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Hydrological Investigation of Swat River Basin Using GIS, Remote Sensing and Snowmelt Runoff ModelingZakir Hussain DahriThesis Submitted for the Degree of Master of Applied Science (Geographic Information Systems)Department of Geomatics Faculty of EngineeringThe University of MelbourneApril, 2008DECLARATIONThis 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 appr
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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
v
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
vi
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
ix
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
Figure 5.17 Computed snowmelt and rainfall runoff components for the Year
2003…………………………………………………………………... 74
Figure 5.18 Computed snowmelt and rainfall runoff components for the Year
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
Figure 5.25 Relationship of average daily snowcover with average daily
simulated snowmelt runoff and average daily observed runoff for
July – August months………………………………………………... 84
Figure 5.26 Relationship of average daily snowcover with average daily
simulated snowmelt runoff and average daily observed runoff for
September – February months……………………………………….. 85
Figure 5.27 Prediction model for estimating May – Aug runoff volume from the
snowcover estimated on May 1-8……………………………………. 86
xii
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
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
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
Calender Month Number
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
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
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
J F M A M J J A S O N D
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
J F M A M J J A S O N D
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
J F M A M J J A S O N D
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
J F M A M J J A S O N D
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
J F M A M J J A S O N D
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
J F M A M J J A S O N D
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
J F M A M J J A S O N D
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
Calender Month Number
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
Snowmelt generally continues throughout the year but contribution of winter snowmelt
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
J F M A M J J A S O N D
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
J F M A M J J A S O N D
M onth
Ave
rage
Dai
ly D
ischa
rge
(Cum
ec)
Snowmelt Discharge Rainfall Discharge
Figure 5.17 Computed snowmelt and rainfall runoff components for the Year 2003
74
0
100
200
300
400
500
600
700
800
J F M A M J J A S O N D
Month
Ave
rage
Dai
ly D
ischa
rge
(Cum
ec)
Snowmelt Discharge Rainfall Discharge
Figure 5.18 Computed snowmelt and rainfall runoff components for the Year 2004
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 (
%)
Snowmelt Runoff Rainfall Runoff
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
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Month
Ave
rage
Mon
thly
Sim
ulat
edD
ischa
rge
(%)
Snowmelt Runoff Rainfall Runoff
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
J F M A M J J A S O N D
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
Average Daily Snowcover (% of Basin Area)
Ave
rage
Dai
ly D
ischa
rge
(Cum
ec)
10
River Discharge Snowmelt Runoff
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
Average Daily Snowcover (% of Basin Area)
Ave
rage
Dai
ly D
ischa
rge
(Cum
ec)
0
River Discharge Snowmelt Runoff
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.
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 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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