Hydrological Investigation of Swat River Basin

Hydrological Investigation of Swat River Basin

Using GIS, Remote Sensing and

Snowmelt Runoff Modeling

Zakir Hussain Dahri

Thesis Submitted for the Degree of

Master of Applied Science

(Geographic Information Systems)

Department of Geomatics

Faculty of Engineering

The University of Melbourne April, 2008

DECLARATION

This is to certify that (i) The thesis comprises only my original work, (ii) Due acknowledgement has been made in the text to all other material used, and (iii) The thesis is approximately 20,300 words in length exclusive of tables, maps, and references.

April, 2008

Zakir Hussain Dahri

i

DEDICATION

To my beloved mother to whom I am most inspired in my life and

my personality is largely her reflection

and

To my beloved late son who I lost at the age of less than

two and half years during the course of this study

ii

ABSTRACT

The snowcover and glaciers of HKH region of Pakistan are one of the largest repositories

of inland cryosphere outside Polar Regions and obviously the lifeline of Pakistani people.

However, reliable estimates of the snow area extent and snowmelt runoff have been

lacking in this largely inaccessible and data sparse region. The study utilized GIS, RS and

hydrological modeling techniques to evaluate the distribution of snowcover, estimated

snowmelt runoff and statistically related both these variables.

A very high variability of snowcover and associated snowmelt runoff during the entire

calendar year is observed. Snowfall usually starts abruptly in September and October

months but the following four main winter months (Nov – Feb) generally bring in most

of the snowfall and snowcover is increased from less than 2 % in August to about 64 %

by the end of January or in early February.

Snowmelt generally continues throughout the year but contribution of winter snowmelt

runoff is often very low. Unlike snowfall, snowmelt runoff usually progresses gradually

and smoothly and is more easily predictable. The summer snowmelt normally gets

momentum in March and increases linearly from around 30 – 60 m3/sec to 400 – 760

m3/sec in late June or early July. It declines gradually thereafter reducing to 30 - 50

m3/sec in December. The Dec – Feb snowmelt runoff normally tends to remain same.

The results reveal Swat river basin of Pakistan as predominantly a snow-fed as the annual

snowmelt runoff contribution to the total runoff may ranges from 65 – 75 %. The study

observes a definite response of observed river discharges and simulated snowmelt runoff

to seasonal snowcover changes, i.e. an association of low stream flows with high snow

area extent during the winter season (Sep – Feb), an increase in discharge associated with

a decrease of snow area extent during the early summer (Mar – Jun), and decrease in

discharge with decreasing snowcover in the late summer, monsoon season (Jul – mid

Sep). It employs the daily records of snowcover and relates them with the daily river

discharges and snowmelt runoff and also develops prediction model for the total runoff

volume of the four main summer months (May – Aug).

iii

ACKNOWLEDGEMENTS

I have tremendous appreciation and gratitude to my research advisor Dr. Joseph H.

Leach, Department of Geomatics, for his invaluable guidance, constructive ideas,

positive criticism and constant encouragement during the course of this research study.

I am highly indebted to thank AusAID for sponsoring me and also to my parent

department PARC for allowing me to avail this opportunity.

I am extremely grateful to my friend Faisal Masood Qureshi, PhD scholar at the

Department of Geomatics, for his valuable and constant help especially in GIS and RS

related issues and also for a wonderful company throughout my stay here in Melbourne.

Sincere thanks are also due to my friend and colleague Dr Bashir Ahmad for arranging

met and flow data and also for his invaluable guidance and support.

Due acknowledgement is extended to NASA’s NSIDC, WAPDA-Pakistan, and PMD for

free distribution of valuable data

The support offered by the Department of Geomatics at the University of Melbourne is

also duly acknowledged. The competence of faculty and friendliness of staff has truly

complemented this academic experience.

Finally, I owe my deepest gratitude to my parents, brothers, sisters and other close

relatives for their constant support and sacrifice. The role and courage of my wife is

unforgettable especially when we lost our beloved son during my stay here. She has been

splendidly brave, always ready for sacrifice and proved to be a devoted life partner.

iv

CONTENTS

DECLARATION I

DEDICATION II

ABSTRACT III

ACKNOWLEDGEMENTS IV

CONTENTS V

LIST OF ABBREVIATIONS VIII

LIST OF TABLES IX

LIST OF FIGURES X

1. INTRODUCTION 1

1.1 Background 1

1.2 Problem Statement and Justification 3

1.2 Study Objectives 5

2 LITERATURE REVIEW 6

2.1 General 6

2.2 Properties of Snow 6

2.3 Remote Sensing of Snow 7

2.4 Process of Snowmelt 9

2.5 Snowmelt Runoff Modeling 10

2.6 Related Research 13

3. DESCRIPTION OF THE STUDY AREA 17

3.1 Physiography 17

3.2 Landuse Pattern 19

3.3 Climatic Conditions 20

3.4 Hydrological Characteristics and Water Resources 22

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4. METHODOLOGY 25

4.1 Outline 25

4.2 The MODIS Instrument 25

4.2.1 MODIS Snow Mapping Algorithm 27

4.2.2 MODIS Snowcover Products 30

4.3 The Snowmelt Runoff Model 33

4.3.1 Governing Equation 34

4.3.2 Model Accuracy Assessment 35

4.4 Data Acquiring and Database Development 36

4.5 River Network and Watershed Delineation 39

4.6 Image Processing & Classification 40

4.7 Derivation of Model Input Parameters 41

4.7.1 Basin Boundary and Zone Areas 41

4.7.2 Temperature Lapse Rate and Degree Days 42

4.7.3 Precipitation 44

4.7.4 Snow Area Extent 45

4.7.5 Runoff Coefficients 46

4.7.6 Recession Coefficient 46

4.7.7 Rainfall Contribution Area and Time Lag 48

4.8 Model Calibration and Verification 48

4.9 Model Simulations 49

4.10 Model Development 49

5. RESULTS AND DISCUSSION 51

5.1 Outline 51

5.2 Parameter Estimation 51

5.3 Snowcover Estimation 57

5.4 Snowmelt Runoff Modeling 69

5.4.1 Calibration and Verification Results 69

5.4.2 Simulation Results 71

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5.5 Relationship of Snow Area Extent with River Discharge

and Snowmelt Runoff 79

6. CONCLUSIONS AND RECOMMENDATIONS 87

6.1 Conclusions 87

6.2 Limitations 88

6.3 Recommendations 89

REFERENCES 90

vii

LIST OF ABBREVIATIONS AMSR-E Advanced Microwave Scanning Radiometer a.s.l Above Mean Sea Level AVCS Advanced Vidicon Camera System AVHRR Advanced Very High Resolution Radiometer BCM Billion Cubic Meters CMG Climate Modeling Grid DAAC Distributed Active Archive Center DEM Digital Elevation Model DHVSM Distributed Hydrology–Vegetation–Soil Model EB Energy Balance EOS Earth Observation System ESSA Environmental Science Service Administration GIS Geographic Information System GPS Global Positioning System HKH Hindukush-Karakoram-Himalaya ICIMOD International Centre for Mountain Development LIDAR Light Detection and Ranging MODIS Moderate Resolution Imaging Spectroradiometer MCM Million Cubic Meters MAF Million Acre Feet NASA National NDSI Normalized Difference Snow Index NDVI Normalized Difference Vegetation Index NOAA National Oceanographic and Atmospheric Administration NSIDC National Snow and Ice Data Centre NWFP North West Frontier Province PARC Pakistan Agricultural Research Council PMD Pakistan Meteorological Department SAE Snow Area Extent SHE European Hydrological System SMMR Scanning Multi-channel Microwave Radiometer SR Scanning Radiometer SRM Snowmelt Runoff Model SRTM Shuttle Radar Topography Mission SSM/I Special Sensor Microwave/Imager TI Temperature Index UBC University of British Columbia USGS United States Geological Survey VHRR Very High Resolution Radiometer WAPDA Water and Power Development Authority

viii

LIST OF TABLES

Table 4.1 MODIS spectral bands and their primary uses…………………….. 27

Table 4.2 MODIS data product inputs to the MODIS snowmap algorithm….. 30

Table 4.3 Summary of the MODIS collection 5 snow data products………… 31

Table 4.4 Classes of the processed MOD10A2 dataset………………………. 33

Table 5.1 Area under permanent and temporary snow cover for three study

years………………………………………………………………. 68

Table 5.2 Year round simulation statistics for different study years…………. 71

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LIST OF FIGURES

Figure 1.1 Spatial distribution of average annual rainfall in Pakistan…………... 2

Figure 3.1 Location map of the study area in Pakistan………………………….. 18

Figure 3.2

3-D view of the true color Lanndsat-7 image draped over DEM of

the study area……………………………………………………… 19

Figure 3.3 Variability of average monthly temperature at the three places……... 21

Figure 3.4 Variability of average monthly precipitation at the three places……. 21

Figure 3.5 Average and at 60 % probability river discharges measured at

Chakdara………………………………………………………........... 22

Figure 3.6 Variability of average monthly discharge at Chakdara for the 1st half

of a year……………………………………………………………… 23

Figure 3.7 Variability of average monthly discharge at Chakdara for the 2nd half

of a year…………………………………………………………. 24

Figure 4.1 Conceptual model (flow chart) of the adopted methodological

approach……………………………………………………………... 26

Figure 4.2 Recession flow plot Qn vs Qn+1 for Swat river basin 47

Figure 5.1 Delineated river network and watershed area of the whole Swat

basin and study area…………………………………………………. 52

Figure 5.2 Elevation zones, their areas & mean hypsometric elevation………… 53

Figure 5.3 Area-elevation (hypsometric) curve of the upper Swat river basin…. 53

Figure 5.4 Average daily minimum and maximum temperature at Kalam……... 54

Figure 5.5 Meteorological stations used for computation of temperature lapse

rate…………………………………………………………………… 55

Figure 5.6 (a) Relationship between temperature and elevation for Jan and Feb

months………………………………………………………………... 55

Figure 5.6 (b) Relationship between temperature and elevation for Mar and Apr

months………………………………………………………………... 56

Figure 5.6 (c) Relationship between temperature and elevation for May and June

months………………………………………………………………... 56

x

Figure 5.6 (d) Relationship between temperature and elevation for Jul and Aug

months………………………………………………………………... 56

Figure 5.6 (e)

Relationship between temperature and elevation for Sep and Oct

months………………………………………………………………... 57

Figure 5.6 (f) Relationship between temperature and elevation for Nov and Dec

months………………………………………………………………... 57

Figure 5.7 (a) Temporal variation of snowcover in the upper Swat basin (Jan - Apr) 60

Figure 5.7 (b) Temporal variation of snowcover in the upper Swat basin (May-

Aug) ………………………………………………………………... 61

Figure 5.7 (c) Temporal variation of snowcover in the upper Swat basin (Sep-Dec). 62

Figure 5.8 (a) Temporal variation of snowcover in the Zone-A (686–1500 m a.s.l).. 63

Figure 5.8 (b)

Temporal variation of snowcover in the Zone-B (1501 – 2500 m

a.s.l)…………………………………………………………………... 63

Figure 5.8 (c) Temporal variation of snowcover in the Zone-C (2501 – 3500 m

a.s.l)…………………………………………………………………... 63

Figure 5.8 (d) Temporal variation of snowcover in the Zone-D (3501 – 4500 m

a.s.l)…………………………………………………………………... 64

Figure 5.8 (e) Temporal variation of snowcover in the Zone-E (4501 – 5808 m

a.s.l)…………………………………………………………………... 64

Figure 5.8 (f) Temporal variation of snowcover in the whole basin………………... 64

Figure 5.9 Comparison of snowcover variation in different years………………. 65

Figure 5.10 Permanent and temporary/seasonal snow cover……………………... 67

Figure 5.11 Glacier location and extent as identified by PARC & ICIMOD 2005. 68

Figure 5.12 Simulated and observed river flows for calibration year of 2003…… 70

Figure 5.13 Simulated and observed river flows for verification year of 2004…... 70

Figure 5.14 Simulated and observed river flows for verification year of 2002…... 71

Figure 5.15 Cumulative runoff components in various zones for the simulation

year 2004 (Red is initial snow, green is new snow, and blue is

contribution of rain)………………………………………………….. 73

Figure 5.16 Computed snowmelt and rainfall runoff components for the Year

2002…………………………………………………………………... 74

xi


2003…………………………………………………………………... 74


2004…………………………………………………………………... 75

Figure 5.19 Average contributions of the two runoff components to the total

runoff generated from the basin……………………………………… 77

Figure 5.20 Contribution of two runoff components to the total monthly runoff 77

Figure 5.21 Average monthly distribution of snowmelt runoff in Jan – Jun

months………………………………………………………………... 78

Figure 5.22 Average monthly distribution of snowmelt runoff in Jul – Dec

months………………………………………………………………... 78

Figure 5.23 Temporal distribution of average daily snow area extent, observed

river discharge and simulated snowmelt runoff……………………… 82

Figure 5.24 Relationship of average daily snowcover with average daily

simulated snowmelt runoff and average daily observed runoff for

March – June months………………………………………………… 83



July – August months………………………………………………... 84



September – February months……………………………………….. 85

Figure 5.27 Prediction model for estimating May – Aug runoff volume from the

snowcover estimated on May 1-8……………………………………. 86

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C H A P T E R O N E

I N T R O D U C T I O N

1.1 Background

The research community over the last several years has been putting momentous efforts

into studying the potential impacts of changing climate on water resources as water has

become a major limiting factor in most of the world’s agricultural development. As the

Earth’s population has been growing rapidly and more stress is put on the land to fulfill

the livelihood requirements of an ever-increasing population, one question remains

ambiguous that how hydrologic resources will be affected. The global climate change

models predict greatest changes at higher latitudes (Rees 2006) and higher altitudes of

northern hemisphere, which mostly accommodate earth’s cryosphere. It is therefore

imperative to monitor these regions to look for manifestation of global climate change.

Pakistan is predominantly a dry country of the warm temperate zone. Its climate is

transitional between that of Central Asia and the monsoonal lands of South Asia, and

varies considerably with latitude, altitude, aspect and localized relief. Temperatures may

reach as low as – 26ºC over the northern high mountains, and as high as 52ºC over the

south-eastern lowland arid plains. The mountainous and sub-mountainous areas of the

northeast can receive over 1700 mm of precipitation annually, in contrast to only 30 mm

in the arid plains of southwest Balochistan. In general, Pakistan is one of the world’s

most arid countries with an average annual rainfall of only 292 mm. More than three-

fourth of the country receives less than 250 mm of annual rainfall (Figure 1.1). About 70

% of the total rainfall occurs in the monsoon season (July – September), and is hardly

used by crops directly as at that time the crops are near to their harvesting stage.

Moreover, rainfall intensity of monsoon rains is generally higher resulting in greater

surface runoff and lower absorption by the soil. Consequently, the agriculture and in turn

population and economy of the country are heavily dependent on an average annual

influx of about 180 BCM (billion cubic meters) of river water mostly derived from

snowmelt in the Hindu Kush-Karakoram-Himalayan (HKH) region into the Indus river

1

system. The snow and glaciers of HKH region act as frozen reservoirs, capturing snow

and rain, holding the water and releasing it into the rivers which feed the lower Indus

plains. However, river inflows in summer are almost four times that of winter flows,

necessitating enormous resources and efforts to control flooding and store water. The

current per capita water storage capacity is only 150 m3 compared to over 5000 m3 in

USA and Australia and 2200 m3 in China (WB 2005).

Figure 1.1 Spatial distribution of average annual rainfall in Pakistan

The snowcover and glaciers of HKH region are the largest repository of inland

cryosphere outside Polar Regions. Significant portion of this snow and glacier cover is

temporary and seasonal in nature. Seasonal snow cover is formed by consecutive

snowfall during the snow-accumulation season and gradually disappears during the

subsequent snowmelt season. Temporary snow cover, which can also be specified as

short-lived snow, is formed by a snowstorm during the snowmelt season, and exists only

2

for a few hours or a few days. While permanent snow cover is retained for many years,

seasonal and temporary snow cover has major impact on region’s renewable fresh water

resources. However, water availability in this region—in terms of temporal as well as

spatial distribution—is expected to be highly vulnerable to anticipated climate changes

(Sing et al 1997). The Indus river flows are estimated to be worst affected as it may loose

about 27 % its total flows by 2050 (Arnel 1999). Hence, one of the most important and

recent thrusts in hydrological research in Pakistan is the monitoring of glacier and

snowcover changes and impact assessment of that variation on region’s water resources.

These changes are the result of natural processes as well as anthropogenic influences.

1.2 Problem Statement and Justification

Snow and glaciers are the frozen reservoirs of fresh water and cover a significant part of

many mountain chains on the globe. In Pakistan about 5218 glaciers covering an area of

15,040 sq. km were identified in the ten sub-basins of Indus River System – namely

Swat, Chitral, Gilgit, Hunza, Shigar, Shyok, Upper Indus, Shingo, Astor and Jhelum –

covering HKH region of Pakistan (PARC and ICIMOD 2005). These glaciers constitute

11.7 % of the total area of these basins and are an important source of fresh water in

Pakistan as 50 – 85 % of the country’s total flows come from melting snows and glaciers

of the this region (Tarar 1982; Hewitt 1985; PARC and ICIMOD 2005).

The major tributaries of the Indus River originate from the HKH region and have their

upper catchments in the high mountain snow covered areas and flow through steep

mountainous slopes. This factor and the perennial nature of these rivers provide excellent

conditions for the development of hydropower resources. Rainfall during the monsoon

season further adds to this potential. Snowmelt season in Pakistan generally coincides

with the monsoon rainfall thereby augmenting the surface runoff often bringing heavy

floods in the lower southern plain areas of the county resulting in substantial loses in

terms of property and lives. As such the Government of Pakistan is undertaking massive

program of hydropower projects under the Water Vision-25 program to have greater

control over the available water resources and store water for next season and possibly

for dry years as well as provide cheap source of renewable energy. The planning of such

3

new multi-purpose projects on HKH rivers in Pakistan emphasizes the need for reliable

estimates of the snow extent and snow and glacier runoff because it provides a more

dependable and perennial flow. Despite their well recognized importance and potential,

little attempts have been made to assess in detail the contributions of snowmelt runoff in

these rivers, although a few studies, e.g. Tarar 1982, Hewitt 1985, DeScally 1994, PARC

and ICIMOD 2005, etc provide some insight in to the important aspects.

Reliable predictions of snowmelt runoff generally require comprehensive snow surveys.

Such surveys on a large scale are almost impossible for highly rugged and mostly

inaccessible mountain topography of HKH region and for a resource poor country like

Pakistan. Also, the ground data collection methods cannot provide either the desired areal

coverage (due to large areal extent or access problem) or observational frequency. There

are also procedural errors in point measurements and their extrapolation to large basins

(Tarar 1982). Moreover, ground methods of snow surveys are often expensive, time

consuming and difficult. Hence, due to lack of field data, unreliable and, often, late

prediction of water availability usually results in ill planning and management of precious

fresh water resources. Early prediction of snowcover extent and expected snowmelt

runoff allows efficient planning and management of water resources for hydropower

generation, regulation of discharge through reservoirs for flood control, and for irrigation,

industrial and domestic water-supply. In the HKH river basins, which for the most parts

are inaccessible due to extreme climate and highly rugged terrain and where snow cover

data from conventional methods are either nonexistent or are very limited, satellite

remote sensed observations provide the attractive and perhaps the only viable alternative

for acquiring snow cover data necessary for hydrologic forecasting of snowmelt runoff.

Recent advances in GIS, remote sensing and hydrological modeling techniques allow

their powerful integration. In the field of snowmelt runoff modeling, such integration

provides valuable basis for better understanding of snow accumulation and snowmelt

runoff processes within the catchments, as well as for incorporating the spatial variability

of hydrological and geographical variables and their impacts on catchment responses

(Ahmad 2005). The research hypothesis of this study builds on such an integration and

4

utilizes remotely sensed satellite imagery of MODIS instrument aboard the Terra

spacecraft for snow cover mapping. The WinSRM (Snowmelt Runoff Model for

Windows) is used for snowmelt runoff modeling while all the analysis and map overlays

are supported by GIS technique using ArcGIS 9.2.

1.3 Study Objectives

Climate change is likely to affect basin’s water resources so there is a need to monitor

and estimate the fresh water resource base (snowcover) and assess the impacts of its

variation on net water availability. Moreover, WAPDA plans to construct two dams each

at Kalam and 5 km upstream of Munda Headworks with live storage capacities of 0.32

BCM (0.26 MAF) and 0.826 BCM (0.67 MAF) respectively under the Water Vision-25

program. The analysis carried out in this study will evaluate temporal availability of

surface water resources and help optimally design and operate these projects. The

specific objectives of this research study are;

1. Estimation of spatial and temporal distribution of snowcover through satellite

remote sensing,

2. Estimation and quantification of snowmelt and rainfall runoff components

through hydrological modeling, and

3. Development of snowmelt runoff prediction models.

5

C H A P T E R T W O

L I T E R A T U R E R E V I E W

2.1 General

Remote sensing and hydrology of snow are indeed wide and quite mature disciplines in

themselves and in fact a large amount of related theories and contemporary literature is

available. This chapter is fairly specific emphasizing only the basic concepts and the most

relevant aspects to this study. For a more detailed and comprehensive review, interested

readers are encouraged to refer Rees 2006; Sing & Sing 2001; and US Army of Corps

Engineers 1956.

2.2 Properties of Snow

Snow is a mixture of ice crystals, liquid water and air; and forms from the crystallization

of ice particles in the atmosphere during precipitation. The newly formed snow generally

crystallizes in hexagonal shapes with grain size varying from 0.01 – 0.5 mm but they

alter greatly over time due to metamorphosis and can form different shapes and sizes.

Snow pack below 0oC temperature is dry snow and it hardly contains any liquid water but

in wet snow at or above 0oC significant quantities of liquid water may be present.

Wetness by volume typically ranges up to about 10 %. The total amount of water

contained in a snow pack is specified by the snow water equivalent (SWE), which is

depth of liquid water layer produced by the melting of all snow pack. If the density of

snow pack is uniform, the typical value of SWE is around one-third of its depth (Rees

2006). A typical density of freshly fallen snow is about 0.1 gm/cc. However, as the snow

ages, its density increases as a result of compaction by wind and gravity, and through

thermal metamorphism (Sing & Sing 2001).

Thermal properties of snow such as specific heat, latent heat of fusion, thermal quality,

thermal conductivity, thermal diffusivity, and cold content are vital for computation of

snow ablation, snowmelt, and energy balance of sow pack.

6

Reflective properties of snow are determined by its albedo and dielectric constant.

Albedo of snow is the ratio of the reflected to the incoming solar radiation. Higher albedo

values indicate greater reflection of incoming radiation. Spectral reflectivity of snow

depends on grain size and shape, impurity content, liquid water content, depth, surface

roughness, and solar elevation angle (Hall and Martinec 1985). Depending on the

condition of the snowcover surface and the height of sun, the value of its albedo may

vary from 0.29 for very porous, dirty, saturated with water snow to 0.86 for clean,

compact and dry snow (Sing and Sing 2001). Moreover, the reflective properties of snow

significantly differ in the various regions of the electromagnetic spectrum. Since ice

constituting snow is in highly divided form, usually 109 particles per cubic meter, the

fresh dry snow looks white and is highly reflective in the visible range (0.4-0.65 µm). In

the short-wave infrared region, however, it has strong absorbing characteristics. In the

thermal infrared region its reflection is very low and does not exceed 1% for grain sizes

above 100 µm (Rees 2006). In the microwave region reflective properties are mainly

controlled by dielectric constant, which is the measure of the response of a material to an

applied electric field, such as electromagnetic wave and is the function of radiation and

frequency. The greater the difference between the dielectric constant of snow and that of

external medium, the greater the reflection coefficient, hence propagation of microwave

radiation through dry snow is generally dominated by scattering. Because real part of

dielectric constant of ice is practically constant throughout the microwave region and

snow is a low loss dielectric medium, the real part of dielectric constant of snow depends

only on the snow density and is given by ∈ s = 1 + 1.9ρs For a snow density of

0.3 gm/cc, the above equation yields dielectric constant of 1.57.

2.3 Remote Sensing of Snow

Satellite-Based Remote Sensing Technology has revolutionized the monitoring of spatial

and temporal distribution of snow area extent (SAE) and snow depth in the complex

natural conditions at regional and global scales. Satellite remote sensing involves making

inferences about the nature of particular objects at the earth from the characteristics of the

electromagnetic radiation received at the sensor and establishes relationship between

object’s physical properties and the received radiation. Because of higher albedo and

7

highly reflective nature, snow offers a good contrast with most other natural surfaces,

except cloud, in the visible region. Hence, it is well suited to satellite remote sensing.

Due to this effect, snow was detected from space in the first ever satellite image obtained

through TIROS-1 weather satellite in its April 1960 launch (Singer and Popham 1963).

Later on snow was mapped from space on a weekly basis following the launch of the

Environmental Science Service Administration (ESSA-3) satellite which carried the

Advanced Vidicon Camera System (AVCS) that operated in the spectral range of 0.5-

0.75 µm with a spatial resolution at nadir of 3.7 km. However large scale purposeful

snowcover mapping in the northern hemisphere intensified after the National

Oceanographic and Atmospheric Administration (NOAA) launched a variety of sensors,

including the Scanning Radiometer (SR), Very High Resolution Radiometer (VHRR) and

Advanced Very High Resolution Radiometer (AVHRR).

Microwave remote sensing products, like SMMR, SSM/I, and AMSR-E, are generally

used for global scale studies because of their coarse resolution (25 km), daily

observational frequency and no influence of cloud cover. Products derived from optical

instruments using reflected solar radiation, such as AVHRR, MODIS, and Landsat, etc.,

have higher spatial resolution and are better for regional studies, but heavily depend on

suitable weather conditions, especially clear sky (no clouds). The high cost and low

temporal resolution (16 days) of Landsat data are an obstacle to its wide application in

monitoring snow, even though it has much higher spatial resolution (30 m) than MODIS

and AVHRR. The NOAA-AVHRR frequency is twice every 24 hours (one daytime pass

and one nighttime pass) but very high resolution (1 km) may be insufficient for snow

mapping outside the polar regions particularly on small basins. The Moderate Resolution

Imaging Spectroradiometer (MODIS) is one of the most sophisticated and recent

instrument carried over Terra/Aqua spacecrafts, which offers good alternative. Due to a

wide range of spectral bands (36), daily observational frequency and relatively higher

spatial resolution (500 m) its use for snowcover detection is preferable.

8

2.4 Process of Snowmelt

Watersheds store water in its various forms including snow, which may range from a

newly fallen crystalline snow to glacial ice. The release of water from various forms of

snow and ice results from the net heat exchange between snow pack and its surrounding

environment, but the rate of melting is different for each form due to their varying

thermal properties. Light fresh snow melts faster than the old snow that has been altered

to ice. The energy balance or heat budget of a snow pack, which governs the production

of melt water, accounts the incoming energy, outgoing energy and the change in energy

storage of the snow pack for a given period of time. If all the heat fluxes toward the snow

pack are considered positive and those away considered negative, the sum of these fluxes

is equal to the energy available for melting of the snow pack for a given time period.

Hm = Hrs + Hrt + Hs + Hl + Hg + Hp (6)

where Hm is the energy available for melting of snow pack; Hrs is the net solar radiation;

Hrt is the net thermal radiation; Hs is the sensible or convective heat transfer from air; Hl

is the latent heat of evaporation, condensation or sublimation; Hg is the heat transfer

through conduction from underlying ground; and Hp is the heat content of precipitation.

The solar radiation (Hrs) is the net of incoming minus reflected solar radiation while

thermal radiation (Hrt) is primarily the net of incoming radiation from the atmosphere,

clouds, and surrounding vegetation minus the outgoing blackbody radiation from the

snow pack itself. Sensible heat transfer occurs when the air temperature is different from

the snow pack temperature. If the air is colder, Hs is negative conversely it will be

positive. Latent heat is the energy released during a phase change of water from vapor to

liquid to solid when condensation onto the snow pack occurs, or conversely, it is the

energy extracted from the snow pack when evaporation or sublimation from the snow

pack occurs. Condensation, evaporation or sublimation depends on the humidity of the air

and the water vapor pressure gradient between the air and the snow surface. If the

humidity is high, such that the vapor pressure of the air is greater than that at the snow

surface, the vapor pressure gradient is towards the snow resulting in condensation and, in

this case Hl is positive. If the air is dry, evaporation and/or sublimation will occur, and Hl

9

will be negative. The Hg will be positive if the snow is colder than the underlying soil and

negative if the snow is warmer, whereas Hp will be positive if the temperature of the

precipitation is warmer than the snow and negative if it is colder.

Only the positive value of Hm will result in melting of snow. The relative importance of

the above described energy balance terms involved in melting of snow pack depends on

time and local conditions. For example radiation melting dominates when wind is calm,

whereas melting due to sensible heat flux dominates in warm and windy conditions (Sing

and Sing 2001). When all the components of energy balance equation are known and Hm

is positive, the melting of snow pack is given by:

βρ LHMw

m= (7)

Where M is the depth of melt water (m/day), L is the latent heat of fusion (333.5 kJ/kg),

ρw is the density of water (1000 kg/m3), and β is the thermal quality of snow. The

thermal quality of snow pack is the ratio of the heat input required to produce a given

amount of water from snow relative to that required to melt the same quantity of water

from pure ice at 0oC. It is usually found in the range of 0.80 – 1.1.

2.5 Snowmelt Runoff Modeling

Snowmelt runoff is a major component of the hydrologic cycle in many regions. Its

modeling needs knowledge of site specific climatic conditions, comprehension of basin

characteristics and an understanding of various processes associated with snow

accumulation, snowcover properties, snowcover distribution, surface energy exchange,

water retention and movement through snow pack, snow soil interaction, and routing of

generated snowmelt runoff (Sing and Sing 2001).

Computation of snowmelt from a snow pack can precisely be accomplished using the

energy balance approach described above. The energy balance models, also known as

physically based models, use fundamental physical principles and equations that describe

the physics of processes in each component of the energy balance (Dingman 1994).

Generally, different models simulate the surface energy balance in similar ways, with

10

more or less complex treatments of albedo, and often ignoring some of the less important

energy terms. However, there is considerable variation between models in the ways in

which the internal distribution of heat and mass are represented within the snow profile.

Many models treat the snow pack as a single, lumped layer. This is true, for example, for

the snow hydrology component of the SHE model (Morris 1982; Abbot et al. 1986), the

DHVSM (Wigmosta et al. 1994), and the Hadley Centre land surface scheme (Essery

1997). In these models, internal state variables such as temperature or density are treated

as average values for the whole snow pack.

The most complex ‘layered models’ utilizes vertically distributed implementations of

coupled partial differential equations to represent heat and mass transfer (Anderson 1976;

Brun et al. 1989; Jordan 1991; Morris et al. 1993). These models simulate details of snow

pack stratigraphy, temperature gradients and melt water movement. These models are

perhaps most suitable for examining processes occurring on short, hourly times scales,

such as nocturnal refreezing of the surface and melt water outflow from the base of snow.

Theoretically, the physically based models are most accurate and have applicability in a

wider range of conditions and environments. However, the major disadvantage of such

models is their large and complex data requirements. In most cases some of the variables

are not observed at all and are often estimated inducing some degree of errors.

Alternatively, conceptually index models use one or more variables in an empirical

expression to estimate snowcover energy exchange. Air temperature is the best and most

commonly used index, but other variables such as net radiation, wind speed, vapor

pressure and solar radiation may also be used. The temperature index, also known as

degree-day method, is more popular and widely used because air temperature reasonably

represents the energy flux and at the same time it is relatively an easy parameter to

measure, extrapolate and even forecast. The temperature index models physically lump

all the components of the surface energy balance into a degree-day melt factor, which is a

proportionality coefficient that calculates melt rates on the basis of air temperature

(normally in excess of some threshold value) alone. Several operational models,

including the Snowmelt Runoff Model-SRM (Martinec 1975 & 2007; HBV (Bergstrom

1975), used to forecast runoff from mountainous areas use temperature index approach.

11

The main advantage of temperature index models is the data requirements may be limited

to as little as average daily air temperatures, the most easily measured and widely

available meteorological variable. However, this is also potentially their biggest

drawback as factors other than air temperature control melt rates. In particular, radiation

is often the most important factor controlling melt rates in mid-latitude mountainous

areas; and although air temperature and net radiation may be correlated over the course of

several weeks (Ferguson 1999), simple temperature index models cannot incorporate

variation in radiation receipt directly. Moreover, even though air temperature is obviously

an important control over turbulent fluxes, wind speed and surface roughness also play a

role and are not included in a degree-day melt factor. Hence, snowmelt prediction

through the conceptually index models can be significantly improved by incorporating

vapor pressure, net radiation and wind speed rather than the temperature alone.

Given the advantages and disadvantages of both conceptual temperature index and

physically based energy balance models, a number of attempts have been made to

generate hybrid approaches, which tend to keep the simplicity of the degree day approach

and accuracy of the energy balance approach by explicitly incorporating other important

components of the surface energy balance, principally the radiation. These ‘extended

formulation’ models include that of Anderson 1973, whose combined approach used

degree-day formulation during dry periods and a simplified empirical energy balance

formulation during rainy periods. The UBC runoff model (Quick and Pipes 1977) and the

HYMET runoff model (Tangborn 1984) both add the use of daily temperature range as a

measure of cloud cover, and thus radiation. The most common addition though to

temperature index-type models has been the simple incorporation of measured shortwave

radiation (Martinec 1989) or net radiation (Martinec and de Quervain 1975; Kustas and

Rango 1994; Brubaker et al. 1996). Pipes and Quick (1987) found that partial energy

based (EB) depicted better results than the temperature index (TI) models in both small

and large basins in British Columbia, and far better results in a heavily glacierized

Karakoram basin where temperature index (TI) drastically underestimated radiation melt

at higher elevations.

12

Irrespective of the choice of method, modeling of the spatial distribution of snowcover

and melt usually is accomplished by dividing a watershed into a number of smaller land

units based on topographic facets such as elevation bands and hill slopes or by

geometrical subdivision into grid squares. Within individual land units all hydrological

processes are parameterized or described by physical and/or empirical formulas.

2.6 Related Research

Areal extent of snowcover has been an important variable for a number of uses including

snowmelt runoff prediction in snow-fed basins (Martinec, 1975 & 1985; Hall & Martinec

1985), for accurate specification of the boundary conditions in surface-atmospheric

modeling (Dai et al 2003; Zeng et al 2001), and for modeling atmospheric, hydrological,

and ecological processes (Simic et al 2004). Snowmelt runoff modeling in high mountain

areas based on periodical snowcover mapping derived from earth observation satellites

has been regularly reported in the literature particularly after 1970s when a breakthrough

was achieved in satellite based snowcover mapping (Martinec1973; Odegaard & Ostrem

1977; Rango et al 1977; Gupta et al 1982; Baumgartner et al 1985; Kumar et al 1991;

Martinec et al 1991; Seidel & Martinec 1992; Rango & Martinec 1999).

Since, runoff regimes in most of the northern basins are mainly controlled by the melting

snowcover; snowmelt runoff modeling in these basins has been important aspect of

hydrology. However, due to a wide variety of input data needs, it is also a cumbersome

issue as most of the data are not available. Consequently attempts have been made to

simplify the effort. Regression and other models of runoff based on satellite derived

estimates of snow covered areas have underscored the importance of seasonal snowmelt

in the HKH region (Rango et al 1977; Qureshi & Umar 1978; Tarar 1982; Dey et al 1983

& 1988; Ramamoorthi 1983 & 1987; Makhdoom & Solomon 1986; Kumar 1991) and

elsewhere (Odegaard and Ostrem 1977; Yang et al 2003; Zhou et al 2005). The snowmelt

runoff volume in the snowmelt period in a given basin was statistically related to the area

covered by snow at the start of snowmelt season. More precise attempts generated

regression models on monthly or even weekly basis.

13

Odegaard & Ostrem (1977); using Landsat satellite data empirically related the

snowcover area with runoff in Norwegian catchment and observed that the snowcover

area can be used to forecast the expected snowmelt runoff with a reasonably good

accuracy.

Rango et al (1977) developed linear regression models for estimating seasonal runoff

volume in the Kabul and Indus River basin from the single time ESSA and NOAA

satellite- observed snowcover data in the western Himalayan basin. Dey et al (1983)

employing similar approach developed similar models for the same area using NOAA-

VHRR snowcover datasets of the following six years. They also extended the earlier

work of Rango et al (1977) and developed linear regression model by combining both the

datasets.

Tatar (1982) also found a significant correlation between the variations in March or April

snowcover and the summer-season runoff for several basins of the Indus system in the

Himalayas. He concluded that the Landsat snow-coverage data for remote areas were

susceptible to yield seasonal stream flow predictions by applying a liner regression

equation and the relationships were at best preliminary and would need to be improved

and refined by supplementation through field data collection and application of improved

analytical tools and procedures.

In an attempt to relate snowcover area with snowmelt runoff volume, Gupta et al (1982)

found that for a particular sub-catchment the relationship between snow area extent and

snowmelt runoff was independent of geographic factors like solar illumination,

catchment orientation and relative location. Instead, geomorphical factors such as size of

sub-catchment, permanent snowcover, average altitude, lithology, and stream have major

impact. Consequently he suggested different relations for each sub-catchment.

Makhdoom and Solomon (1986) examined the usefulness of the snowmelt forecasting

models from the snowcover for the Indus basin of Pakistan and found their limited

practicability due to variability of snow water equivalent (SWE) for the same snowcover.

They emphasized the need for improved estimation of snow area extent and information

on depth and density of snowcover.

14

Dey and Sharma (1989), using NOAA-4 satellite imagery, tested the SRM (Martinec

1983) for a large Kabul river basin to assess the accuracy of model simulation in a

subtropical environment and its performance on a daily basis for the snowmelt season.

They observed very poor simulation due to unrepresentative lapse rate, extremely

marginal climatic data, and larger difference between the mean elevation of the total

basin and that of snowmelt contributing area.

DeScally (1994) found strong correlations between point field measurements of the

annual maximum of snow pack water equivalent and of total winter precipitation in the

Kunhar sub-basin of Chinab River and total annual discharge. The total winter snowfall

also showed significant correlation with annual discharge. Monsoon rainfall appeared to

be a very poor indicator of annual discharge.

Mashayekhi and Mahjoub (1991), using field data, developed multiple linear regression

models for snowmelt forecasting from Karadj river basin, Iran and found their regression

model more accurate for seasonal forecast than the monthly forecasts.

Yang et al (2003) using long term NOAA snowcover data examined and compared the

weekly mean stream flow with the weekly basin snowcover extent in large Siberian

watersheds and developed statistically significant weekly runoff-snowcover logarithmic

relations for weeks of strong snowmelt. This approach is good when there is very high

variability of snowcover and associated stream flow and they could not be related

accurately for longer durations. But when a definite trend is prominent and best-fit

regression line can be drawn for longer durations, then it is better to develop regression

models for monthly or even seasonal discharges.

A regression analysis of stream flow and snow area extent conducted by Zhou, et al 2005

for the Upper Rio Grande River Basin, USA depicted statistically significant logarithmic

decay function of the stream flow with snow area extent for the daily as well eight-day

MODIS snowcover products, but the correlation coefficient from the 8-day product was

larger than that from the daily product.

15

PARC & ICIMOD (2005) developed inventory of glaciers, glacial lakes and dangerous

glacial lake in the HKH region of Pakistan using one time Landsat-7 imagery and

calculated the glacier area and ice reserve in each of the 10 sub basins of Pakistani Indus

Basin. However, they did not estimate or quantify the seasonal variation of snowcover.

The major problem with most of the above referenced studies was that they used satellite

imagery having either very low temporal resolution (mostly single time or in some cases

few images) or very high spatial resolution of over 1 km. The meteorological data used

was not much representative of the study area due to its unavailability. Moreover, they do

incorporate the contribution of rainfall which may have significant impacts particularly in

rainfed areas and the observed flows may not be the true representative of the associated

snowcover. The summer flows are completely the function of winter snowcover only

when no any snowfall is observed in the summer months. Hence, the basins receiving

considerable amount of snowfall in summer months may not be modeled correctly with

that approach.

This study on the other hand takes care of these factors and utilizes continuous time

series satellite data having relatively higher spatial resolution. It also employs a different

approach of relating daily values of snowcover and discharges. Moreover, the more

representative meteorological data and recent improvements in the SRM further add to

the applicability, suitability and recognition of this research study.

16

C H A P T E R T H R E E

D E S C R I P T I O N O F T H E S T U D Y A R E A

3.1 Physiography

The Swat valley, stretched mainly over the Swat River Basin of North West Frontier

Province (NWFP) of Pakistan, is one of the most beautiful valleys of the country.

Because most parts of the valley are geographically located within the monsoon belt, the

valley is largely greener and more fertile than the valleys further north. The Swat valley

is famous as the land of waterfalls, lakes, lush green hills and other beautiful natural gifts

bestowed upon it by the nature. The valley also offers some of the best walking trails in

Pakistan, as well as excellent opportunities for fishing and climbing. The excavated

archaeological sites here range from prehistoric caves through Aryan graveyards to

Buddhist monasteries.

The Swat River originates in upper Swat between Shandur and Kalam towns tumbling

through pine forests hemmed in by snowcapped mountains through two of its originating

tributaries, the Ushu (north-eastern) and Utrot (north-western) rivers, which together

form the Swat River near Kalam. The river drains parts of the Hindu Kush, Dir, Swat,

and Kohistan ranges in the western territory of Pakistan. The Panjkora River is its major

tributary joining it on the right side downstream of Chakdara town and upper Swat canal.

After passing through eastern parts of Bajour and Mohmand agencies close to Munda

Headworks, the river unites the Kabul River near Nowshera in the NWFP, which

ultimately joins the Indus River downstream of Tarbela dam at Atock.

The study is undertaken in the catchment area of upper Swat River upstream of Chakdara

gauge station. The upper Swat River Basin is located between the latitude and longitude

range of 34.57 to 35.896 and 71.928 to 72.834 decimal degrees respectively covering an

area of 5713.38 km2 (Figure 3.1), which is about 39 % of the total area of the Swat river

basin. Its northern part has high mountainous rugged terrain with elevation range of 2000

– 5808 m a.s.l., whereas the southern part is relatively flat with elevation range of 686 –

2000 m a.s.l. having some crop fields on either side of the river.

17

Figure 3.1 Location map of the study area in Pakistan

18

3.2 Landuse Pattern

The most part of the basin lies in the active monsoon belt and possesses coniferous forest

dominant landuse. The other major landuse types in the basin are scrub, and alpine

forests, agriculture, and grassland. Figure 3.2 presents a Three-Dimensional view of the

rough idea of the landuse pattern obtained through Landsat-7 TM image draped over

Digital Elevation Model (DEM) of the study area. Socio-economic conditions in the area

are generally poor. Snowcover at higher elevations at northern part is quite prominent in

this image.

Figure 3.2 3-D view of the true color Lanndsat-7 image draped over DEM of study area

19

3.3 Climatic Conditions

Based on the historic as well as prevailing climatic conditions, the study area can be

divided into two parts. The upper north-eastern part – Kalam and surrounding areas –

comprises very rugged mountain topography and may receive a maximum temperature of

37 oC in June at Kalam to as low as – 18.2 oC in January at Shandur. The lower south-

eastern part – near Saidu and Chakdara – is relatively flat, receiving considerably higher

temperatures ranging from -2 oC in January to as high as 45 oC in June. Similarly, the

precipitation pattern in the lower south-western part is influenced by the summer

monsoon rainfall, which originates in the Bay of Bengal and after crossing India reach

Pakistan in early July and continue till late September. The upper north-eastern part on

the other hand is dominated by the winter rainfall mainly received from the Western

Disturbances, which come from the Mediterranean and after passing through Iran and

Afghanistan enter Pakistan in December and continue till early April. The northern

highlands receive most of winter precipitation in the form of snow.

Figures 3.3 and 3.4 present average monthly temperature and precipitation received at

Saidu (1990 – 2005 record), Kalam (2003 – 2006 record) and Shandur (2003 – 2006

record) met stations respectively. The mean annual precipitation at Saidu is 1086 mm, of

which 56% falls in summer (Apr – Sep). Kalam receives little more annual precipitation

but summer precipitation is only 33% of the total of 1376 mm. Due to higher elevation

most of the winter precipitation at Kalam falls in the form of snow, whereas relatively

higher summer monsoon rain at lower elevations augmented by snowmelt runoff,

sometimes causes heavy floods in the area. At Shandur, however, the weather becomes

significantly dry with only 208 mm of annual precipitation. Generally weather becomes

gradually drier if some one goes further north from Kalam. This diverse climate coupled

with very high variability of altitude ranging from 686 – 5808 m a.s.l. provide conducive

environment for significant snow accumulation in the winter months and subsequent

snow ablation in the following summer months.

20

-15

-10

-5

0

5

10

15

20

25

30

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Month

Tem

pera

ture

(oC

)

Saidu Kalam Shandur

Figure 3.3 Variability of average monthly temperature at the three places

0

25

50

75

100

125

150

175

200

225


Month

Prec

ipita

tion

(mm

)

Saidu Kalam Shandur

Figure 3.4 Variability of average monthly precipitation at the three places

21

3.4 Hydrological Characteristics and Water Resources

The upper Swat is predominantly a snow-fed river basin as under optimum conditions

about 80% of its area can receive snowfall and over 74% of the total flows can come

from snowmelt runoff. Most of that snowcover is concentrated in the northern part at an

elevation exceeding 2500 m a.s.l.. In winter the areas even at 1500 m elevation can be

blocked by snow, which however melts in the summer and one can drive up beyond

Kalam and from there trek north to either Chitral or Gilgit valleys. The upper reaches of

the Kohistan-Swat ranges are mostly covered with snow and glaciers. PARC and

ICIMOD (2005) identified six types of glaciers present in the basin. These are Mountain,

Cirque, Ice cap, Niche, Ice apron, and Valley. Mountain glaciers are the dominant type

followed by Valley glaciers.

Pakistan’s Water and Development Authority (WAPDA) has established two river gauge

stations each at Kalam and Chakdara towns on the upper Swat River. The flows at Kalam

gauge come predominantly from snowmelt runoff, whereas at Chakdara considerable

contribution of summer monsoon rainfall runoff is also received. Figure 3.5 presents

average and at 60 % probability (3 out of 5) daily river discharges measured at the

Chakdara gauge station.

0

100

200

300

400

500

600

J F M A M J J A S O N D

M onth

Dai

ly D

ischa

rge

(Cum

ec)

Average 60% Probability

Figure 3.5 Average and at 60 % probability river discharges measured at Chakdara

22

The average monthly flows observed at the Chakdara gauge station can be estimated by

the second order polynomial function for the two halves of a calendar year as shown in

Figures 3.6 and 3.7. The lowest average monthly flows of about 41.93 m3/sec (0.112

BCM–billion cubic meters) are observed in January, whereas the highest flows observed

in June are over ten times (425.81 m3/sec or 1.14 BCM) that of January flows. Similarly,

on an average, about 80 % flows are received in Kharif (summer) cropping season (Apr –

Sep) leaving only 20 % for Rabi (winter) cropping season (Oct – Mar). The minimum

annual flows of 3924.954 MCM were observed in 2001-02 because of severe drought,

while maximum annual flows of 6803.035 MCM were observed in 2004-05 when

drought was over, depicting the variability of 1.73 times between minimum and

maximum annual flows. This very high monthly, seasonal as well as annual variability of

upper Swat river flows necessitates comprehensive study of the available hydrological

resources and development of appropriate models for predictions of water resources to

ensure their better planning and management.

y = 10.503x2 + 4.2595x + 24.424R2 = 0.9995

050

100150200250300350400450

1 2 3 4 5 6

Calender Month Number

Ave

rage

Mon

thly

Obs

eved

Disc

harg

e (C

umec

)

Figure 3.6 Variability of average monthly discharge at Chakdara for the 1st half of a year

23

y = 19.203x2 - 205.87x + 605.35R2 = 0.9938

050

100150200250300350400450

7 8 9 10 11 12


Ave

rage

Mon

thly

Obs

eved

Disc

harg

e (C

umec

)

Figure 3.7 Variability of average monthly discharge at Chakdara for the 2nd half of a year

24

C H A P T E R F O U R

M E T H O D O L O G Y

4.1 Outline

This chapter describes the MODIS instrument, its snowmap algorithms and snowcover

products and discusses the accuracy of snowcover products. The Snowmelt Runoff

Model is also described followed by the details of the important methodological steps

employed to achieve the specified research objectives. Finally the statistical approach

used to develop the prediction models of snowmelt runoff is explained. However, the

overall conceptual model or flow chart of the basic methodological approach adopted to

accomplish the study is summarized in the Figure 4.1.

4.2 The MODIS Instrument

The Moderate Resolution Imaging Spectroradiometer (MODIS) is a key instrument

aboard the Terra spacecraft launched on December 18, 1999 and the Aqua spacecraft,

launched on May 4, 2002. Terra's orbit around the Earth is timed so that it passes from

north to south across the equator in the morning, while Aqua passes south to north over

the equator in the afternoon. MODIS instrument acquires images in 36 spectral bands

between 0.405 and 14.385 µm for different uses (Table 3.1). A ± 55 degree scanning

pattern via a two-side scan mirror at the EOS orbit of 705 km achieves a swath of 2,330

km cross track by 10 km along track (at nadir) each scan and views the Earth’s entire

surface ranging from every day at high latitudes to every other day at low latitudes

(Justice et al. 1998). Its spatial resolution varies with spectral band, and ranges from 250

m to 1 km at nadir. The 1st two bands are imaged at a nominal resolution of 250 m at

nadir, next five bands at 500 m, and the remaining 29 bands at 1 km.

This study uses snow products of the TMODIS-Terra, because band 6 on Aqua spacecraft

is only partly functional and most of algorithm development and testing work is done on

MODIS-Terra products. Hence the algorithms and data products described here in

primarily refer to MODIS-Terra sensor. These algorithms and products for MODIS-Aqua

sensor however, only slightly change.

25

Digital

Elevation

Model

MODIS

Snow Cover

Imagery

Daily

Climatic

Data

Daily

Observed

Discharge

Stream Network & Watershed Delineation

Elevation Zones

Snowcover Distribution

Model Input Parameters

Figure 4.1 Conceptual models (flow chart) of the adopted methodological approach

Model Calibration

Snowmelt Runoff Model

OK

Model Simulations

No

Yes

Snowmelt Runoff Rainfall Runoff

Regression Models Regression Models

26

Table 4.1 MODIS spectral bands and their primary uses.

Primary Use Band Bandwidth (µm) Primary Use Band Bandwidth (µm)1 0.620 – 0.670 20 3.660 - 3.840 Land/Cloud/

Aerosols Boundaries 2 0.841 – 0.876 21 3.929 - 3.989

3 0.459 – 0.479 4 0.545 – 0.565

Surface/ Cloud Temperature

22 3.929 - 3.989

5 1.230 – 1.250 23 4.020 - 4.080 6 1.628 – 1.652

Atmospheric Temperature 24 4.433 - 4.498

Land/Cloud/ Aerosols Properties

7 2.105 – 2.155 25 4.482 - 4.549 8 0.405 – 0.420 26 1.360 - 1.390 9 0.438 – 0.448

Cirrus CloudsWater Vapor

27 6.535 - 6.895

10 0.483 – 0.493 Cloud Properties 28 7.175 - 7.475

11 0.526 – 0.536 Ozone 29 8.400 - 8.700 12 0.546 – 0.556 30 9.580 - 9.880

13 0.662 – 0.672

Surface/ Cloud Temperature 31 10.780 - 11.280

14 0.673 – 0.683 32 11.770 - 12.270 15 0.743 – 0.753 33 13.185 - 13.485

Ocean Color/ Phytoplankton/ Biogeochemistry

16 0.862 – 0.877 34 13.485 - 13.785 17 0.890 – 0.920 35 13.785 - 14.085 18 0.931 – 0.941

Atmospheric Water Vapor

19 0.915 – 0.965

Cloud TopAltitude

36 14.085 - 14.385

4.2.1 MODIS Snow Mapping Algorithm

The development of the MODIS snow mapping algorithm (snowmap) is chronicled in

detail elsewhere (Hall et al 1995; Klein et al 1998; Hall et al 2001 and 2002; Hall and

Riggs 2007; Riggs et al 2006), hence only a cursory overview is presented here. The

snowmap (Hall et al 1995) is the basis for all MODIS snow cover products. However, the

algorithm has been continuously evolving as limitations become apparent in early

versions of data. The basic techniques used in the snowmap algorithm are grouped-

criteria incorporating the normalized difference between bands, threshold-based criteria

tests, and decision rules (Hall et al 2001).

27

The first test of snow detection uses the Normalized Difference Snow Index (NDSI)

approach, which is an effective way to distinguish snow from many other surface features

taking advantage of strong visible reflectance and strong short-wave IR absorbing

characteristics of the snow pack. The NDSI is defined as the difference of reflectances

observed in a visible band such as MODIS band 4 (0.555 μm) and a short-wave infrared

band such as MODIS band 6 (1.640 μm) divided by the sum of the two reflectances.

6464

BandBandBandBandNDSI

+−

= (1)

Generally, snow is characterized by higher NDSI values than other surface types and

pixels. A pixel is mapped as snow if the NDSI value is ≥ 0.4 and the reflectance in

MODIS band 2 is greater than 0.11. However, if the reflectance in MODIS band 4 is less

than 0.10 then the pixel will not be mapped as snow even if the other criteria are met

(Hall et al 2001 and 2002). This minimum reflectance test screens low reflectance

surfaces, e.g. water that may have a high NDSI value from being erroneously detected as

snow. However, in forest areas snow-covered pixels may have considerably lower NDSI

values and to correctly classify these pixels as snow-covered, NDSI and NDVI are used

together to the pixels that have an NDSI value in the range of 0.1 to 0.4. MODIS bands 2

and 1 are used to calculate NDVI.

1212

BandBandBandBandNDVI

+−

= (2)

Snow cover tends to lower the NDVI therefore pixels with NDVI value of ≈ 0.1 may be

mapped as snow even if the NDSI < 0.4 (Klein et al 1998). Moreover, pixels with an

absolute reflectance of greater than 0.11 in MODIS band 2 and greater than 0.10 in

MODIS band 1 are determined as snow.

Because of higher reflectance of clouds in near-infrared wavelengths the NDSI generally

separates snow from most obscuring cumulus clouds, but it cannot always discriminate

optically-thin cirrus clouds from snow. Instead, cloud discrimination is accomplished by

using the MODIS cloud mask product, MOD35L2, (Ackerman et al. 1998; Plat nick et al.

28

2003), which employs a series of visible and infrared threshold and consistency tests to

specify confidence that an unobstructed view of the Earth’s surface is observed. An

indication of shadows affecting the scene is also provided.

Land and inland waters are masked with the 1 km resolution land/water mask, contained

in the MODIS geolocation product (MOD03). In Collection 5 the land/water mask made

by the Boston University (BU) team based on EOS data is used. The 1 km data of the

land/water mask is applied to the four corresponding 500 m pixels in the snow algorithm

to analyze inland waters.

Thermal mask is used to improve the snow mapping accuracy and to eliminate the

spurious snow especially in warm climates. Using MODIS infrared bands 31 (10.78–

11.28 μm) and 32 (11.77–12.27 μm), a split window technique (Key et al., 1997) is used

to estimate ground temperature (Hall et al., 2002). If the temperature of a pixel is >283 K

then the pixel will not be mapped as snow (Riggs et al., 2006).

The collection 5 snowmap algorithm also includes computation of fractional snow cover

for all land and inland water body pixels in a swath. Fractional snow cover is calculated

using the regression equation of Salomonson and Appel 2004, which is based on a

statistical-linear relationship developed between the NDSI from MODIS and the true sub-

pixel fraction of snow cover as determined using Landsat scenes from Alaska, Canada

and Russia. Table 4.2 summarizes the data inputs to the MODIS snowmap algorithm.

The accuracy of snowmap has been tested over a variety of surface covers relative to

other derived snow cover maps; errors were estimated for seven different land covers

using Landsat Thematic Mapper and MODIS Airborne Simulator data prior to the

MODIS launch. In addition, it is fully automated thus reducing or eliminating biases due

to human subjectivity which are problematic in long-term climatology studies (Hall et al

2001). Under ideal conditions of illumination, clear skies and several centimeters of snow

on a smooth surface the snow algorithm is about 93-100% accurate at mapping snow

(Hall and Riggs 2007).

29

Table 4.2 MODIS data product inputs to the MODIS snowmap algorithm.

Earth Science Data Type (ESDT)

Long Name Data Used

MOD02HKM MODIS Level 1B Calibrated and Geolocated Radiances

Reflectance for MODIS bands:

1 (0.645 μm)

2 (0.865 μm)

4 (0.555 μm)

6 (1.640 μm)

MOD021KM MODIS Level 1B Calibrated and Geolocated Radiances

31 (11.28 μm)

32 (12.27 μm)

MOD03 MODIS Geolocation

Land/Water Mask

Solar Zenith Angles

Sensor Zenith Angles

Latitude

Longitude

MOD35L2 MODIS Cloud Mask Cloud Mask Flag

Unobstructed Field of

View Flag

Day/Night Flag

After Riggs et al 2006

4.2.2 MODIS Snowcover Products

MODIS snow products produced through the snowmap algorithm described above are

archived at and distributed by the National Snow and Ice Data Center (NSIDC), which is

one of NASA’s eight Distributed Active Archive Centers (DAACs). The collection 5

MODIS snow data products are currently produced as a sequence of seven products

(Table 4.3) beginning with a 5 min swath segment (granule) at a nominal pixel spatial

resolution of 500 m and a nominal swath coverage of 2330 km (cross track) by 2030 km

(along track) and progressing, through spatial and temporal transformations, to a monthly

global gridded product (Hall et al 2002; Riggs et al 2006; Hall and Riggs 2007).

30

Table 4.3 Summary of the MODIS collection 5 snow data products.

Earth Science Data Type (ESDT)

Product Level

Nominal Data Array Dimensions

Spatial Resolution

Temporal Resolution

Map Projection

MOD10L2 L2 1354 km by 2000 km

500 m Swath (scene)

None (lat, lon referenced)

MOD10L2G

L2G

1200 km by 1200 km

500 m day of multiple coincident swaths

Sinusoidal

MOD10A1 L3 1200 km by 1200 km

500 m Day Sinusoidal

MOD10A2 L3 1200 km by 1200 km

500 m Eight days Sinusoidal

MOD10C1 L3 360° by 180° (global)

0.05° by 0.05°

Day Geographic

MOD10C2 L3 360° by 180° (global)

0.05° by 0.05°

Eight days Geographic

MOD10CM L3 360° by 180° (global)

0.05° by 0.05°

Month Geographic

After Riggs et al 2006

The swath product (MOD10L2) takes input of the MODIS calibrated data products

presented in Table 2, and other criteria specified in the snowmap algorithm and has two

snow cover fields (snow extent and fractional snow cover) at 500 m spatial resolution for

each swath (Riggs et al 2006; Hall and Riggs 2007). The snow cover field classifies each

cloud-free land or inland water body pixel as snow-covered or snow-free, while fractional

snow cover field provides the percent of snow cover within each pixel for land and inland

water bodies. The resultant snow cover maps are the consequence of the snowmap

algorithm, which identifies snow covered land, snow covered ice on inland water and

computes fractional snow cover. After the swath product, each product in the sequence

assimilates accuracy and error from the preceding product (Riggs et al 2006).

31

The second product, MOD10L2G, is a multidimensional data product created by

mapping the pixels from the MOD10L2 granules for a day to the appropriate Earth

locations on the sinusoidal map projection. The third product, MOD10A1, is a tile of

daily snow cover maps at 500 m spatial resolution. The daily observation that is selected

from multiple observations in a MOD10L2G cell is selected using a scoring algorithm to

select the observation nearest local noon and closest to nadir. The fourth product,

MOD10A2, is an eight-day composite of MOD10A1 to show maximum snow extent. The

MOD10C1 is daily global snow cover map in a geographic map projection created by

assembling MOD10A1 daily tiles and binning the 500 m cell observations to the 0.05°

spatial resolution of the Climate Modeling Grid (CMG) cells. Similarly, the global eight-

day snow cover product, MOD10C2, is created by assembling MOD10A2 daily tiles.

There are several different data-product levels starting from level 1B (L1B), which is a

swath (scene) of MODIS data geolocated to latitude and longitude centers of 1 km

resolution pixels. A level 2 (L2) product is a geophysical product that remains in latitude

and longitude orientation of L1B; it has not been temporally or spatially manipulated. A

level 2 gridded (L2G) product is in a gridded format of a map projection. The L2G

algorithm creates a gridded product necessary for the level 3 products. A level 3 (L3)

product is a geophysical product that has been temporally and or spatially manipulated,

and is in a gridded map projection format and comes as a tile of the global grid. A full

description of the products and levels is provided in the MODIS Snow Products User

Guide (Riggs et al., 2006) and product documentation available at the MODIS website.

The study utilizes the MODIS/Terra Snow Cover 8-Day L3 Global 500m Grid

(MOD10A2) data set, which composites eight-days of input from MOD10A1 to generate

maximum snow extent for the period and tracks the chronology of snow observations for

each day. The product gives classified (Table 4.4) image of eight day period showing a

maximum eleven classes presented in the Table 3.4. The eight day periods begins on the

first day of the year and extends into the next year. The product can be produced with two

to eight days of input, as there may not always be eight days of input, because of various

reasons. If snow cover is found for any day, then the cell in the Maximum Snow Extent

field is labeled as snow. If no snow is found, but there is one value that occurs more than

32

once, that value is placed in the cell. Similarly if a cell is observed as other than cloud on

any of the eight days the algorithm assumes a cloud free period and labels the pixel with

the observed value. This logic minimizes cloud-cover extent, such that a cell needs to be

cloud-obscured for all days in order to be labeled cloud.

Table 4.4 Classes of the processed MOD10A2 dataset. Maximum Snow Extent Coded Integer Values

Sample Value Explanation 0 data missing 1 no decision 11 night 25 snow free land / forest 37 inland water 39 ocean 50 cloud 100 lake ice 200 snow 254 detector saturated 255 fill

4.3 The Snowmelt Runoff Model

The snowmelt runoff model (SRM), also known as “Martinec Model” or “Martinec-

Rungo Model” is a semi-distributed, deterministic and degree-day hydrological model

especially designed to simulate and forecast daily stream flow in mountain basins where

snowmelt is major runoff factor (Martinec et al 2007). The model utilizes ambient air

temperature values combined with a degree-day coefficient in order to estimate the

ablation factor of the snow cover (Martinec et al 1998) and takes input of snow covered

area and its variation along meteorological data (Martinec et al 1983). The model can

also be used to evaluate the effect of climate change on seasonal snow cover and

snowmelt runoff. The SRM was originally developed for small European basins but with

the breakthrough achieved in estimating snow cover through satellite remote sensing and

model improvements, it can now be applied in mountain basins of any size and any

elevation range throughout the world (Martinec et al 2007).

33

The study utilizes the Windows Version 1.11 of the Snowmelt Runoff Model (WinSRM),

which is the most recent version. The WinSRM provides an excellent environment for

snowmelt runoff modeling in mountain basins. The basin area is divided in to a suitable

number of elevation zones (not exceeding 16) and various input parameters including

basin characteristics, climatic variables, snow covered area, runoff coefficients, recession

coefficients, etc are specified for each elevation zone. The model manages a physical

database of both input and output for a given basin. Each simulation in the model is a

unique entity operating on a 2 – 366 days. Different simulations can be sequenced for

greater time periods.

Unlike most of the non-deterministic hydrological models, the input parameters for the

SRM are not calibrated or optimized from the historical records. Instead those are either

derived from field measurements or estimated through physical laws, theoretical

principles and empirical or regression relations (Martinec et al 2007) as unsatisfactory

results have been improved not by adjusting input parameters but by correcting the errors

in datasets and input of variables. For this reason the model does not necessarily require

calibration and can be used for ungauged basins as well. However occasional

adjustments, never exceeding the range of physically and hydrologically acceptable

values, are often done. In the rugged and high mountain regions and in a country like

Pakistan where adequate field data is hardly gathered and meteorological data are only

available at a limited density and also in lower valleys, model calibration and allowable

adjustment of certain input parameters is unavoidable.

4.3.1 Governing Equation

Daily water produced from snowmelt and rainfall is computed, superimposed on the

calculated recession flow and transformed into daily discharge from the basin according

to the following equation.

( )[ ] ( ) 111 18640010000.

+++ +−+Δ+= nnnnRnnnnnSnn kQkAPcSTTacQ (3)

34

where:

Q = average daily discharge [m3 s-1]

c = runoff coefficient expressing the losses as a ratio (runoff/precipitation),

with cS referring to snowmelt and cR to rain

a = degree-day factor [cm oC-1 d-1] indicating the snowmelt depth resulting

from 1 degree-day

T = number of degree-days [oC d]

ΔT = the temperature lapse rate correction factor [oC d]

S = ratio of the snow covered area to the total area

P = precipitation contributing to runoff [cm]. A pre-selected threshold

temperature, TCRIT, determines whether this contribution is rainfall

(immediate) or snow (delayed).

A = area of the basin or zone [km 2]

k = recession coefficient indicating the decline of discharge in a period

without snowmelt or rainfall:

k = Qm+1/Qm (m, m + 1 are the sequence of days during a true

recession flow period).

n = sequence of days during the discharge computation period. Equation (1)

is written for a time lag between the daily temperature cycle and the

resulting discharge cycle of 18 hours. In this case, the number of degree-

days measured on the nth day corresponds to the discharge on the n + 1 day.

Various lag times can be introduced by a subroutine.

10000/86400 = conversion from cm·km2 d-1 to m3 s-1

If the study area is divided into certain number of zones then the above equation is

repeated for each zone and the sum of all gives total discharge from the basin.

4.3.2 Model Accuracy Assessment

Apart from the first glance visual inspection of the actually measured and simulated daily

flows in its graphics section, the SRM uses two well established accuracy criteria, the

35

coefficient of determination (R2) and the volume difference (Dv), which are computed as

follows (Martinec et al 2007):

( )

( )∑

∑

=

=

−

−−= n

ii

n

iii

QQ

QQR

1

2

1

2'

2 1 (4)

where: Qi is the measured daily discharge

Qi’ is the computed (simulated) discharge

Q is the average measured discharge of the given season year

n is the number of daily discharge values

The deviation of the runoff volume, Dv (%) is computed as follows:

100'

R

RRv V

VVD

−= (5)

where: is the measured seasonal or annual runoff volume RV'

RV is the computed seasonal or annual runoff volume

The perfect matching and highly accurate simulation will result in R2 values closer to one

and Dv values closer to zero.

4.4 Data Acquiring and Database Development

The primary data required to accomplish this research study are the digital elevation

model (DEM) of the basin; remotely sensed satellite imagery of MODIS instrument; and

daily records on temperature, precipitation and outflows from the basin. The other data

required for model input are mainly derived from these records or estimated through

physical laws and adjusted further during model calibration and verification.

A DEM is the most versatile and widely used representation of a terrain for the

continuous variation of the relief over space. It is a raster representation in which each

grid cell records elevation of the earth’s surface and reflects a view of terrain as a field of

elevation values. The recorded elevation is often the elevation of the cell’s central point

36

but in some cases it may be mean elevation of the entire grid cell. The digital elevation

data are usually organized into three data structures — regular grids, triangulated

irregular networks, and contours — depending on the source and/or preferred method of

analysis. The square-grid digital elevation models have emerged as the most widely used

data structure during the past decade because of their simplicity (i.e. simple elevation

matrices that record topological relations between data points implicitly) and relative ease

of computer implementation.

The elevation data are vital for some applications including prediction of the effects of

global warming and rising sea levels in coastal areas. However, for many other important

applications the value of DEM lies in its ability to produce important derivative measures

and fields such as aspect, slope, flow direction, flow accumulation, stream network and

watershed delineation, etc. through calculation and transformation. The most important

applications of DEM can be seen in physical geography, geomorphology, hydrology,

ecology, soil conservation, forest and watershed management, etc. There has been an

increasing trend during the recent past in the use of DEMs in terrain analysis of the

earth’s surface due to advancements in computer based GIS technologies and easy

availability of digital data.

Most of the currently available digital elevation datasets are the product of

photogrammetric data capture – data are collected by decoding stereo air photos and by

manually or automatically extracting satellite pictures using stereograph plotters.

Additional elevation datasets can be acquired by contour digitalization, field surveys,

global positioning system (GPS), and light detection and ranging (LIDAR).

In 2003, the National Aeronautics and Space Administration (NASA) of the United

States released the Shuttle Radar Topography Mission (SRTM) dataset for some regions,

with 3 arc-second resolution for the globe, and 1 arc-second for the US. This dataset

superseded the previous global dataset of topography, the GTOPO30, produced by the

United States Geological Survey (USGS). The SRTM DEM data was produced using

radar images gathered from NASA’s shuttle. Two antennae received the reflected radar

pulses at the same time, one antenna located in the shuttle’s cargo bay, the other at the tip

37

of a 60-m-long mast. This configuration allowed single-pass radar interferometry, and

consequently the generation of a highly accurate global elevation model with a vertical

accuracy of 6 m and a horizontal pixel spacing of 30 m (Jarvis et al 2004).

The study utilizes digital elevation data acquired from the NASA’s SRTM, which freely

distributes such data through the CGIAR-CSI GeoPortal (http://srtm.csi.cgiar.org). The

latest version (V3) of the SRTM DEM data is available in the shape of tiles and is

projected in WGS-84 projection. This version of SRTM DEM data does not contain any

data holes where water or heavy shadow prevents the quantification of elevation. These

are generally small holes, which nevertheless render the data less useful, especially in

fields of hydrological modeling.

The remotely sensed satellite imagery from the MODIS instrument is processed at the

NASA’s Goddard Space Flight Center and a number of products are developed using the

best available techniques and theories. The MODIS snowmap products are archived at

and distributed freely by the National Snow and Ice Data Center (NSIDC), of NASA,

USA. The study utilizes the collection 5, level 3, MODIS / Terra eight daily maximum

snow extent (MOD10A2) data set, which composites eight-days of input from

MOD10A1 daily snow cover product to generate maximum snow extent for the period

and also tracks the chronology of snow observations for each day. The MOD10A2

snowmap products are available as a 500 m grid (at the equator) projected in Sinusoidal

World projection in the shape of tiles. The study area is covered by the h23v05 tile. In all

141 MOD10A2 snowmap products for three years 2002 – 2004 were downloaded and

processed further in a GIS environment to estimate altitudinal, spatial and temporal

distribution of snowcover in the study area.

The daily meteorological data for a number of met stations is collected from Pakistan

Meteorological Department (PMD). In 2002-03 PMD has established a meteorological

station at Kalam at a height of 2103 m amsl. This met station is located at the centre of

the northern part of the study area. Saidu Sharif and Shandur met stations are located just

outside the study area at an elevation of 961 and 3719 m amsl respectively. There are few

other met stations located outside of the study basin and their temperature data is used to

38

http://srtm.csi.cgiar.org/

estimate the temperature lapse rates due to elevation difference. The study however

utilizes temperature data of Kalam met station and precipitation data of Kalam, Shandur

and Saidu met stations for different elevation zones.

Daily river discharge data for the study area is available from Surface Water Hydrology

Project (SWHP) of Water and Power Development Authority (WAPDA), Pakistan for

two gauge station namely Chakdara and Kalam over the upper Swat River. Chakdara

gauge station is located at the lowest end and is the exit point of all the runoff generated

in the basin. Therefore flow data of this gauge station is used for calibrating and

verification of the SRM and also developing relationship with the snowcover.

4.5 River Network and Watershed Delineation

The two tiles of SRTM DEM data (srtm_51_05 and srtm_51_06) were mosaic and subset

for the study area using the ERDAS Imagine software. The SRTM DEM data has spatial

resolution of 0.0008333 degrees, which becomes approximately 92.6 m by 75.6 m for the

study area. Since most of the analysis in ArcGIS environment is automatically performed

using square cells rather than rectangular, unless the user specifies differently. Therefore

the DEM data was re-sampled to a cell size of 77.2 m2. This cell size when multiplied

with six gives the cell size of the MOD10A2 snowmap data product of MODIS. This

matching of the cell sizes of both the data sets is necessary as it will help perform further

GIS analysis and simplify map overlays. The re-sampled DEM was then re-projected into

Pak-1 projection, which is the modified form of Lambert Conformal Conic projection and

is the standard projection for Pakistan used by Survey of Pakistan and most of the other

organizations.

ArcHydro extension of the ArcGIS 9.2 was used to delineate the river network and their

drainage areas. Before generating the flow direction grid, the sinks present in the original

DEM were filled in and a depression less DEM was generated. A sink is a cell or set of

spatially connected cells whose flow direction cannot be assigned one of the eight

neighboring cell values in a flow direction grid. This can occur when all neighboring

cells are higher than the processing cell, or when two cells flow into each other creating a

two-cell loop. Sinks are considered to have undefined flow directions and are assigned a

39

value that is the sum of their possible directions. The flow direction function in Arc

Hydro Tools assigns to each cell a number corresponding to which of the 8 neighboring

cells lies on the path of steepest descent. The direction of flow is determined by finding

the direction of steepest descent or gradient from each cell. This is calculated as drop =

change in z value / distance * 100. The distance is determined between cell centers.

The flow accumulation grid is created from the flow direction grid by accumulating the

weight for all cells that flow into each down slope cell. Cells of undefined flow direction

will only receive flow; they will not contribute to any downstream flow. A cell is

considered to have an undefined flow direction if its value in the flow direction grid is

anything other than 1, 2, 4, 8, 16, 32, 64, or 128. The accumulated flow is based upon the

number of cells flowing into each cell in the output grid. The results of flow

accumulation are used to create a stream network by applying a threshold value to subset

cells with a high accumulated flow. Higher threshold values will delineate major streams

and lower threshold values will define minor streams. The resultant raster of stream

definition is used for calculating stream segmentation or stream links, which are the

sections of a stream channel connecting two successive junctions, a junction and the

outlet, or a junction and the drainage divide. Finally, the catchment areas for each stream

are delineated from the stream link raster using the catchment grid delineation option.

4.6 Image Processing & Classification

The MODIS snowmap data products are produced through intensive processing and

analysis using the best available techniques and algorithms and often do not require

further processing work generally required in remote sensing techniques. However, some

specific processing is necessary for achieving particular study objectives. The MOD10A2

dataset was acquired for three calendar years (2002 – 2004) and was converted from HDF

format to imagine using IRDAS Imagine software. The MODIS snowcover data products

have spatial resolution of 463.3127165 m2 for the study area. The images were re-

sampled into 77.21878608 m2 cell size to match the cells size of the DEM, while

conserving all the properties of the original dataset. This re-sampling generated 36 cells

from a single cell of the original dataset. The images were then re-projected and subset

40

for the study area using ArcGIS and ERDAS Imagine softwares. In all, 140 images for

the selected three years study period were processed and analyzed in GIS to determine

the altitudinal, spatial and temporal distribution of the snow cover in the different

elevation zones of the study area.

4.7 Derivation of Model Input Parameters

The SRM has modest input data requirements. Besides the DEM, daily records of

temperature, precipitation, and snowcover are the basic input variables. The other input

parameters are mainly derived from these records and outflows from the basin. The

following paragraphs highlight the general procedure adopted to derivation of these input

parameters.

4.7.1 Basin Boundary and Zone Areas

The basin boundary is usually defined by the location of stream gauge (or some arbitrary

point on the course of stream, while watershed divide is identified from the digital

elevation model using suitable GIS software such as ArcHydro in this case. Due to higher

elevation range of 686 – 5808 m amsl the basin is divided into five elevation zones

(Zone-A to Zone-E). The area occupied by each elevation zone and mean hypsometric

elevation of each zone is determined. The mean hypsometric elevation can be determined

either from the area elevation curve or manually by weighted average technique. The

area-elevation (hypsometric) curve is the plot of cumulative area versus elevation. The

zonal mean hypsometric elevation ( h ) can be determined from this curve by balancing

the areas above and below the mean elevation. The manual weighted average technique

calculates the percent area under each individual elevation and multiplying that area with

its corresponding elevation and summing up this elevation. Alternatively, it can also be

determined by calculating cumulative elevation of each zone, and then the elevation and

number of counts for each elevation grid are multiplied and cumulated. The cumulative

of the product of the number of counts and elevation is then divided by the cumulative

elevation of each zone. This study adopted the weighted average technique to determine

the mean hypsometric elevation of each elevation zone. The mean hypsometric elevation

41

of each zone is used as an elevation to which the base or reference station temperatures

are extrapolated for the calculation of degree days.

4.7.2 Temperature Lapse Rate and Degree Days

The model accepts either daily average temperature or both minimum and maximum

daily temperatures. These values can be input as basin wide or different values for each

zone. Although air temperature is a continuous field, usually point measurements are

recorded at each but distant meteorological station. In a mountain terrain air temperature

is significantly dependent on the elevation rather than the horizontal location. The

environmental temperature lapse rate is about 6.5 oC/km in the troposphere, which may

be used in the absence of any actual local data (Singh and Singh 2001).

The model can take input of one or several met stations. With the input of single station,

temperature values are extrapolated from the reference elevation of area (usually

elevation of the met station) to the mean hypsometric elevation of each zone using the

temperature lapse rate, which is change in temperature per unit of elevation. If the user

wants to use separate met station for each zone, the temperature values must have already

been lapsed with respect to the entered reference elevation as the program does not

accepts separate reference elevation for each zone. In this case it is better to use either

input from a good, reliable and true representative met station or prepare a single

synthetic station from data of multiple stations. If the elevation of the selected met station

is equal to the mean elevation of the study area then the possible errors in the lapse rate

are to some extent cancelled because of both upward and downward extrapolation

(Martinec 2007). Significant errors however may occur with too much difference in the

elevation of met station and mean elevation of the study area and in such cases correct

estimation of the temperature lapse rate becomes important.

Although the elevation of the only met station of the basin at Kalam is within the active

hydrological zone, it is well below the mean hypsometric elevation of the study area. Due

to higher elevation range of the study there is need to calculate the lapse rate of

temperature due to difference of elevation. For this purpose temperature records of few

other stations located outside the study area at various elevations are used to determine

42

the temperature lapse rates for different months. The altitudinal adjustment (ΔT) in the

model’s governing equation is computed through the following formula.

( )100

1.. hhT st −=Δ γ (6)

where: γ is the temperature lapse rate (oC per 100 m)

hst is the altitude of selected met station (m)

h is the mean hypsometric elevation of each elevation zone (m)

Because the average temperatures always refer to a 24 hour period starting at 6.00 hrs,

they become degree-days, T (oC.d). Degree-day factor ( ) can be determined by

comparing degree-day values (temperature values above a certain base temperature) with

the daily decrease of snow water equivalent. However, the data on variation of SWE is

rarely available. In the absence of any detailed data, the degree day factor can be

calculated from the following empirical relation (Martinec 1960):

a

w

saρρ

.1.1= (7)

where: is the degree day factor (cm/a oC/d), and sρ & wρ are densities of snow

and water respectively.

Density of snow usually varies from 0.3 to 0.55 gm/cc resulting in value of degree-day

factor in the range of 0.35 – 0.61, with lower value recommended for fresh snow and

snow under forest canopy. However, slightly higher values have also been reported in the

snow melt runoff modeling studies (Martinec 2007).

The degree-days factor is used to convert the number of degree-days, T (oC.d) in to the

daily snowmelt depth, M (cm) by:

TaM .= (7)

The degree-day factor does not account for the other components of the energy balance

43

notably the solar radiation, wind speed, and latent heat of condensation and its values are

extremely variable over the time because changing properties of snow significantly

influence the snow melting process.

4.7.3 Precipitation

The correct evaluation of true representative precipitation in mountain basins is a real

challenge as it usually has high variability depending on geographical location, elevation,

direction of air currents, height of mountain barriers, vegetation cover, etc. Unlike

temperature, which tends to change gradually, precipitation may not be continuous and it

may have abrupt and very high spatial variability. The estimates of spatial precipitation

are also highly uncertain unless a good network precipitation gauges exist. In this area of

HKH region, the precipitation is caused by different weather systems during different

seasons of a year and varies from place to place because of highly rugged topography of

the HKH mountains. Arora et al 2006 studied the spatial, altitudinal and seasonal

variability of rainfall in the Chenab basin of the Himalayan region and found elevation,

distance and direction of wind currents to be equally important in explaining the

variability in annual rainfall distribution.

The study area possesses only one whether station (Kalam) inside its boundaries. The out

side met stations, except Saidu and Shandur, are far away from its boundaries. Moreover,

the lower southern part of the study area is located in the monsoon belt, whereas the

outside stations are located in relatively dry zone. The example is the north-western part

of Northern Areas where despite higher elevations the area is relatively dry.

The Shandur, Kalam and Saidu met stations are not only located almost at the three ends

(head, centre and tail) of the basin but also at varying elevations of 3719, 2103 and 961 m

amsl respectively. The close examination of the data of these three stations revealed only

14 % variation in the mean annual precipitations of Saidu and Kalam and most of the

rainstorms occur at almost similar times. The Shandur area however is considerably dry

because of its location outside the monsoon belt, but the rainstorms here also tend to

occur at the same times. Hence, it is assessed better to use the precipitation data of these

three stations separately for different zones rather than conduct analysis to determine the

44

precipitation lapse rate, as in case of temperature. Moreover, instead of synthesizing the

data of these three stations, the precipitation data of Saidu station is used for Zone-A area

as it is very close to this zone and also their elevation is quite closer. Similarly, Zones-B,

-C and -D areas very closely match the characteristics of the Kalam met station. The

characteristics of the last zone (Zone-E) are best matched by the Shandur met station,

which is located further north of the basin. Hence, precipitation data of this station is used

for the last zone area.

Critical temperature determines whether the precipitation is in the form of rain or

snowfall. Usual values range from 0 – 3 oC with higher values in snow accumulation

periods, but it can never be less than 0 oC (Martinec 2007). This parameter is more

important for year round simulations which model both snow accumulation and snow

ablation periods. For precipitation identified to be snow, model accounts its delayed

effect on runoff generation differently for snow covered and snow free areas. The new

snow that falls over the previously snow covered area is assumed to become part of the

seasonal snow pack and its effect is included in the normal depletion curve of the snow

coverage. The new snow falling over the snow free area is considered as precipitation to

be added to snowmelt, with this effect delayed until the next day warm enough to

produce melting. However, it is difficult to differentiate exactly between rain and snow

because the temperature used is the daily average while precipitation may occur at any

time during the day and that particular moment may be warmer or colder than the

assigned temperature value.

4.7.4 Snow Area Extent

Information on the temporal, spatial and altitudinal distribution of snow cover in the area

of interest is the heart of snowmelt runoff modeling with the SRM. To estimate snow

cover in high mountain rugged terrain, satellite remote sensing is more suitable

alternative than the field measurements. The eight daily, maximum snow extent

(MOD10A2) snow cover product of the MODIS instrument was processed to determine

the altitudinal, spatial and temporal distribution of snow cover in the study area using

GIS and remote sensing techniques. Since each MOD10A2 snow cover product gives

45

snow cover for the eight days, an abrupt change in snow cover is usually observed from

the map of one time period to another. This effect was smoothed by taking snow cover of

these products for only two middle days and estimating it for the rest six days (three days

before and three days after) using linear interpolation from the previous and next image.

Few pixels of inland water and lake ice in some of the images were simply neglected and

any image with considerable cloud cover was considered as an outlier and excluded from

the Snowcover analysis. The Snowcover for that time period was also estimated by liner

interpolation between the two time images.

4.7.5 Runoff coefficients

The runoff coefficient takes care of the losses from the basin’s available water resources

(rain + snow) during its journey to the outlet. The average value of runoff coefficient for

a particular basin is given by the ratio of annual runoff to annual precipitation. The

comparison of historical precipitation and runoff ratios provide starting point for

estimation of runoff coefficient. However, more often it varies throughout the year as a

result of changing temperature, vegetation and soil moisture conditions. Moreover, very

high uncertainty involved in the measurement of true representative precipitation poses

serious difficulties in its correct estimation. For this reason, among SRM parameters, the

runoff coefficient is the primary candidate for adjustment during model calibration.

Runoff coefficient is usually higher for snow melt than for rainfall due to effect of cold

water soil hydraulic conductivity.

4.7.6 Recession Coefficient

Stream flow recession represents withdrawal of water from the storage with no or little

inflow. Analysis of historical discharge data is usually a good way to determine recession

coefficient (k). The discharge on a given day (Qn) is plotted on the logarithmic scale

against the value of discharge on the following day (Qn+1) as shown in the Figure 4.2. An

envelop is drawn to enclose most of the points and the lower envelop line represents the

extreme discharge decline, i.e. the recession without any partial delay by possible

precipitation or snowmelt.

46

Figure 4.2 Recession flow plot Qn vs Qn+1 for Swat river basin

For a snow fed basin, the value of recession coefficient changes with time due to changes

in the characteristics of the drainage basin. For example, the changes in the snow covered

area and depth of snow pack with time influence the recession trend of the basin. The

recession coefficient will always be less than unity (normally greater than 0.9), and also

not constant but may increases with decreasing discharges and is given by:

ynn xQk −

+ =1 (9)

where: the constants x and y are determined by solving the above equation for

two Qs and Ks from the Figure 4.2.

Theoretically, k can exceed the value of one in some cases of very small discharges in

large basins but practically large basins usually have large discharges. However, the

model avoids such cases by preventing k values from exceeding 0.99. The estimated x

and y values must fulfill this condition, Qmin > x1/y. Recession coefficient can also be

adjusted by comparing the measured and simulated flows during calibration.

47

4.7.7 Rainfall Contribution Area and Time Lag

Snow pack is usually dry before and during early snowmelt season and most of the rain

falling on snow pack is normally retained by it. Only snow free area contributes to

rainfall runoff during that period. However, at some later stage the snow pack becomes

wet and the rain falling afterwards can flow as runoff. The user has to decide which time

periods snow pack in a particular area and height will be dry and assign that input to the

model accordingly.

For large basins with multiple elevation zones, the time lag changes during the snowmelt

season as a result of changing spatial distribution of snow cover with respect to the basin

outlet. Generally the time lag in a basin increases as the snow line retreats. If there is

uncertainty, the time lag can be adjusted in order to improve the synchronization of the

measured and simulated peaks of average daily flows.

4.8 Model Calibration and Verification

The mountain hydrology is mainly the function of topography and meteorology (Ahmad

and Joya 2003). The knowledge about interaction of these components of mountain

hydrology is generally limited and qualitative in nature. Therefore there is more reliance

on river flow data of the mountain areas which largely represent the hydrological

responses of all the existing topographical factors and meteorological events taking place

in the mountain regions (Sing & Kumar 1997; Siddiqui et al 2003).

The SRM normally does not require calibration as its input parameters are generally

derived from the field data and historical records through physical laws and empirical

relationships. However, gathering of all the required data is only a dream for a highly

rugged mountain terrain in a country like Pakistan, where inaccessibility and lack of

resources generally limit collection of such data. Hence, calibration of the model and

some adjustment of few input parameters is quite necessary and in fact the user gains

more confident over the simulation results. Therefore, the SRM was calibrated against the

daily river inflows of year 2003 and was validated by backward as well as forward

verification for the daily flows of years 2002 and 2004.

48

The accuracy of the model calibration is judged from the two well established accuracy

criteria, the coefficient of determination (R2) and the deviation of runoff volumes (Dv),

which are described earlier in the equations (4) and (5) respectively.

4.9 Model Simulations

Once the model is adequately calibrated, it can be run for a number of scenarios as per

requirements. One of the major objectives of this study is to quantify the snowmelt and

rainfall runoff, which can be achieved by the following two ways; i.e. either by setting

the runoff coefficient for rainfall as zero or setting the SRM to run with zero input of

precipitation. Practically it is not possible as this precipitation is the only source of

snowfall. But, since the SRM does not take input of precipitation to be converted as

snow, instead it takes the input of daily snowfall from outside source such as remote

sensing, the results are unaffected.

The other major objective is to relate the snowmelt runoff with the observed snowcover

for different times. When the calibrated model is run differently for three years, it takes

temperature input of that particular year and in this way the effect of temperature is also

incorporated in snowmelt runoff generation. Since temperature is the only source of

energy to melt the available snow, it can not be set to zero as in case of precipitation.

Instead the effect of temperature was normalized (equalized) by assigning the average

values of daily minimum and maximum temperatures for all the three runs (2002-04),

rather than their own temperature data. This way whatever the effect of temperature is, it

remains the same for each run and only the effect of varying snowcover is simulated. The

simulated runoff achieved this way is then related with the average observed snowcover

for different months.

4.10 Model development

As described in the preceding paragraph, the simulations with the input of normalized

temperature are run to derive the variation of snowmelt runoff only as the function of

snowcover change. These daily runoffs are then averaged to compute the average

49

monthly runoff, which are plotted against the average monthly observed snowcover

obtained similar way and the best fit regression model is developed for forecasting the

one variable from the other.

Similarly, such regression models are also developed by relating the snowcover with the

actually observed river runoff. The river runoff however also contains the rainfall runoff

component, which has very high variability as compared to snowcover and snowmelt

runoff. Even then the prediction models developed this way can be a good tool for a

rough estimate of runoff from the snowcover.

50

C H A P T E R F I V E

R E S U L T S A N D D I S C U S S I O N

5.1 Outline

The results and discussion chapter has been divided into four main sections. The 1st part

discusses the results of derived input parameters while the 2nd component covers

altitudinal, spatial, and temporal distribution of snowcover estimated through remote

sensing in the Swat River Basin. The 3rd part presents snowmelt runoff modeling results

including SRM calibration, verification and simulations. The last segment is dedicated to

the development of relationship between the observed river flows as well as simulated

snowmelt runoff with the computed snowcover for different time intervals.

5.2 Parameter Estimation

The analysis started with delineation of river network and watershed boundaries of the

study area from the SRTM DEM data using ArcHydro GIS software. Figure 5.1 presents

the delineated river network and basin boundary of the whole Swat Basin and study area

(upper Swat basin). The total area of the basin is 5713.38 sq. km with a mean

hypsometric elevation of 2727.2 m. Since the SRM represents a semi-distributed

approach, considering each catchment section with similar hydrological characteristics as

a single unit (hydrological response unit, HRU), the basin is divided into five elevation

zones (Zone-A to Zone-E) keeping in view the available elevation range of 686 m – 5808

m, as described in the Figure 5.2 The area occupied by each elevation zone is 23.16,

23.10, 19.47, 26.99, and 7.28 % of the total basin area respectively. The plot of

cumulative area versus elevation (area-elevation curve) is presented in the Figure 5.3.

The mean hypsometric elevation for each elevation zone is 1133.42, 1956.63, 3014.76,

4007.57, 4726.55 m respectively. The mean hypsometric elevation of each zone is used

as an elevation to which the base or reference station temperatures are extrapolated for

the calculation of degree days. The elevation distribution depicts northern part of the

basin with high mountainous terrain having elevation range of 1500 – 5808 m, while the

southern part is relatively flat with elevation ranging from 686 – 2500 m a.s.l.

51

Figure 5.1 Delineated river network and watershed area of the Swat River Basin

52

Figure 5.2 Elevation zones, their areas & mean hypsometric elevation.

Figure 5.3 Area-elevation (hypsometric) curve of the upper Swat river basin

53

Due to the sensitivity of temperature to elevation and higher elevation range of the study

basin, the temperature data of its sole met station at Kalam has been extrapolated. Figure

5.4 presents the average daily minimum and maximum temperature at Kalam. The

temperature lapse rates for different months are calculated using the temperature records

of few other stations located outside the study area at various elevations (Figure 5.5).

The average monthly temperature of all these met stations is plotted against their

elevations and the best fit regression line is drawn. A very high linear correlation can be

found between the temperature and elevation for all the met stations throughout the year.

Figure 5.6 presents these regression models which are used to compute the values of the

temperature lapse rate for different months. The computed lapse rates are 0.68, 0.69,

0.69, 0.67, 0.70, 0.73, 0.62, 0.61, 0.64, 0.68, 0.66, and 0.65 oC / 100 m for January to

December months respectively. These lapse rates were input to the SRM for all its

scenarios. However, for precipitation there is no need of such a practice. Instead as

mentioned earlier, precipitation data of Saidu is used for Zone-A area, Kalam for Zones-

B, -C, and –D areas and Shandur for Zone-E area. The recession coefficient was

calculated from the discharge data of 2001-2005, whereas the other parameters were

adjusted during model calibration and verification.

-15-10

-505

101520253035


Month

Ave

rage

Dai

ly T

empe

ratu

re (o

C)

M aximum Minimum

Figure 5.4 Average daily minimum and maximum temperature at Kalam

54

Figure 5.5 Meteorological stations used for computation of temperature lapse rate.

y = -0.0068x + 13.999R2 = 0.985

y = -0.0069x + 16.205R2 = 0.9763

-20

-15

-10

-5

0

5

10

15

0 1000 2000 3000 4000 5000

Elevation (m)

Tem

pera

ture

(oC

)

Jan Feb

Figure 5.6 (a) Relationship between temperature and elevation for Jan and Feb months

55

y = -0.0069x + 21.074R2 = 0.9747

y = -0.0067x + 26.234R2 = 0.9752

-15-10-505

10152025

0 1000 2000 3000 4000 5000

Elevation (m)

Tem

pera

ture

(oC

)

Mar Apr

Figure 5.6 (b) Relationship between temperature and elevation for Mar and Apr months

y = -0.007x + 31.11R2 = 0.9755

y = -0.0073x + 36.314R2 = 0.9539

-505

101520253035

0 1000 2000 3000 4000 5000

Elevation (m)

Tem

pera

ture

(oC

)

May Jun

Figure 5.6 (c) Relationship between temperature and elevation for May and June months

y = -0.0061x + 36.113R2 = 0.8926

y = -0.0062x + 36.721R2 = 0.8866

0

5

10

15

20

25

30

35

0 1000 2000 3000 4000 5000

Elevation (m)

Tem

pera

ture

(oC

)

Jul Aug

Figure 5.6 (d) Relationship between temperature and elevation for Jul and Aug months

56

y = -0.0068x + 27.148R2 = 0.9558

y = -0.0064x + 32.896R2 = 0.937

-10-505

1015202530

0 1000 2000 3000 4000 5000

Elevation (m)

Tem

pera

ture

(oC

)

Sep Oct

Figure 5.6 (e) Relationship between temperature and elevation for Sep and Oct months

y = -0.0065x + 15.784R2 = 0.989

y = -0.0066x + 21.005R2 = 0.982

-20-15-10-505

101520

0 1000 2000 3000 4000 5000

Elevation (m)

Tem

pera

ture

(oC

)

Nov Dec

Figure 5.6 (f) Relationship between temperature and elevation for Nov and Dec months

5.3 Snowcover Estimation

Snowcover estimation is an integral part of hydrological modeling as it provides basic

information for calculating snowmelt runoff from any snow-fed basin. The areal extent of

snowcover is two-dimensional information and is an important variable for snowmelt

runoff computations, because each daily melt water volume stored in a basin is obtained

as a product of this area and the associated snowmelt depth. Determining contribution of

snowmelt runoff to total river runoff has great practical significance as snowmelt runoff

is more dependable source of fresh water. Unfortunately, in the high mountainous terrain

57

with an extreme and harsh climate, such as HKH region of Pakistan, where highly rugged

terrain provide limited accessibility and little ground control, it is very difficult to

monitor metrological data and snowcover information accurately on a continuous basis.

The ruggedness further complicates the definition of snow line owing to occurrence of

snow in patches. In such circumstances satellite remote sensing has great value and seems

to be the only viable alternative, as it can provide repetitive data on snow area extent at

different, regular time intervals.

The study utilizes MODIS snowcover products to estimate snow area extent in the Swat

river basin of Pakistan. The MODIS 8-daily (level 3, version 5) maximum snow extent

composite snowcover product (MOD10A2) was processed in a GIS environment to

determine spatial and temporal variation of snow cover in the basin. The MOD10A2

snow cover product contains information on the presence or absence of a number of

classes (Table 3.4). The study area is covered by the h23v05 tile of the MODIS

sinusoidal grid. In all 140 MOD10A2 images of that grid, distributed over three years

period (Jan 2002 to Dec 2004), were analyzed. Figure 5.7 presents a selected sequence of

time series GIS processed snowcover maps of the basin for the three years.

The gradual or sometimes abrupt increase in areal extent of snowcover during winter

months and its gradual decrease during the subsequent summer season, a typical

phenomenon of the mountain snow hydrology, is prominent in all the maps of Figure 5.7.

The MOD10A2 snowcover product was further processed using GIS techniques to

determine altitudinal distribution of the snowcover. Figures 5.8 present temporal and

altitudinal variation of snowcover for the five elevation zones during three year study

period, whereas Figure 5.9 shows three years average conditions.

The analysis and visual observation of the generated snowcover maps and developed

graphs reveal that snowfall and subsequent snowmelt in the Swat river basin is highly

variable in terms of altitude, space and time.

Winter snowfall usually starts by the mid to late September initially at higher elevations

and snow area may be increased abruptly from less than 2% in August to about 10 – 20 %

of the total basin area. Occasional and unpredictable rainstorms in September and

58

October months sometimes bring immediate and abrupt but significant increase in

snowcover area and snowcover may cover about 45% of the total basin area by the end of

October. However, the following few weeks are unable to maintain that tempo and

consequently some decline in snowcover is usually observed in many cases due to

subsequent and immediate melting of that fresh and temporary snowcover. The main

winter months (Nov – Feb) generally bring in most of the snowfall and snowcover keeps

accumulating reaching its peak area by the end of January or early February covering

about 58 – 64 % of the basin area. Significant snowfall at lower elevations is also

witnessed during these main winter months as the snowcover gets extended down to

valleys in southern parts and snowline may reach at elevations less than 1500 m.

However, this snowcover at lower elevations completely disappears by mid to late March

when snowmelt season starts.

At higher elevations above 3500 m a.s.l. snow continues to fall even in March and April

months as can be observed in Figures 5.8 d and 5.8 e when snow area in 2003 was

increased in both these months. Although some occasional snowfall has also been

witnessed during the main summer months (May – Aug) particularly when continued

rainfall significantly brings down the temperature. However, it rarely happens as snow in

these months does not last for long but rather melts very soon. So, practically this

occasional summer snowfall does not make any difference and can easily be neglected.

Hence, this four month (May – Aug) period can be termed as purely snowmelt season

during which snowcover gradually declines.

Based on the three years daily snowcover observed through remote sensing, the average

monthly snowcover in the upper Swat river basin can best be described by the fourth

order polynomial function with highest peak in February and lowest peak in August

months as depicted in the Figure 5.9, which also compares average monthly snowcover

among three years. Figures 5.8 (e) and 5.9 depict very high variability of mid September

to late October snowcover among the three years period due mainly to uncertainty and

variability of precipitation and marginal temperatures during this period.

59

Figure 5.7 (a) Temporal variation of snowcover in the upper Swat basin (Jan - Apr)

60

Figure 5.7 (b) Temporal variation of snowcover in the upper Swat basin (May-Aug)

61

Figure 5.7 (c) Temporal variation of snowcover in the upper Swat basin (Sep – Dec)

62

05

1015202530


M onth

Dai

ly S

now

cove

r(%

of

Zon

e-A

Are

a)

2002 2003 2004 Average

Figure 5.8 (a) Temporal variation of snowcover in the Zone-A (686 – 1500 m a.s.l)

0

10

20

30

40

50


Month

Dai

ly S

now

cove

r(%

of

Zon

e-B

Are

a)

2002 2003 2004 Average

Figure 5.8 (b) Temporal variation of snowcover in the Zone-B (1501 – 2500 m a.s.l)

0

20

40

60

80

100


Month

Dai

ly S

now

cove

r(%

of

Zon

e-C

Are

a)

2002 2003 2004 Average

Figure 5.8 (c) Temporal variation of snowcover in the Zone-C (2501 – 3500 m a.s.l)

63

0

20

40

60

80

100


Month

Dai

ly S

now

cove

r(%

of

Zon

e-D

Are

a)

2002 2003 2004 Average

Figure 5.8 (d) Temporal variation of snowcover in the Zone-D (3501 – 4500 m a.s.l)

0

20

40

60

80

100


Month

Dai

ly S

now

cove

r(%

of

Zon

e-E

Are

a)

2002 2003 2004 Average

Figure 5.8 (e) Temporal variation of snowcover in the Zone-E (4501 – 5808 m a.s.l)

010203040506070


Month

Dai

ly S

now

cove

r(%

of

Bas

in A

rea)

2002 2003 2004 Average

Figure 5.8 (f) Temporal variation of snowcover in the whole basin

64

2002 2003 2004 Average

y = -0.0368x4 + 1.1749x3 - 11.052x2 + 28.518x + 33.747R2 = 0.9895

0

10

20

30

40

50

60

70

1 2 3 4 5 6 7 8 9 10 11 12


Ave

rage

Mon

thly

Sno

wco

ver

(% o

f Bas

in A

rea)

Figure 5.9 Comparison of average monthly snowcover variation in different years


runoff is often insignificant. Flow during the winter season is usually augmented from

surface flow due to seasonal rains, sub-surface flow, and ground-water contribution and

is termed as the base flow. Unlike snowfall, snowmelt usually progresses gradually and

smoothly and is more easily predictable. The summer snowmelt normally gets

momentum in the month of March which also brings in some new snows at times of cold

waves accompanied with precipitation particularly at higher elevations. The net outcome

however is towards snowmelt.

At first the snow starts disappearing rapidly from valleys at southern parts of the basin

and from elevations less than 2500 m in early March, which gradually widens and the

snowline retreats upward as the summer season progresses and temperature gets

increased. At elevations greater than 4500 snowmelt starts in late April and continues till

mid September. Starting in March, the rate of snowcover retreat reaches at its peak in

June and thereafter declines rapidly up to August and consequently snowmelt runoff also

reaches at its peak in late June or early July and thereafter declines gradually up to

August. During July to mid September temperatures are usually sufficient enough to melt

65

the snow and snowmelt is mainly the function of available snow, which is mostly

concentrated at highest elevations and is about to finish. Minimum snowcover is usually

observed in the late August until the new snowfall season starts in September. During the

monsoon season, the peak snowmelt runoff sometimes is augmented by monsoon rains to

produce higher discharges and occasional peak floods sometimes destroying the

infrastructure.

The three years snowcover monitoring with remote sensing shows that under conducive

climatic conditions, the maximum snow area extent may cover about 64 % of the total

area of the basin during January-February to as low as 1.7 % in late August during the

snowmelt season. However, not always the same area receives snowfall. Spatial analysis

of the three years snowcover maps in a GIS environment (Figure 5.10 and Table 5.1)

show that about 79.14 % of the area received snowfall at any time during 2002 – 2004.

This area can be termed as area which generally accommodates temporary and seasonal

snowfall. A handful of 20.72 % never received snowfall during that period, while only in

0.14 % (8.187 sq. km) area of the basin the snowcover remained in tact and could not be

melted during that three years period. This area can be termed as permanent snow. It

means that the entire basin predominantly accommodates temporary and seasonal

snowcover, which is an important element of the hydrological cycle of the basin and

major contributor to the basin’s fresh water resources.

PARC and ICIMOD (2005) identified six types of 200 glaciers present in the basin

covering an area of about 195.84 sq. km using the single time Landsat-7 ETM+ Imagery

obtained in September-October 2001 as shown in Figure 5.11. The size of these 200

glaciers varied from 0.06084 sq. km to 8.02 sq km. The number of glaciers having size

less than 0.25 sq. km were 46 covering an area of 7.43 sq. km.

The present study however observed minimum snowcover of only 97.13 sq. km in the 1st

week of September 2003 during the three years study period and only 8.187 sq. km

permanent snowcover, which could not be melted during that period. The location of this

permanent snowcover is also by and large different than the glacier location identified by

the PARC and ICIMOD. The difference in glacier area between the two studies may be

66

attributed to the reasons; either they may have overestimated the glacier area as in Sep-

Oct months winter snowfall gets started; or these glaciers might have retreated during the

meantime. The other reason may be underestimation of this study due to coarse resolution

of 500 m (0.25 sq. km), which might have overlooked smaller sized glaciers. However,

one thing seems quite clear that significant flow does take place from the glacier melt

particularly in July – Sep months.

Figure 5.10 Permanent and temporary/seasonal snow cover

67

Table 5.1 Area under permanent and temporary snow cover for three study years

Year Permanent Land (sq. km)

Temporary Snow (sq. km)

Permanent Snow (sq. km)

2002 1617.960 (28.32) 4043.269 (70.77) 52.150 (0.91)

2003 1809.644 (31.67) 3888.715 (68.06) 15.021 (0.26)

2004 1675.184 (29.32) 3981.328 (69.68) 56.867 (1.00)

2002-2004 1183.646 (20.72) 4521.546 (79.14) 8.187 (0.14)

Figure 5.11 Glacier location and extent as identified by PARC & ICIMOD 2005.

68

5.4 Snowmelt Runoff Modeling

5.4.1 Calibration and Verification Results

After derivation of the variables and parameters necessary for model input, the SRM was

run and calibrated for the river flows of year 2003. During the process of calibration

deficiencies in some of the input parameters were identified which were adjusted

accordingly. The WinSRM program includes a good facility of graphical display of the

simulated and observed hydrographs of the river runoff. This visual examination at the

first glance shows whether the simulation adequately represents the flow conditions or

not. Additionally, the SRM uses two well established and statistically valid accuracy

criteria, namely, the coefficient of determination (R2) and the deviation of runoff volume

(Dv) to evaluate model calibration in quantitative terms. Again the model displays the

results of both these criteria terms and there is no need of any manual calculations or

graphical representations.

After calibrating the model for 2003 river flows, the model was run for 2004 to verify the

calibration by inputting the daily records of temperature, precipitation and snowcover for

that year. Few deficiencies in some of the input parameters were observed and these

parameters were adjusted once again to match the flow regimes of both years. Similarly

the model was verified for 2002 year with temperature, precipitation and snowcover

inputs of its own and input parameters were further refined. With this forward as well

backward verification, the SRM is ready for any simulations as per requirements.

Figures 5.12, 5.13 and 5.14 show the plots of the observed and simulated river flows for

years 2002, 2003 and 2004 respectively, while Table 5.2 presents the simulation statistics

and calibration results of the two accuracy parameters. The coefficient of determination is

79.60, 82.37 and 80.15 for years 2002, 2003 and 2004 respectively and the volume

difference for these years is – 2.815, - 4.077 %, and 3.202 % respectively. The minus sign

indicates overestimation of simulated runoff by the SRM. These calibration and

verification results can be termed quite good and well under acceptable limits as SRM

have been applied in the past with 60 % and ± 8 % values of both these criteria

69

respectively (Martinec 1995). Hence this calibrated and verified model can be used for

simulation and forecasting.

Figure 5.12 Simulated and observed river flows for calibration year of 2003

Figure 5.13 Simulated and observed river flows for verification year of 2004

70

Figure 5.14 Simulated and observed river flows for verification year of 2002

Table 5.2 Year round simulation statistics for different study years

Simulation Year

Measured Runoff Volume

(106 m3)

Simulated Runoff Volume

(106 m3)

Volume Difference

(%)

Coefficient of Determination (R2)

2002 4465.183 4590.864 - 2.815 0.7960

2003 5742.862 5977.021 - 4.077 0.8237

2004 5874.324 5686.182 3.202 0.8015

5.4.2 Simulation Results

Keeping in view the specific objectives of this study three scenarios have been

developed. The first scenario runs the model with each year’s own data and computes the

daily runoff. The second scenario runs the model for each year’s data but with no rainfall

to calculate the respective share of two runoff components i.e. snowmelt and rainfall

71

runoff. The third scenario runs the model for each year with no rainfall and with

normalized (putting historical average temperature values rather than each year’s own

temperature data) temperature. This scenario is developed to normalize the effect of

temperature. It means whatever the effect of temperature is, it remains the same for each

year and only the effect of snowcover change on snowmelt runoff is simulated.

The distribution and share of simulated potential runoff for different zones is shown in

the Figure 5.15. These components of accumulated runoff are the total (potential) depth

of water which could be generated at the source. The contribution of new snow is

computed from the input of precipitation and critical temperature, which determines the

form of precipitation. The runoff water reached at the gauge station however will be

significantly lower due to runoff losses. Figures 5.16, 5.17 and 5.18 present the simulated

snowmelt and rainfall runoff components for the three study years at the Chakdara gauge

station computed through the SRM.

The graphs of Figure 5.15 are just to have general idea of the contribution offered by the

three runoff components. Very similar trend, in terms of start and end times, is quite clear

in Figures 5.16, 5.17 and 5.18 as well. These figures clearly indicate dominancy of

snowmelt runoff as the basin is predominantly a snow-fed. However, there is also

significant contribution of rainfall runoff particularly in Mar – May and Jul – Sep

periods, whereas the rainfall contribution of the rest six months is considerably less.

Although there occurs higher precipitation in the winter months but it usually falls in the

form of snow due to low temperatures. June on the other hand is the driest month of year

so rainfall contribution during this month is also very low. However, due to maximum

temperatures during June, it receives highest snowmelt runoff. Since, snowmelt runoff in

a snow-fed basin is mainly the function of temperature and available snowcover therefore

it responses accordingly with the change in temperature and available snowcover.

72

Figure 5.15 Cumulative runoff components in various zones for the simulation year 2004 (Red is initial snow, green is new snow, and blue is contribution of rain).

73

0

100

200

300

400

500

600

700

800


Month

Ave

rage

Dai

ly D

ischa

rge

(Cum

ec)

Snowmelt Discharge Rainfall Discharge

Figure 5.16 Computed snowmelt and rainfall runoff components for the Year 2002

0

100

200

300

400

500

600

700

800


M onth

Ave

rage

Dai

ly D

ischa

rge

(Cum

ec)



74

0

100

200

300

400

500

600

700

800


Month

Ave

rage

Dai

ly D

ischa

rge

(Cum

ec)



The snowmelt runoff from Dec – Feb mostly remains in between 30 – 50 m3/sec due to

less variability of temperatures in these three months. The summer snowmelt runoff

however has very high variability among the months as well as among the years.

At elevations less than 1500 m (Zone-A area, which is located in the active monsoon

belt) flow is mainly coming from rainfall runoff, which contributes almost 70% of the

total zonal runoff. The contribution of snowmelt runoff in this elevation zone is received

during main winter months only.

In the areas having elevation range of 1500 – 2500 m, rainfall contribution starts by mid

March to mid December, during rest of the period it falls as snow. Snow continues to

melt almost throughout the year in this zone, however during December to January; its

contribution is very little and is mainly coming from fresh snow.

In Zone-C area (elevations range of 2500 – 3500 m), snowmelt usually starts in mid

March and continues till the end of November with almost similar trend of rainfall runoff.

At higher elevation range of 3500 – 4500 m (Zone-D area) snowmelt commences in late

75

April and continues up to September and at further high elevation range of greater than

4500 m, the snowmelt runoff is only generated by the mid of May to mid September. The

rainfall contribution in both these zones is mainly received in monsoon season.

Precipitation during rest of the period is in the form of snowfall.

On the basis of three years simulation results, the study basin is predominantly a snow-

fed as the annual snowmelt runoff contribution to the total runoff may ranges from 65 –

75 %. Figure 5.19 shows the average contribution of the two runoff components

(snowmelt and rainfall) for each month to the total runoff from the basin. About 65.5 %

of the total runoff (45.9 % snowmelt and 19.6 % rainfall) is generated in the four main

summer months (May – Aug).

The results further suggest that about 30 – 60% of the total rain fall runoff occurs in

monsoon season (Jul – Sep) and about 25 – 50 % in Mar to May period. Figure 5.20

presents the average share (in per cent) of each runoff components to the total monthly

runoff generated in different months. The average contribution of snowmelt runoff to the

total monthly runoff is 98.5, 91.2, 61.3, 61.6, 70.8, 83.0, 67.6, 53.3, 61.5, 73.1, 82.5, and

86.7 % for Jan – Dec months respectively.

The average monthly snowmelt discharge from the basin estimated through SRM can be

described by the third order polynomial functions for the two halves of a calendar year,

which are presented by the Figures 5.21 and 5.22 for January to June and July to

December respectively. Snowmelt runoff reaches at its peak by the end of June and

declines gradually in both ways i.e. before and thereafter.

76

0

4

8

12

16

20

24

Jan Feb Mar Apr May Jun Jul Aug Se p Oct Nov Dec

Month

Ave

rage

Mon

thly

Con

trib

utio

n to

T

otal

Run

off (

%)


Figure 5.19 Average contribution of the two runoff components to the total runoff generated from the basin.

0

10

20

30

40

50

60

70

80

90

100


Month

Ave

rage

Mon

thly

Sim

ulat

edD

ischa

rge

(%)


Figure 5.20 Contribution of two runoff components to the total monthly runoff

77

y = 0.7517x3 + 10.356x2 - 37.49x + 56.917R2 = 0.9905

050

100150200250300350400

1 2 3 4 5 6

Calendar Month Number

Ave

rage

Mon

thly

Sno

wm

elt

Disc

harg

e (C

umec

)

Figure 5.21 Average monthly distribution of snowmelt runoff in Jan – Jun months

y = -3.6406x3 + 50.159x2 - 235.08x + 425.97R2 = 0.9991

0

50

100

150

200

250

300

7 8 9 10 11 12

Calendar Month Number

Ave

rage

Mon

thly

Sno

wm

elt

Disc

harg

e (C

umec

)

Figure 5.22 Average monthly distribution of snowmelt runoff in Jul – Dec months

78

5.5 Relationship of Snow Area Extent with River Discharge and Snowmelt

Runoff

The snowfall and corresponding snowmelt in a particular river basin are mainly the

function of topography and meteorology. The topographical factors may remain

unchanged while the meteorological factors may have very high temporal, spatial and

altitudinal variability. Fortunately the SRM takes input of daily snowcover determined

through remote sensing and models only the snowmelt process by taking input of

temperature and precipitation data. As per objectives of the study the SRM was run for

different scenarios discussed in preceding paragraphs. This section summarizes the

results of these scenarios and relates the simulated snowmelt runoff (without rainfall

runoff component achieved through normalized temperature input) and observed river

discharges at the Chakdara gauge station with the estimated snow area extent. The study

has quantified the monthly and seasonal cycles and variations of river flows and

simulated runoff with the snow area extent and identified a clear correspondence of river

flows and simulated snowmelt runoff to the change in snow area extent.

In practice normally the summer snowmelt runoff is mainly the function of winter

snowcover as snowfall hardly occurs during summer. Consequently, some researchers

(Rango et al 1977; Dey et al 1983; Tarar 1982) have related winter snowcover (normally

at the end of winter when snowfall is stopped) with the total summer season runoff

volume. However, winter snowcover can be related with summer snowmelt runoff when

there is no snowfall during the forecasting period and snowcover data is not available for

that period. Moreover, relating winter snowcover with summer runoff volume seems an

impractical approach as farmers as well as water resources planners are more interested

in daily, weekly or monthly discharges rather than total seasonal volume. Further, most of

the past studies have related snowcover with observed river runoff volume, which

incorporates the rainfall runoff component which usually have higher variability and low

predictability and may also have significant contribution particularly in rainfed areas and

the observed flows may not be the true representative of the associated snow cover.

79

This study on the other hand employs the daily records of snowcover which show that

snowfall can take place during eight months (Sep – Apr) and even minute amounts can be

observed during the four main summer months. Therefore relating winter snowcover with

total summer runoff volume will give correct estimates for only the four main summer

months (May – August) and may not be a wise approach as for as the other seasons and

monthly are daily estimates are concerned. Instead this study not only relates the daily

river discharges with the daily snow area extent but also develops prediction model for

the total runoff volume of the four main summer months. The study also relates the

simulated snowmelt runoff (excluding rainfall runoff component) with the snow area

extent. Relating snow area extent with the snowmelt runoff (excluding rainfall runoff)

rather than the observed river flows, which also contain rainfall runoff component, is

absolutely logical concept particularly for a snow-fed river basin. The contribution of

rainfall runoff may be added after the estimation to compute total river discharge. But the

major problem with this kind of approach lies in the uncertainty involved in the simulated

snowmelt runoff estimation. However, selection of the best model, accurate estimation of

model input parameters and adequate calibration and verification of the selected model

may significantly avoid this problem.

To achieve this, the study uses the SRM, which takes daily inputs of snowcover during

the whole simulation period and the effect of specified snowcover in the winter months is

superseded by the subsequent snowcover inputs on the following days. Hence, the

snowcover keeps changing as an outside input rather than modified by the model itself

during the different simulation time periods. Therefore the model results don’t totally

describe summer snowmelt runoff as the function of winter snowcover.

Due to unpredictability and high variability of weather, snowcover is subjected to vary

each year for different days and months. The same is also true for temperature, which

strongly influence the melting of snowcover. For example certain month of a year may

receive significantly different snowcover at its start during different years due to variation

or shift of weather, and this difference may sometimes exceed the average total

snowcover depletion in that month. In such cases there may be very high variability of

snowmelt runoff and estimates based on average conditions might contain some degree of

80

error. It means that there is need to relate the snowcover with runoff for a particular

month in both ways, i.e. for year to year variation and for during month or year variation.

Hence, it is really difficult to exactly relate snowcover with river discharge as snowmelt

runoff on each day of the year may be significantly different because of temperature and

available snowcover variation particularly in snowmelt season.

If we neglect the variation of temperature on a particular day of multiple years, we can

easily relate snowcover with runoff for that particular day and such an ideal situation will

lead development of 365 regression models (separate model for each day of the year).

However, this would be an absolutely impractical approach as no one likes such a large

number of models. Obviously, snowmelt runoff on a particular day is directly

proportional with the snowcover available on that day. The magnitude of this proportion,

however, may be significantly different for each day of a year due to temperature

variation. If we average the snowcover and corresponding runoff of each day of multiple

years then snowmelt runoff may behave systematically during the course of year and can

be related with the available average snowcover through regression models for different

time intervals (months or season). However, since this approach uses average conditions,

it may result in serious errors in extreme cases when a particular day or month receives

significantly different snowcover than the average snowcover due to drastic change or

shift in weather conditions.

As described earlier, this study utilizes snowcover and corresponding river discharge and

snowmelt runoff data of three years (2002 – 2004), which are averaged and then related

with the average snow area extent using the regression analysis. To quantify and analyze

the relationship of mean daily snow area extent with the observed river discharges and

simulated snowmelt runoff from the basin, their records for the period of 2002-2004 are

examined and compared in the Figure 5.23. It clearly indicates a definite response of

observed river discharges and simulated snowmelt runoff to seasonal snow cover

changes, i.e. an association of low stream flows with high snow area extent during the

winter season (Sep – Feb), an increase in discharge associated with a decrease of snow

area extent during the early summer (Mar – Jun), and decrease in discharge with

decreasing snowcover in the late summer, monsoon season (Jul – mid Sep).

81

0

10

20

30

40

50

60

70


Month

Av

Dai

ly S

now

cove

r(%

of B

asin

Are

a)

0

100

200

300

400

500

600

700

Av

Dai

ly D

isch

arge

(C

umec

s)

Snowcover River Discharge Snowmelt Runoff

Figure 5.23 Temporal distribution of average daily snow area extent, observed river discharge and simulated snowmelt runoff

Based on the three years time series data of MODIS snowcover products for the study

area, the regression models for various time periods are developed to estimate the average

daily river discharge and snowmelt runoff from the average daily snowcover available at

different times of the year. The estimated average daily snowcover is plotted against the

average daily observed river discharge and simulated snowmelt runoff computed with

zero input of rainfall and normalized temperature in the upper Swat river basin of

Pakistan. The results generally confirm a very strong linkage between the river flows and

snow area extent in the basin. The first of these relationships is presented in the Figure

5.24 for the early summer snowmelt season starting from March and extending up to

June. The relationship of average daily snow area extent with the observed daily river

discharge for this period can be described by the negative linear regression model as the

river discharge increases with decrease in corresponding snowcover. Its relationship with

the daily simulated snowmelt runoff is also negative but slightly different and is best

explained by the third order polynomial function. This difference between the two

regression models is due to incorporation and variation of rainfall runoff component in

the river discharges.

82

y = -0.0022x3 + 0.3374x2 - 23.303x + 628.54R2 = 0.9522

y = -9.2607x + 558.38R2 = 0.9507

0

100

200

300

400

500

600

0 10 20 30 40 50

Average Daily Snowcover (% of Basin Area)

Ave

rage

Dai

ly D

ischa

rge

(Cum

ec)

60

River Discharge Snowmelt Runoff

Figure 5.24 Relationship of average daily snowcover with average daily simulated snowmelt runoff and average daily observed runoff for March – June months.

It is worth mentioning that increase in river discharge is not due to decrease in

snowcover, rather decrease in snowcover is due to its melting, which ultimately increases

river discharge. Moreover, this inverse relationship is only true for the first part of the

snowmelt season during which availability of snowcover is generally not a limiting factor

and snowmelt runoff is largely the function of available temperature. But as the melting

season progresses, the available snowcover gets depleted and it starts limiting the

snowmelt runoff more than the temperature. Consequently, the relationship of the

snowcover with the snowmelt runoff and river discharge during this second part of the

snowmelt season (July – August) is completely different from that for the first part.

During this late summer monsoon period most of the temporary and seasonal snowcover

at lower to medium elevations is melted and snowmelt runoff mainly comes from the

permanent snow and glaciers of higher elevations. The relationship of average daily

snowcover with the average daily river discharge and snowmelt runoff for this second

part of snowmelt season (July – August) is shown in the Figure 5.25. Unlike the previous

model, this regression model shows positive relationship of average daily snowcover with

83

the two runoffs. Also, there is exchange in type of regression model between the two

relationships. The average daily snowcover now relates the simulated snowmelt runoff

linearly, whereas its relationship with the average daily observed river discharge can be

simplified by the second order polynomial function. The river discharge during the early

July month tends to remain constant but slightly and occasional greater river discharges

in mid or late July than the early July month are due to greater contribution of rainfall

runoff component during that period, otherwise snowmelt runoff decreases linearly

during the following period.

y = -14.331x2 + 210.35x - 276.33R2 = 0.8785

y = 65.898x - 99.637R2 = 0.9489

0

100

200

300

400

500

600

0 2 4 6 8


Ave

rage

Dai

ly D

ischa

rge

(Cum

ec)

10


Figure 5.25 Relationship of average daily snowcover with average daily simulated snowmelt runoff and average daily observed runoff for July – August months.

The relationship of the average daily snowcover with the river discharge and snowmelt

runoff for the remaining six moths (September – February), the winter season, is shown

in the Figure 5.26. This relationship shows a completely different condition. The two

runoff discharges are now decreasing with increase in snowcover. Apparently, this is an

unbelievable trend as snowcover has always positive impact on the snowmelt runoff. It is

again worth mentioning that snowmelt runoff still causes the snowcover to deplete but

84

due to onslaught of winter season, the new snowfall of the season has been started and

the net effect on the snowcover is positive resulting in increase of snow area extent. The

relationship of snowcover with both the runoffs for this time period is described by the

third order polynomial function.

y = -0.0012x3 + 0.1305x2 - 6.2054x + 179.88R2 = 0.8375

y = 0.0007x3 - 0.0647x2 + 0.6011x + 79.582R2 = 0.9414

020406080

100120140160180200

0 10 20 30 40 50 60 7


Ave

rage

Dai

ly D

ischa

rge

(Cum

ec)

0


Figure 5.26 Relationship of average daily snowcover with average daily simulated snowmelt runoff and average daily observed runoff for September – February months.

Apart from the above predictive regression models based on daily data, the total runoff

volume of the four main summer months (May – August) is related with the snowcover

observed at the start of May month for the purpose of seasonal water resources planning.

These four months are selected because there occurs hardly any snowfall during the May

– August period and their contribution to total river discharge is about 64%. To

accomplish this objective snowcover data at the start of May (1 – 8th May) month for few

other years was processed to develop prediction model of hydrological significance. The

previous models, developed for prediction of daily flows, were based on three years

average data, whereas this model is developed for incorporating snowcover and runoff

data of five years, i.e. 2001 – 2005.

85

The results of this seasonal model are summarized in the Figure 5.27. This model also

shows a very strong association of the observed river runoff volume of the four main

summer months with the late April snowcover. The relationship of snowcover at the start

of May with the total runoff volume for the four months is explained by the linear

function of regression model. The correlation coefficients for all the models are quite

high depicting strong correlations.

y = 0.1645x - 1.57R2 = 0.8966

0

2

4

6

8

10

0 10 20 30 40 50 60

Snowcover on May 1-8 (% of Basin Area)

May

-Aug

Run

off V

olum

e (B

CM

)

Figure 5.27 Prediction model for estimating May – Aug runoff volume from the snowcover estimated on May 1-8.

86

C H A P T E R S I X

C O N C L U S I O N S A N D R E C O M M E N D A T I O N S

6.1 Conclusions

The altitudinal, spatial and temporal distribution of snowcover in the Swat river basin of

Pakistan was successfully evaluated using remotely sensed satellite imagery of the

MODIS instrument, GIS techniques and snowmelt runoff modeling. A very high

variability of snowcover during the calendar year was observed. Snowfall usually starts

abruptly by the mid to late September increasing snow area extent from less than 2 % in

August to about 10 – 20 % by the end of September. More abrupt increase in snowcover

is observed in October month and snow area extent sometimes may cover 45 % of the

basin area. The main winter months (Nov – Feb) generally bring in most of the snowfall

and snowcover keeps accumulating reaching its peak area of about 64 % by the end of

January or early February. Significant snowfall at lower elevations is also witnessed

during this period as the snowcover gets extended down to valleys in southern parts and

snowline may reach at elevations less than 1500 m. Snowfall also continues in March and

April months at higher elevations but the net result during this period is towards

snowcover depletion due to its greater melting at the lower elevations. The occasional

and very little snowfall at the highest elevations during the main summer months (May –

August) does not have any practical value therefore this period can be termed as purely

snowmelt season during which snowcover gradually declines from around 40% at the

start to less than 2% by the end of August.


runoff is often very low. Unlike snowfall, snowmelt runoff usually progresses gradually

and smoothly and is more easily predictable. The summer snowmelt normally gets

momentum in the month of March and increases linearly from around 30 – 60 m3/sec to

more than 400 m3/sec to as high as 760 m3/sec in late June or early July. Snowmelt

runoff thereafter declines gradually up to December reducing to 30 - 50 m3/sec. The

December – February snowmelt runoff normally tends to remain same. The July – mid

87

September runoff is believed to be coming from the melting of permanent snow and

glacier melt at the highest elevations as most of the snowcover at lower to medium

elevations is finished. The runoff of the following period is primarily coming from the

fresh snowfall precipitating in these months.

On the basis of three years simulation results, the study basin is found predominantly a

snow-fed as the annual snowmelt runoff contribution to the total runoff may ranges from

65 – 75 %. About 65.5 % of the total runoff (45.9 % snowmelt and 19.6 % rainfall) is

generated in the four main summer months (May – Aug). The results further suggest that

about 30 – 60% of the total rain fall runoff occurs in monsoon season (Jul – Sep) and

about 25 – 50 % in Mar to May period. The average contribution of snowmelt runoff to

the total monthly runoff is 98.5, 91.2, 61.3, 61.6, 70.8, 83.0, 67.6, 53.3, 61.5, 73.1, 82.5,

and 86.7 % for Jan – Dec months respectively.

The study has quantified the monthly and seasonal cycles and variations of river flows

and simulated runoff with the snow area extent and identified a clear correspondence of

river flows and simulated snowmelt runoff to the change in snow area extent. The study

observes a clear and definite response of observed river discharges and simulated

snowmelt runoff to seasonal snow cover changes, i.e. an association of low stream flows

with high snow area extent during the winter season (Sep – Feb), an increase in discharge

associated with a decrease of snow area extent during the early summer (Mar – Jun), and

decrease in discharge with decreasing snowcover in the late summer, monsoon season

(Jul – mid Sep). It employs the daily records of snowcover and relates the average daily

snowcover with the daily river discharges and snowmelt runoff and also develops

prediction model for the total runoff volume of the four main summer months (May –

Aug).

6.2 Limitations

The study can give reasonably good estimates for average weather conditions.

The developed regression equations better model the flow conditions for a

progressing or continuing year. But if the snowcover of a particular day or month

88

is significantly different from the average conditions, then the results might incur

some degree of error.

The prediction models developed for snowmelt runoffs are based on the results of

SRM application therefore the limitations of the model and uncertainties involved

in input data are incorporated. However, selection of the best model, accurate

estimation of model input parameters and adequate calibration and verification of

the selected model may significantly avoid this problem.

The study uses MODIS snowcover products directly, without testing and

validating their accuracy in study area, therefore inaccuracies, if any, incorporated

in this data are also accumulated.

6.3 Recommendations

There is need to test and validate the MODIS snowcover products in the HKH

region of Pakistan and compare its accuracy with the actually observed field data.

The study findings are based on only three years daily records of snow area

extent, any expansion in the study time period may improve the developed

models.

Also, there is need to test the developed regression models against the observed

river flows and snow extent areas for different years.

89

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