCLUSIONS AND RECOMMENDATIONS
5.1 Conclusion
SWAT, a semi-distributed model, was use to simulate the hydrology. SWAT works on
SCS curve number method to calculate flows and Hargreaves method for finding
losses. APHRODITE was used for precipitation and CFSR for daily minimum and
maximum temperatures. R square and NS came out to be 0.698 and 0.576 for
calibration period of 2006-2009 and 0.707 and 0.668 for validation period of 20102012. Calibrated model was then used for future water availability under changing
climate. The time period from 2011-2100 was divided in three 30 year periods to
assess climatic changes. Precipitation patterns are expected to change with changing
climate, this study concludes the same as an increase in winter and autumn seasons and
a decrease in summer and spring seasons is forecasted by this study. Snowmelt is
expected to increase for the coming winters as the temperatures are rising. An increase
in winter and autumn season flows and a decrease for summer and spring seasons is
projected. A shift in peak flows is also expected at monthly scale. Currently the area
experiences its peak flow in April which will be shifted in March. Although at yearly
scale, the amount of flow does not change, their patterns indeed do, therefore this
study concludes that climate change will have its impacts on study area and SWAT
hydrological model can be used to simulate and forecast them.
65
5.2 Recommendations
A fully distributed model should be applied to simulate hydrology, which would be
much more efficient as its capability to account spatial variability will be much more
accurate. Local land use, soil and slope datasets should be developed and used for
future studies. There is no Automatic Weather Station (AWS) in the area; therefore a
vast array of AWS should be applied to the area to reduce dependence on global
weather datasets leading to modeling with much truer inputs. As this study predicts a
change in flow and precipitation patterns for the area, policy makers are recommended
to conduct studies towards adaptation techniques.
66
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