Snowmelt Runoff Investigation in Swat River Upper Basin Using SRM Model

8/13/2019 Snowmelt Runoff Investigation in Swat River Upper Basin Using SRM Model

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Snowmelt Runoff Investigation in River Swat Upper

Basin Using Snowmelt Runoff Model, Remote

Sensing and GIS Techniques

Mateeul Haq

March, 2008


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Snowmelt Runoff Investigation in River Swat Upper

Basin Using Snowmelt Runoff Model, Remote Sensing

and GIS Techniques

by

Mateeul Haq

Thesis submitted to the International Institute for Geo-information Science and Earth

Observation in partial fulfilment of the requirements for the degree of Master of Science in

Geo-information Science and Earth Observation, Specialisation: (Geo-hazards)

Thesis Assessment Board

Prof.Dr. V.G. Jetten (Chairman)

Dr. T.W.J. van Asch (External Examiner)

Dr. D. Alkema (1stSupervisor)

Prof.Dr. V.G. Jetten (2nd

Supervisor)

Dr. Z. Vekerdy (2nd

Supervisor)

Drs. T.M. Loran (Course Director AES)

INTERNATIONAL INSTITUTE FOR GEO-INFORMATION SCIENCE AND EARTH OBSERVATION

ENSCHEDE, THE NETHERLANDS


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Disclaimer

This document describes work undertaken as part of a programme of study at the

International Institute for Geo-information Science and Earth Observation. All views

and opinions expressed therein remain the sole responsibility of the author, and do

not necessarily represent those of the institute.


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Abstract

Snow is a great water resource, but when melted abruptly it causes flooding. The River Swat,

which flows from the Hindukush mountains starting from Kalam valley, is flooded in summer

when abrupt melting of snow occurs. The aim of this study was to simulate the daily snowmelt

runoff using Snowmelt Runoff Model (SRM) in Kalam basin and use it for flood forecasting and

management of river Swat. An SRTM DEM was used to define the catchment area upstream of

Kalam hydrological station, and to get the elevation zones of 500 meters intervals. Snow cover in

different elevation zones were mapped, using MODIS cloud free images for the melting seasons

(April to August) of 2004-2006. After analyzing the available meteorological and field data,

input parameters were obtained to feed the model. SRM uses accuracy criteria, namely, thecoefficient of determination R

2 and volume difference Dv, calculated from the measured river

discharge data and the simulated discharge by the model. The model was calibrated for the

melting season of 2004, with R2=0.74 and Dv= -0.7%. When verified for 2005 and 2006, the

results were R2=0.54, Dv= -16.1% and R

2=0.72, Dv= -0.3% respectively. Years 2004 and 2006

were very much alike in snowfall in winter and average temperatures in summer and hence their

simulated results were quite alike. However year 2005 received a record 30 years snowfall in

winter and above average temperatures in summer causing changes in runoff coefficients and

degree day factor. After adjustment of these parameters a new simulation resulted in R2=0.90 and

Dv= 3.2%. The weekly forecast by the model from in the 1stweek of September, and the melting

season of 2005 resulted with R2=0.80, Dv=2.1% and R2=0.95, Dv= -1.2% respectively. Keeping

in view the values of R2 and Dv for the melting season simulations and seasonal as well as

weekly forecasts, it is concluded that SRM model can be used for flood forecasts and water

resource management in the study area.


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Acknowledgements

Alhamdulillah, all glory be to ALLAH, who gave me the strength to complete my M.Sc. study at

ITC. I would like to acknowledge my family in Pakistan, especially my parents for their spiritual

support and prayers during my studies. Besides, thanks to Institute of Space and Technology

(IST) Pakistan, my parent department for sponsoring my M.Sc. studies here in ITC.

My special thanks go to my supervisors Professor Vector Jetten, Dr. Dinand Alkima for allowing

me to carry out this research and spending their valuable time, efforts and sharing their

knowledge. Especial regards to second supervisor Dr. Vekerdy Zoltan from Water Resource

(WRS) department for his critical comments and valuable suggestions which enabled me to

complete the research. Thanks to all lecturers in ESA department and to all my lovely

international friends and most respect to Dutch people.

Special thanks and regards to Mr. Raza Hussain Ali, Chairman (IST), who provided me this

opportunity of getting master degree in applied earth sciences. Thanks to Mr. Arshad H. Siraj,

Director General (IST), for his continuous support during my studies. Many thanks to Dr. Badar

Munir Khan Ghauri, Deputy Chief Manager and Mr. Rahmatullah Jilani, General Manager,

SPAS division for their struggle to get all the necessary data required for this research work.

Special regards to Pakistan Meteorological Department (PMD), Pakistan Water and Power

Development Authority (WAPDA) for providing the important Meteorological and river

discharge data.

I present my gratitude to Mr. Gulzar Khan, and Mr. Hydayattullah for their support during my

fieldwork. Thanks to my friends Mr. M. Shafique, Mr. Imtiaz Hasan and Mr. Zahir Ali and to all

my friends from Pakistan in ITC for their help during my research.

MAY ALLAHALLAHALLAHALLAH FORGIVE US (AMIN)

MATEEUL HAQ

ENSCHEDE, THE NETHERLANDS


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Table of contents

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

1.1 PROBLEM STATMENT ....................................................... ........................................................... ...... 1

1.2 GENERAL OBJECTIVES ..................................................... ........................................................... ...... 2

1.2.1. Specific Objectives ............................................................................................................... 2

1.3 RESEARCH QUESTIONS..................................................... ........................................................... ...... 3

1.4 RESEARCH HYPOTHESIS................................................... ........................................................... ...... 3

1.5 RESEARCH METHODOLOGY ....................................................... ....................................................... 4

2. LITERATURE REVIEW..................... ............................................................ ................................... 6

2.1 OPERATIONAL SNOWMELT MODELS .................................................... ............................................. 6

2.2 COMPARISON OF SNOWMELT RUNOFF MODELS ...................................................... .......................... 7

2.3 KABUL RIVER SRMSTUDY ....................................................... ....................................................... 9

2.4 WHY SRMIS SELECTED FOR THIS STUDY? ..................................................... ................................. 10

2.5 DESCRIPTIONOFTHESTUDYAREA........................................... ........................................... 10

2.6 PHYSIOGRAPHY...................................................... ........................................................... .............. 11

2.7 CLIMATE OF THE STUDY AREA.................................................... ..................................................... 12

3. DESCRIPTION OF SNOWMELT RUNOFF MODEL................................................... .............. 14

3.1 HISTORY OF SRMDEVELOPMENT........................................................ ........................................... 15

3.2 DATA REQUIREMENT ....................................................... ........................................................... .... 193.3 BASIN CHARACTERISTICS ........................................................... ..................................................... 19

3.3.1. Basin and zone areas ................................................................................. ........................ 19

3.3.2. Area elevation curve................................................................................... ........................ 20

3.4 SNOW AREA,S....................................................... ........................................................... .............. 20

3.5 INPUT VARIABLES.................................................. ........................................................... .............. 21

3.5.1. Temperature, T................................................................................................................... 21

3.5.2. Precipitation, P ................................................... ........................................................... .... 22

3.5.3. Snow Depletion Curve........................................................................................................ 23

3.6 INPUT PARAMETERS......................................................... ........................................................... .... 23

3.6.1. Runoff Coefficient, C..................................................... ..................................................... 24

3.6.2. Degree Day Factor: ...................................................... ..................................................... 253.6.3. Temperature Lapse Rate: ........................................................ ........................................... 26

3.6.3.1. Fieldwork .................... ...................... ..................... ..................... ...................... ..................... ....... 263.6.4. Critical temperature, TCRIT................................................................................................. 27

3.6.5. Rainfall contributing area, RCA .................................................................................... .... 27

3.6.6. Recession coefficient, k .................................................................................................. .... 27

4. GIS AND REMOTE SENSING................................................... ..................................................... 30

4.1 SOFTWARE USED ................................................... ........................................................... .............. 30

4.2 DIGITAL ELEVATION MODEL (DEM).................................................... ........................................... 30

4.2.1. Fill Sink ..................................................... ........................................................... .............. 31

4.2.2. Flow Direction ................................................................................................................... 32

4.2.3. Flow Accumulation ............................................................................................................ 32


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4.2.4. Drainage Network Extraction ........................................................... ................................. 32

4.2.5. Drainage Network Ordering ................................................... ........................................... 32

4.2.6. Catchment Extraction......................................................................................................... 33

4.2.7. Catchment Merge .......................................................... ..................................................... 34

4.3 REMOTE SENSING OF SNOW COVER ..................................................... ........................................... 35

4.4 MODERATE RESOLUTION IMAGING SPECTRORADIOMETER (MODIS) ............................................. 35

4.5 EXTRACTION OF STUDY AREA SUBSET .......................................................... ................................. 38

4.6 CLASSIFICATION OF IMAGES FOR EXTRACTION OF SNOW ................................................... .............. 39

4.7 EXTRACTION OF SNOW FOR EACH ELEVATION ZONE ........................................................ .............. 40

4.7.1. Extract by Attribute ....................................................... ..................................................... 40

4.7.2. Resample ................................................... ........................................................... .............. 41

4.7.3. Extract by Mask............................................................. ..................................................... 41

4.7.4. Reclassify....................................................................... ..................................................... 42

5. MODEL RESULTS .................................................. ........................................................... .............. 45

5.1 ASSESSMENTOFTHEMODELACCURACY .................................................. ........................ 45

5.1.1. Accuracy criteria................................................. ........................................................... .... 45

5.2 SIMULATIONS OF SRM..................................................... ........................................................... .... 46

5.3 MODEL CALIBRATION ...................................................... ........................................................... .... 46

5.4 SRMVERIFICATION FOR 2005AND 2006....................................................... ................................. 47

5.5 DISCUSSION ON MODEL SIMULATIONS ............................................................ ................................. 49

5.6 FORECASTS USING WINSRM............................................ ........................................................... .... 51

5.7 SENSITIVITY ANALYSIS .................................................... ........................................................... .... 53

5.8 EFFECT OF DISTORTED DEPLETION CURVE .................................................... ................................. 54

6. CONCLUSION AND RECOMMENDATIONS ..................................................... ........................ 55

6.1 CONCLUSION.......................................................... ........................................................... .............. 55

6.2 RECOMMENDATIONS........................................................ ........................................................... .... 57

7. REFERENCES: ........................................................ ........................................................... .............. 58

APPENDICES........................................................... ........................................................... ........................ 60


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List of figures

Figure 1.1: River Swat Pre and Post Flood Comparison. Left side image is showing normal

river flow, whereas right side image is showing overflow in the river. Bright blue in the

image is snow. Dots are showing villages around river Swat. Courtesy to MODIS rapid fire

team ......................................................................................................................................... 2

Figure 1.2: Flow chart showing methodology followed in the research................................. 5

Figure 2.1: Combined representation of model performance using three criteria: R2, DG and

Dv. The volumes of the prisms indicate the average inaccuracies of the tested models from

all results for snowmelt seasons reported in the WMO project (Rango 1988). ...................... 8

Figure 2.2: Combined representation of model performance using three criteria: R

2

, DG andDv. The volumes of the prisms indicate the maximum inaccuracies of the tested models from

all results as listed for snowmelt seasons and individual years in the WMO tables (Martinec

1989)........................................................................................................................................ 9

Figure 2.3: Location of the Catchment (Study Area) upstream the Kalam gauge station. The

district boundaries have been delineated by Humanitarian Information Centre for Pakistan.

............................................................................................................................................... 10

Figure 2.4: SRTM derived DEM including hill shading effect is showing the elevation

differences in the study area. The base station is at 2000 meters above sea level................ 11

Figure 3.1: The structure of the SRM ................................................................................... 16

Figure 3.2: Kalam basin has been divided into 8 elevation zones of 500 meter interval...... 19

Figure 4.1: DEM of the study area obtained from Shuttle Radar Topography Mission

(SRTM) ................................................................................................................................. 31

Figure 4.2: Catchment extraction using Drainage network ordering and Flow direction as

inputs. .................................................................................................................................... 34

Figure 4.3: Extracted catchment above Kalam station.......................................................... 35

Figure 4.4: MODIS subset of Afghanistan covering parts of Pakistan including the study

area of Kalam. Grey white color is showing snow and ice................................................... 37

Figure 4.5: Subset of Afghanistan has been geo-referenced and re-projected to UTM co-

ordinates. ............................................................................................................................... 38Figure 4.6: Satellite image of study area on the right after masking it from Afghanistan

subset. .................................................................................................................................... 39

Figure 4.7: Classified image of the study area. Blue color is showing snow whereas dark

brown is snow free. ............................................................................................................... 39

Figure 4.8: Main steps for getting snow cover using ERDAS and Arc GIS tools................ 40

Figure 4.9: Extract by attribute tool and its result. Grey color is showing snow whereas

white is snow-free area.......................................................................................................... 41

Figure 4.10: Extract by mask tool and the resulted DEM of the area covered with snow.

White color is no-data (snow-free)........................................................................................ 42

Figure 4.11: Snow class in each elevation zone in raster format. Lowest height starts from

Zone1 and ends at Zone8. Zone1 has no snow in the month of May.................................... 42


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Figure 4.12: Sequence of snow cover maps from MODIS, Upper Swat River at Kalam, 3032

km2 , 1991- 5790 m a.s.l. Blue is snow covered area. .......................................................... 44Figure 5.1: Statistics of the daily simulations for the melting season (April-August) 2004. 47

Figure 5.2: Statistics of 2005 melting season simulations following accuracy criteria ........ 48

Figure 5.3: MODIS Land surface temperatures and NDVI data showing high temperatures

and stressed vegetation in 2005 in comparison to 2004 where temperatures are low and

vegetation is healthy. Red color in upper half of the figure is showing high temperatures and

dark green in lower half is showing healthy vegetation........................................................ 49

Figure 5.4: Statistics of the daily simulations for the melting season 2005.......................... 51

Figure 5.5: Depletion was distorted in the middle of the melting season............................. 54

Figure 5.6: Depletion curve was distorted at the end of the depleted curve......................... 54


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List of tables

Table 2.1: Runoff models selected for WMO study on Intercomparison of runoff models. adapted

from (Russell 2003) and (WMO 1986). .......................................................................................... 6

Table 2.2: Results of model performance in the WMO project (10 years, snowmelt season) ....... 8

Table 3.1: Temperature data recorded during fieldwork .............................................................. 26

Table 5.1: Sensitivity analysis results ........................................................................................... 53


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List of Graphs

Graph 2.1: Annual Precipitation in comparison with precipitation in summer for the last 15 years

has been shown here...................................................................................................................... 12

Graph 2.2: Temperature averages of monthly min, max, and average for the last 20 years

recorded data in Kalam Catchment. .............................................................................................. 13

Graph 3.1: Countries name against the frequency of SRM applications..................................... 18

Graph 3.2: Cumulative area elevation curve of the study area. There are 8 Small dots on the

curve and they are representing hypsometric mean elevation of each zone starting from zone1 to

zone8. ............................................................................................................................................ 20Graph 3.3: Daily average temperatures of melting seasons of 2004, 2005, and 2006 are used as

input to the model for the respective years. These are then extrapolated to the hypsometric mean

altitudes of all eight zones............................................................................................................. 21

Graph 3.4: Daily rainfall recorded at base station Kalam in 2004 and 2005................................ 22

Graph 3.5: Depletion curves of the snow coverage for 8 elevation zones of the basin Kalam,

derived from the MODIS imagery Zone1: 1991 - 2490 m a.s.l., Zone2: 2491 - 2990 m a.s.l.,

Zone3: 2991 - 3490 m a.s.l., Zone4: 3491 - 3990 m a.s.l., Zone5: 3991-4490 m a.s.l, Zone6: 4491

- 4990 m a.s.l, Zone7: 4991-5490 m a.s.l, Zone5: 5491-5990 m a.s.l........................................... 23

Graph 3.6: Runoff coefficients for rain and snow used for the model calibration for the melting

season (April-August) 2004. ......................................................................................................... 24Graph 3.7: Degree day factor values against the months of the melting season 2004, 2005 and

2006 for Kalam basin, Pakistan..................................................................................................... 25

Graph 3.8: Recession flow plot Qn Vs Qn+1 for Kalam basin in Pakistan. Either the solid lower

envelope line or the dashed medium line is used to determine k-values for computing the

constants x and y. .......................................................................................................................... 28

Graph 4.1: Snow Deletion Curves of Snowmelt Seasons from April to August in the year 2004.

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

Graph 4.2: Snow Deletion Curves of Snowmelt Seasons from April to August in the year 2005.

....................................................................................................................................................... 43Graph 4.3: Snow Deletion Curves of Snowmelt Seasons from April to August in the year 2006.

....................................................................................................................................................... 44

Graph 5.1: Runoff simulations for the melting season 2004 of Kalam basin. The dashed line is

the measured discharge at Kalam station whereas the solid line is simulated runoff................... 46

Graph 5.2: Runoff simulations for the melting season 2005 of Kalam basin. The dashed line is

the measured discharge at Kalam station whereas the solid line is simulated runoff................... 47

Graph 5.3: Daily runoff simulations for melting season, 2006. The triangular points measured

discharge values whereas the gray circular points are computed values. ..................................... 48

Graph 5.4: Same period temperatures comparison between 2004 and 2005 ................................ 50

Graph 5.5: Runoff simulations for the melting season 2005. R 2=0.90 and Dv=3.2%................... 50Graph 5.6: Weekly forecast of runoff in the Kalam catchment, 1st week of Aug, 2005.............. 52


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Graph 5.7: Melting season runoff forecast for the year 2005....................................................... 53


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List of Appendices

Appendix 1: Average temperatures at Kalam station for the melting season 2004. ..................... 60



Appendix 4: Measured Vs Computed River Discharge (m3s

-1) 2004 (Melting Season), Kalam

Basin.............................................................................................................................................. 63



Basin.............................................................................................................................................. 64



Basin.............................................................................................................................................. 65Appendix 7: Precipitation data for 2004, Kalam station............................................................... 66

Appendix 8: Precipitation data for 2005, Kalam station............................................................... 67

Appendix 9: Precipitation data for 2006, Kalam station............................................................... 68


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List of Abbreviations

WinSRM Window based Snowmelt Runoff Model

MODIS Moderate Resolution Imaging Spectroradiometer

NDVI Normalized Difference Vegetation Index

N.W.F.P. North West Frontier Province

LST Land Surface Temperature

SRTM Shuttle Radar Topography Mission


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SNOWMELT RUNOFF INVESTIGATION IN RIVER SWAT UPPER BASIN USING SNOWMELT RUNOFF MODEL

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1. INTRODUCTION

Precipitation can be in many different forms, such as rain, freezing rain, hail, sleet, and snow.

Snow is composed of small ice particles and hence is a granular material. It is a soft structure,

unless packed by external pressure. Snow is a great source of water as it acts as natural reservoir

for many water supply systems.

Nature has blessed Pakistan with the icy peaks of the Himalaya, Karakoram and Hindukush in

the north of the country. These mountain ranges receive heavy snowfall in winter, which is a

great water resource feeding most of the rivers in Pakistan throughout the year. Snow is precious

water resource, but when it melts rapidly it causes flooding in most of the rivers in Pakistan.

1.1 Problem Statment

River Swat flows from Hindukush Mountains starting with River Ushu, and Utror, meeting in

Kalam valley and merges into Kabul River in Peshawar valley, North West Frontier Province

(N.W.F.P), Pakistan. River Swat is flooded in summer (Figure 1-1) when abrupt melting of snow

occurs under the combined effects of sunlight, winds, rainfall and heat waves in the region.

There is only one river discharge measurement station and one meteorological station at Kalam

city. Snow monitoring is only limited to finding the depth of snowfall in winter and the river

gauge station is used to warn about flood like situation when water discharge is increased.

Therefore, monitoring of snow-related processes can play a vital role in controlling the flood

problem along with water resource evaluation and management. Snowmelt Runoff Model,

developed by (Martinec 1975) will be used to evaluate the snowmelt runoff in Kalam basin

(Swat District) a tributary of Swat River.


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Figure 1.1:River Swat Pre and Post Flood Comparison. Left side image is showing normal river

flow, whereas right side image is showing overflow in the river. Bright blue in the image is snow.

Dots are showing villages around river Swat. Courtesy to MODIS rapid fire team

1.2 General Objectives

The general objective of this study is to simulate and estimate the daily snowmelt runoff in

snowmelt seasons for the study area and apply it for flood forecasting and management.

1.2.1. Specific Objectives

The following specific objectives will contribute to get the general objective.

Simulation of river flows on daily basis in a snowmelt season.

Short term and seasonal runoff forecasts.

Providing the forecasts for flood prediction and water resource management


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Based on the specific objectives of the research the following are the possible research questions:

1.3 Research Questions

What are the parameters to which SRM is more sensitive?

What may be the effect of occasional summer snowfall on runoff simulation?

What may be the effect of long dry periods or above average temperatures in a

melting season on runoff coefficients?

What may be the effect of wet or dry years on the model performance?

How useful the runoff simulation and forecasts would be for flood prediction and

water resource management?

Keeping in view the structure of the model and the previous studies using the same model, the

following are the hypothesis to answer the above research questions.

1.4 Research Hypothesis

SRM is a degree day model; therefore, it is expected to be more sensitive to

temperature lapse rate as this is used to extrapolate temperatures from base station to

higher altitudes.

The depletion curves derived from the measured points are used by SRM to simulate

runoff. These curves are distorted by occasional summer snowfalls (Short lived)

which may lead to excessive melt-water especially if the time interval between too

successive measured points is too long.

Long dry periods or above average temperatures both increase the chances of water

losses due to infiltration or utilization by the stressed vegetation, and hence may

reduce the runoff coefficients for snow and rain.

The model calibrated for one year, is supposed to be verified by other years as well.

Some parameters used in the calibration may vary in different weather conditions.

Therefore, if the calibrated year weather conditions (Dry Year) are drastically

different from the verification year conditions (Wet Year) then problems may occur

in verification.


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The daily discharge and the seasonal average discharge measured at the river gauge

station, and the computed daily discharge by the model along with the seasonal

measured volumes and seasonal computed volumes, are used for calculating the

coefficient of determination, R2and the volume difference, Dv. R

2and Dvare used to

find the accuracy of the model simulations or forecasts. If the simulated results are

found accurate (Based on the accuracy criteria), then it will be justified to say that

the model can play a significant role in the flood forecasting and water resources

management.

1.5 Research Methodology

SRM will be used for estimating the runoff due to snowmelt. Keeping in view the necessary data

required running the model and the research questions supposed to be answered, following are

the steps followed in the research.

Extraction of the river basin above Kalam city, using SRTM data.

Extractions of snow cover extent from a series of MODIS images for the years 2004,

2005, and 2006.

Calculation of snow covers extent time series in selected elevation zones.

Analysis of hydrological and meteorological data of the respective years of the base

station Kalam.

Calculation of SRM model parameters from the above mentioned hydrological and

meteorological data.

Calculation of the temperature lapse rate from the temperature data recorded during

fieldwork.

Calibration of the model using the above information.

Verification of the model.

Using the model for weekly as well as seasonal runoff forecasts

Sensitivity analysis.


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Checking the model run for distorted depletion curve.

Assessment of the model accuracy.

A schematic diagram below shows the methodology to use SRM model for the current study.

Figure 1.2: Flow chart showing methodology followed in the research.


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2. Literature Review

2.1 Operational Snowmelt Models

Many models have been created around the world over the last four decades to describe

snowmelt runoff. These can be divided into two broad categories, statistical and physical. A

statistical model utilizes statistical relationships between inputs and outputs. A physically based

model describes the physical processes that relate inputs to outputs. In turn, these models can be

applied in a lumped or distributed mode. A lumped model describes catchment processes with

single catchment average values. A distributed model divides a catchment into sections and

carries out model calculations for each section. A lumped model can be considered a single

section distributed model, or equally, a distributed model can be considered a series of small

lumped models. The two more common ways of sub dividing an area of interest for snowmelt

modelling is into elevation zones, or into grid squares (KERR 2005). Some of these models are

briefly discussed in Table 2.1:

Table 2.1: Runoff models selected for WMO study on Intercomparison of runoff models. adapted

from (Russell 2003) and (WMO 1986).

S.No Model Author(s) (Year) Data Required Remarks

1

University of

British Columbia

(UBC)

(Quick 1976)

Temperature,

precipitation,

discharge

Process-oriented,

lumped parameter,

continuous

simulation model

2CEQUEAU (Charbonneau

1977)

Temperature,

precipitation,

snowfall, discharge

Hydrological

repercussions

3 ERM (Turcan 1981)

Temperature,

precipitation,

discharge

Water management,

Reservoir operation


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4 NAM (Gotleib 1980)

Temperature,

precipitation,

discharge

Lumped, continuous

rainfall-runoff model

5 TANKSugawara et al.

(1974)

Temperature,

evapotranspiration,

precipitation,

discharge

Process-oriented,

semi-distributed or

lumped continuous

simulation model

6

Hydrological

Simulations (HBV) (Bergstrm 1975)

Temperature,

precipitation,

evapotranspiration,

discharge

Process-oriented,

lumped, continuous

streamflow

simulation model

7Snowmelt Runoff

Model (SRM)(Martinec 1975)

Temperature,

precipitation,

snow cover,

discharge

Lumped continuous

snowmelt-runoff

simulation model

8

Stream flow

Synthesis andReservoir

Regulation

(SSARR)

(Anderson 1973)

Temperature,

precipitation,discharge

Lumped continuousstream flow

simulation model

9

Precipitation

Runoff Modeling

System Model

(PRMS)

(Leavesley 1983)

Temperature,

precipitation,

solar radiation

Deterministic

physical-process

model for runoff

simulations

10National Weather

Service (NWS)(Burnash 1973)

Temperature,

precipitation,

discharge

Lumped continuous

river forecast system

2.2 Comparison of Snowmelt Runoff Models

The World Meteorological Organization (WMO) organized an international comparison of

snowmelt runoff models (WMO 1986) in which hundreds of model runs were performed in six


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selected test basins. Table 2.2 compares the numerical results of each model in the WMO

project.

Table 2.2: Results of model performance in the WMO project (10 years, snowmelt season)

Figure 2.1 shows a summary of all numerical values of R2, DG (Coefficient of Gain) and Dv

published by WMO (WMO 1986). DG and R2

have the same formula, but DG uses the average

measured discharge from a number of past years whereas R2uses the average measured discharge

of the particular year used in the simulations. Each prism refers to a tested model. The length

along the x-axis corresponds to the arithmetic mean of all (1- R2) values, length along the y-axis

to the arithmetic mean of all (1-DG) values, and length along the z-axis to the arithmetic mean of

all Dvvalues as achieved in the snowmelt seasons of 10 test years.

Figure 2.1:Combined representation of model performance using three criteria: R2, DG and Dv.

The volumes of the prisms indicate the average inaccuracies of the tested models from all resultsfor snowmelt seasons reported in the WMO project (Rango 1988).


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2.4 Why SRM is selected for this study?

There are two main reasons why SRM was selected for this study:

1. The strength of SRM is its primary reliance on snow cover areal extent. This allows for

limited data input needs, and the snow-covered-area can be derived from satellite,

aircraft, or ground measurements.

2. Secondly the (WMO 1986) study of Intercomparison of various models proves SRM the

best of all (Figures 2.1, and 2.2).

2.5 DESCRIPTION OF THE STUDY AREA

Kalam catchment (Study Area) is situated in the North West Frontier Province (N.W.F.P) of

Pakistan, between 72 12& 72 51E and 35 17& 35 54N. The study area is surrounded by

Ghizer district in the North, Chitral in the North West, Upper Dir in the West, Swat in South and

Kohistan in the East (Figure 2.3). SRTM DEM data has been used for the extraction of the

catchment boundary. This catchment belongs to tributaries of the Indus River, starting with Swat

River and Joining Kabul River which finally becomes part of river Indus. The whole area of thecatchment upstream the Kalam station is approximately 2032 sq km.

Figure 2.3:Location of the Catchment (Study Area) upstream the Kalam gauge station. The

district boundaries have been delineated by Humanitarian Information Centre for Pakistan.


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2.6 Physiography

Physiography is the study of the physical characteristics and morphological conditions of a

catchment. These have important role and effect on the hydrological characteristics and water

regime. To have a good quantitative and qualitative assessment of the hydrological system, it is

important to know the physiographic characteristics of the catchment under study.

Kalam catchment is mountainous with high internal relief. The elevation range between lowest

and highest altitudes is 1991-5790 a.m.s.l. The huge elevation gradient affects precipitation andtemperature value in the catchment. There are a few lakes in the catchment which can affect the

runoff coefficients for both rain and snow.

Figure 2.4:SRTM derived DEM including hill shading effect is showing the elevation differences

in the study area. The base station is at 2000 meters above sea level.


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2.7 Climate of the study area

Kalam valley is situated in the upper reaches of Swat district, in the North-West Frontier

Province. In Kalam catchment the annual rainfall averages around 1030 mm, with about 101.6

mm expected between July and September. October and November are the driest months with

rainfalls generally under 30 mm per month except in the most exposed areas. Graph of annual

precipitation in comparison with precipitation in summer, recorded at Kalam Station, has been

shown in Graph 2.1. Precipitation in winter season is mostly in the form of snow. High mountain

peaks can receive snow even in summer.

Kalam Annual Precipitation in Comparison with Precipitation in Summer

0

200

400

600

800

1000

1200

1400

1600

1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

Years

Precip

itation(mm)

Annual

Summer

Graph 2.1:Annual Precipitation in comparison with precipitation in summer for the last 15 years

has been shown here

Monthly average temperatures in this region (Kalam Catchment) remains below 5 C in the

months of Dec, Jan and Feb, which touches 20 C on the average in June, July and August

(Graph 2.2). Maximum temperatures can reach up-to 26 C whereas minimum can go below -6

C.


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Kalam 20 years Min, Max, and Ave Temperature Values

-10

-5

0

5

10

15

20

25

30

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

Months

Tem

perature(Centigrade)

Max

Min

Ave

Graph 2.2:Temperature averages of monthly min, max, and average for the last 20 years

recorded data in Kalam Catchment.


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3. Description of Snowmelt Runoff Model

The Snowmelt-Runoff Model (SRM) is designed to simulate and forecast daily stream flow in

mountain basins where snowmelt is a major runoff factor (Jaroslav Martinec 2005). SRM was

developed by (Martinec 1975) for small European basins. Thanks to the progress of satellite

remote sensing of snow cover, SRM has been applied to larger and larger basins. Recently, the

runoff was modelled in the basin of the Ganges River, which has an area of 917,444 km2 and an

elevation range from 0 to 8,840 m a.s.l. Contrary to the original assumptions, there appear to be

no limits for application with regard to the basin size and the elevation range.

SRM uses percentage areal snow cover, air temperature, and precipitation as critical input

variables. SRM divides the watershed into elevation zones and accounts for degree-days in each

elevation zone to drive the amount of snowmelt. Specific basin characteristics include runoff

coefficients, degree-day factors, and historical recession coefficients (Shafer 1981). Definition of

the basin includes careful determination of basin areas and, once the elevation zones are

established, finding the area of each zone. The zonal mean hypsometric elevation is determined

for each zone from an area-elevation curve. It is also necessary to know the temperature lapse

rate for the basin. In SRM, Each day during the snow melt season, the water produced from

snow melt and from rainfall is computed, superimposed on the calculated recession flow and

transformed into daily discharge from the basin (Martinec 1983).

Through the use of the zonal mean hypsometric elevations, the actual elevation of the

temperature measurement station and the temperature lapse rate, the melting degree-days for

each elevation zone are calculated. The precipitation for each zone is determined to be either rain

or snow, depending on the average zonal temperature and a critical temperature selected to be

slightly above freezing. The snow coverage for each zone is determined by ground observation,

aircraft photography, or by satellite and is arrayed as a depletion curve over the snowmelt period.

Runoff coefficient estimation requires knowledge of the basin and its hydrology, and it varies

over the year (Martinec 1983). The snowmelt-degree-day factor can be varied throughout the

snowmelt period to account for the changing density and albedo of the snowpack. The recessioncoefficient is estimated from historical records of the actual daily average flows.


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SRM accumulates the number of degree-days in each elevation zone over the snowmelt period

and discriminates the input precipitation into snow or rain by comparing the assigned critical

temperature to the average daily temperature. Snowmelt is calculated using a degree-day factor

that is applied to the portion of the elevation zone that is snow covered. Within each elevation

zone, an average snow cover depletion curve is used to estimate the temporal change in the snow-

covered area. The snowmelt is distributed according to the chosen elevation zones and summed

to give total average daily runoff from the entire watershed.

3.1 History of SRM Development

SRM model is originated by Jaroslav Martinec, attached at the time to the Swiss Snow and

Avalanche Research Institute at Davos. Martinec (1975) described a snowmelt runoff model

which has come to be referred to as SRM. It has undergone substantial development since 1975,

by Martinec himself in collaboration with Al Rango (US NASA, later US ARS) and Michael

Baumgartner (University of Bern). The model has been amended and extended several times in

the light of operational experience.

SRM was developed specifically to predict snowmelt runoff, unlike HBV and other general-

purpose hydrological models containing a snowmelt routine. It has been extensively applied to

snowmelt modelling in mountainous terrain, and recently also to climate-change scenarios.

The basic structure of SRM is shown schematically in Figure 3.1. The basin is subdivided into

elevation zones. Runoff from all elevation zones is added together before routing, so location is

not taken into account in the model. A daily time step is used. Snowmelt in each zone is

predicted from air temperature, any rainfall is added on, and the total new water is routed

through a single store.


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Figure 3.1:The structure of the SRM

The original version of the model can be represented by a single equation (3.1):

1 (1 ) ( )+ = + +n n i i i ii

Q kQ k a T A P

( )3.1

Where:

n is the day number

i are indexes of the elevation zones

Q is discharge from the basin (m3per second)

T is the air temperature (C) extrapolated to hypsometric mean height of each zone

P is the precipitation (mm and cm both can be used) falling as rain in the zone (when T

> Tcrit)

A is the current snow covered area (SCA) in the zone (% of zone area)

k is a recession coefficient

a is degree day factor (cm per C per day)

One other parameter is implicit in the equation above: a temperature lapse rate used to

extrapolate Ti from temperature at a base station.

The version 4 of SRM is rather more complicated and contains additional parameters, though the

basic structure is unchanged. It can be represented as (equation 3.2):


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1 1 1(1 ) ( )+ + += + +n n n n Sn i i i Rn iQ k Q k c a T A c P ( )3.2

Where:

Snc is a correction factor for losses from snowmelt [0, 1]

Rnc is a correction factor for losses from rainfall [0, 1]

1+nk

is a function of nQ

(Other parameters as original SRM equation (3.1))

So far the SRM philosophy was opposed to calibration; users were urged to use standard default

values based on worldwide experience and physically reasonable limits. The only exception was

that k must be determined for the basin concerned by analysis of recession flow.

The current version of SRM is represented by equation (3.3): Each day, the water produced from

snowmelt and from rainfall is computed, superimposed on the calculated recession flow and

transformed into daily discharge from the basin.

[ ]1 1 110000

( ) (1 )86400

n Sn n n n n Rn n n n n

AQ c a T T S c P k Q k

+ + += + + +

( )3.3

Where: Q = average daily discharge [m3s-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 per C per day] indicating the snowmelt depth

resulting from 1 degree-day

T = number of degree-days [C d]

T = the adjustment by temperature lapse rate when extrapolating the

temperature from the station to the average hypsometric elevation of the basin or

zone [C d]

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

P = precipitation contributing to runoff [cm]. A pre-selected thresholdtemperature, TCRIT, determines whether this contribution is rainfall or snow. If


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precipitation is determined by TCRIT to be new snow, it is kept on storage over

the hitherto snow free area until melting conditions occur.

A = area of the basin or zone [km2]

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

without snowmelt or rainfall:

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

period).

n = sequence of days during the discharge computation period. Equation (3.3) 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 cmkm2d

-1to m

3s

-1

T, S and P are variables to be measured or determined each day, whereas, cs, cr, lapse rate, TCRIT,

k and the lag time are parameters which are characteristic for a given basin. If the elevation range

of the basin exceeds 500 m, it is recommended that the basin should be subdivided into elevation

zones of about 500 m each (Figure 3.2). To date the model has been applied by various agencies,

institutes and universities in over 100 basins, situated in 29 different countries. Almost 50% of

these studies have been done in USA, Spain and Switzerland (Graph 3.1).

Graph 3.1:Countries name against the frequency of SRM applications


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3.2 Data Requirement

Actually there are three input tables required for running the model.

1. The characteristics of the basin with inputs area of each elevation zone and hypsometric

mean elevation of that zone, which can be obtained from area elevation curve of the

study area.

2. The snow cover, precipitation, and temperature data as input variables.

3. The parameters for the basin, which are; runoff coefficient for snow, critical temperature,runoff coefficient for rain, rainfall contributing area, degree day factor, recession

coefficient, temperature lapse rate and time lag. These parameters are used to calibrate

the model.

3.3 Basin characteristics

3.3.1. Basin and zone areas

The basin boundary is defined by the location of the stream gauge (which is Kalam in this study)

and the watershed divide is identified on a topographic map or DEM. In this study SRTM data

was used for getting the basin boundary above Kalam which is the base station as well. This was

done in ILWIS software using DEM-hydro processing tools. The basin boundary is obtained as

polygon map. The polygon map is used for masking only the basin part of SRTM DEM. The

resulted DEM is then re-sampled into different elevation zones of 500m as recommended by the

model developers. The elevation difference in the study for this research is from 1991m a.m.s.l to

5790m a.m.s.l hence it has 8 elevation zones as shown below (Figure 3.2).

Figure 3.2:Kalam basin has been divided into 8 elevation zones of 500 meter interval.


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3.3.2. Area elevation curve

The area elevation curve is obtained from digital elevation model. The model needs hypsometric

mean elevation of each zone to extrapolate various variables to it with a known rate. Temperature

and precipitation are variables which are extrapolated from base station to hypsometric mean

elevation of each, as measurement stations are not available in each zone. The zonal hypsometric

mean elevation, h, was determined from the attribute table, by calculating half area for each zone

as well as adding it to total area of zones below it. This area was then compared against the

elevation in the attribute table (Graph 3.2).

Graph 3.2:Cumulative area elevation curve of the study area. There are 8 Small dots on the curve

and they are representing hypsometric mean elevation of each zone starting from zone1 to zone8.

3.4 Snow Area, S

Snow cover is the most important input variable for this study. Its areal extent gradually

decreases during a snowmelt season. Snow cover has been mapped using images of Moderate

Resolution Imaging Radio-spectrometer (MODIS) sensor onboard Aqua and Terra satellites.

Thanks to MODIS Rapid Response Project at NASA/GSFC who are providing subsets of my

study area free of charge on their website. Detail given in section 4.3


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3.5 Input Variables

SRM needs three input variables, namely daily temperature, daily precipitation data and

snow cover as discussed below.

3.5.1. Temperature, T

SRM estimate the daily snow water equivalent by computing the daily snowmelt depth using the

number of degree days. A degree day is a day with an average temperature one degree above 2

C. For example a day with an average temperature of 15 C, gives 13 degree-days. SRM model

calculate degree days itself from daily average temperatures stored in the database for eachsimulation. Average daily temperatures for the years 2004, 2005, and 2006 are used for the

current study (Graph 3.3).

Graph 3.3:Daily average temperatures of melting seasons of 2004, 2005, and 2006 are used as input to the

model for the respective years. These are then extrapolated to the hypsometric mean altitudes of all eight

zones.

The program accepts either temperature data from a single station or from several stations, by

zone. In the current case basin wide temperature data from a single station was used. The altitude

of the station is entered and temperature data are extrapolated to the hypsometric mean

elevations of all zones using the temperature lapse rate.


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3.5.2. Precipitation, P

Precipitation is a main component of the hydrological cycle, and as such is of primary

importance in hydrology. Mapping of areal precipitation is generally difficult in mountainous

basins, because it shows large and spatial variations and usually exhibits rapid temporal

variations. WinSRM accepts either a single, basin-wide precipitation input from one station

(which is Kalam in this study) or different precipitation inputs zone by zone. Graph 3.4 shows

the daily rainfall in Kalam catchment (2004-2005).

The precipitation can be treated as snow if the temperature values are less than a critical

temperature (section: critical temperature). The new snow that falls over the previously snow-

covered area is assumed to become part of the seasonal snowpack. This precipitation is stored by

SRM and then melted as soon as a sufficient number of degree-days have occurred. Rainfall data

of 2004, 2005, and 2006 have been stored in the database for Kalam station.

0

10

20

30

40

50

60

70

80

90

100

Rain(mm)

1/1/2004

3/1/2004

5/1/2004

7/1/2004

9/1/2004

11/1/2004

1/1/2005

3/1/2005

5/1/2005

7/1/2005

9/1/2005

11/1/2005

1/1/2006

3/1/2006

5/1/2006

Day of year

Daily Rainfall (mm) Recorded at Kalam Station (2004 and 2005)

Graph 3.4:Daily rainfall recorded at base station Kalam in 2004 and 2005


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3.5.3. Snow Depletion Curve

A depletion curve of the snow coverage is the curve showing how the snow cover in each zone is

depleted temporally. It can be interpolated from periodical snow cover mapping so that the daily

values can be read off as an important input variable to SRM.Together with temperature and

precipitation data, such depletion curves enable SRM to simulate runoff in a past year. The

depletion curves in zone7 and zone8 refer to the glaciers and permanently snow-covered areas, so

that is why the values do not change by time.

Graph 3.5: Depletion curves of the snow coverage for 8 elevation zones of the basin Kalam,derived from the MODIS imagery Zone1: 1991 - 2490 m a.s.l., Zone2: 2491 - 2990 m a.s.l.,

Zone3: 2991 - 3490 m a.s.l., Zone4: 3491 - 3990 m a.s.l., Zone5: 3991-4490 m a.s.l, Zone6: 4491

- 4990 m a.s.l, Zone7: 4991-5490 m a.s.l, Zone5: 5491-5990 m a.s.l.

3.6 Input Parameters

Input parameters are used for the calibration of SRM model. These parameters include

runoff coefficient for snow, and rain, temperature lapse rate, degree day factor, lag time,

rainfall contributing area (RCA), and recession coefficients.


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3.6.1. Runoff Coefficient, C

The difference between the available water volume (snowmelt + rainfall) and the outflow from

the basin are the losses due to evaporation, Evapo-transpiration, interception and infiltration.

Runoff coefficient takes care of such losses. Comparison of historical precipitation and runoff

ratios provide a starting point for the runoff coefficient. Daily precipitation values recorded by a

station inside the basin multiplied by the total area of the basin and then dividing the recorded

daily discharge at the outlet of the basin by the results gives the runoff coefficient. Of the SRM

parameters, the runoff coefficient appears to be the primary candidate for adjustment if a runoff

simulation is not at once successful. The river discharge and meteorological data were provided

by the Pakistan Meteorological Department and Pakistan Water and Power Development

Authority (WAPDA). No recorded information about runoff coefficients is available about the

study area.

In the study area losses are supposed to be high in the start of the melting season because out of 8

elevation zones 7 zones are still covered with snow, which may capture most of the precipitation

on it. In the middle of the melting season the losses are dependent on weather conditions, whichmean both low and high losses are possible. Towards the end of the snowmelt season, direct

channel flow from the remaining snowfields and glaciers may prevail in some basins which lead

to a decrease of losses and to an increase of the runoff coefficient. Moreover, c is usually

different for snowmelt cS, and for rainfall cR.

Graph 3.6:Runoff coefficients for rain and snow used for the model calibration for the melting season

(April-August) 2004.


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3.6.2. Degree Day Factor:

The degree day factor is used to compute the snowmelt depths. It increases as the snow becomes

older because of the reduction in albedo of the snow. The degree day factor for snow can be

determined using snow density measurements since they are good indicators for the albedo

(Rango 1995). No data about the snow density in the area are available.

The degree factor was considered for the study area as low in the start of the melting season and

high at the end (Graph 3.7). Because the start of the melting season (April-May) receives most of

the precipitation, and the temperatures extrapolated from base station to altitudes higher than

4000m a.m.s.l are below critical temperature (2 C), which increases the chances of summer

snow fall and hence low snow densities.

Degree Day Factor Values, Kalam Basin

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

26-Mar 15-Apr 5-May 25-May 14-Jun 4-Jul 24-Jul 13-Aug 2-Sep

D a t e

Zones 1-4 (2004,2006)

Zones 5-8 (2004, 2006)

All Zones 2005

Graph 3.7: Degree day factor values against the months of the melting season 2004, 2005 and

2006 for Kalam basin, Pakistan.


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3.6.3. Temperature Lapse Rate:

Temperature decreases with the increase in altitude above mean sea level with a certain rate

called temperature lapse rate. It can be predetermined from historical data if temperature stations

at different altitudes are available. Otherwise one has to go to field for some duration to install

temperature sensors at different altitudes and record the temperature data to get the lapse rate.

The same was done for the current study as no permanent stations at different altitudes are

available in the study area or in the neighbourhoods.

3.6.3.1. Fieldwork

During the fieldwork, eight temperature sensors were installed at an altitude difference of 100

meters. The data was collected for four weeks, but due to unskilled manpower only last 8 days

temperature recorded data was reliable. The sensors were installed in different villages inside the

houses. Care was taken in installing the sensors under a shadow but due to winds and other

reason few were exposed to direct sun light. This resulted in high temperatures recorded by the

sensors. Therefore only minimum temperatures were used for lapse rate calculations (Table 3.1).

After analyzing the data, the average temperature lapse rate was determined as 0.63 per 100 m

change in altitude which is close to 0.65 proposed by the developers of the SRM model. This

figure (0.63) is used for extrapolating the temperature values of the base station to higher

altitudes.

Table 3.1:Temperature data recorded during fieldwork

Lower

Liakot

(1830m)

Kass

Aryanai

(1910m)

Bishmal

Port

(1960m)

Kalam

Tehsil

(2016m)

Gaheel

(2100m)

Utror

(2229m)

Gibrall

(2300m)

Chorat

(2340m)

Date Tmin(C) Tmin(C) Tmin(C) Tmin(C) Tmin(C) Tmin(C) Tmin(C) Tmin(C)

9/18/2007 13.6 14 15.3 12.5 13.6 9.2 12.9 11.5

9/19/2007 13.1 13.3 14.4 12 13.1 9.1 13.1 10.9

9/20/2007 12.9 12.7 14.2 11.5 12.3 13.4 10.9 10.6

9/21/2007 12.8 12.5 12.8 11 11.5 12.7 12.6 10

9/22/2007 11.3 10.6 11.2 9 9.7 9.2 9.2 7

9/23/2007 10.4 8.7 9.9 8.5 8.7 10.5 8.1 6.9

9/24/2007 10.4 12.3 9.5 8.3 8.1 11.7 7.5 7.1

9/25/2007 10.4 7.5 9 7.7 7.7 14.1 6.4 7.19/26/2007 9.7 8.3 9.9 8.1 7 14 7.1 6.5


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3.6.4. Critical temperature, TCRIT

The critical temperature determines whether the measured or forecasted precipitation is rain or

snow. SRM needs the critical temperature only in the snowmelt season (unless a year round

computer run is made) in order to decide whether precipitation immediately contributes to runoff

(rain), or if T < TCRIT, whether snowfall took place.

Unfortunately event (snow or rain) dependent temperature data is not available in Kalam basin;

however, a nearby station in the west of the study area, namely Drosh has historical records of

hourly temperature values against the events of snow or rain. The data reveals that snow event

has mostly occurred at temperatures below or equal to 2 C in the winter season i.e. from

October to March. No snowfall has been recorded by the station in the melting season, making it

difficult to know the critical temperature for the melting season. Hence compromise on

considering 2 C (April-June) and 0.75 C (July, August) as critical temperature has been made.

3.6.5. Rainfall contributing area, RCA

When precipitation is determined to be rain, it can be treated in two ways. In the initial situation

(option 0), it is assumed that rain falling on the snowpack early in the snowmelt season is

retained by the snow which is usually dry and deep. Rainfall runoff is added to snowmelt runoff

only from the snow-free area, that is to say the rainfall depth is reduced by the ratio snow-free

area/zone area. At some later stage, the snow cover becomes ripe (the user must decide on which

date) and the computer program should be switched to option 1, this was done for the current

study on 1stJune and thereafter. Now, if rain falls on this snow cover, it is assumed that the same

amount of water is released from the snowpack so that rain from the entire zone area is added to

snowmelt. For this study 0 was selected from April to May, and 1 from June-August.

3.6.6. Recession coefficient, k

The recession coefficient is an important feature of SRM because (1-k) is the proportion of the

daily meltwater production which immediately appears in the runoff. Analysis of historical


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discharge data is usually a good way to determine k. Values of Qnand Qn+1 are plotted against

each other and the lower envelope line of all points is considered to indicate the k-values as

shown in the figure below (Graph 3.6).

Graph 3.8:Recession flow plot Qn Vs Qn+1 for Kalam basin in Pakistan. Either the solid lower

envelope line or the dashed medium line is used to determine k-values for computing the

constants x and y.

Based on the relation

1n

n

Q

k Q

+=

(3.4)

In the study area 1k

= 0.85 for nQ

= 200 m3s-1 and 2k

= 0.90 for nQ

= 30 m3s-1. This means

that k is not constant, but increases with the decreasing river discharge according to the

equation:

1

y

n nk x Q

+ =

(3.5)

Where the constants x and y must be determined for a given basin by solving the equations:


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1 1

yk x Q = (3.6)

2 2

yk x Q = (3.7)

1 1log log (log )k x y Q= (3.8)

2 2log log (log )k x y Q= (3.9)

Putting values of 1k

, 2k

and 1Q

, 2Q

in equations (3.8) and (3.9)

log(0.85) log( ) log(200)x y= (3.10)

log(0.90) log( ) log(30)x y= (3.11)

x = 0.997

y= 0.030

Care should be taken in finding the recession flow. It was experienced in this study that when

recession coefficients were calculated considering the discharge data from the middle of the

recession period, the resulted recession coefficients were giving completely strange runoffs.


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4. GIS AND REMOTE SENSING

4.1 Software Used

The SRM model needs daily snow cover as input variable. Daily remote sensing data from

MODIS on board Terra and Aqua satellites were processed using ERDAS and Arc GIS. Exact

and accurate boundary of the catchment is very important as this is used for the masking of

satellite image of the study area. Also the SRM recommend dividing the study area into different

elevation zones if the difference in the base station and the highest altitude is greater than 500

meters. Furthermore, measurements of snow cover in different elevation zones require a DEM as

input. Extraction of catchment/basin of the study area was done using SRTM DEM data with the

help of Integrated Land and Water Information System (ILWIS).

4.2 Digital Elevation Model (DEM)

The Shuttle Radar Topography Mission (SRTM) obtained elevation data on a near-global scale

to generate the most complete high-resolution digital topographic database of Earth. Elevation

data of the area from SRTM was used for the extraction of catchment boundaries above Kalam

river discharge measurement station on river Swat (Figure 4.1).

DEM hydro-processing operations are a set of tools which can lead to the extraction of

catchment as well as its boundary using digital elevation data. Those tools which were used in

this study are fill sink, flow direction, flow accumulation, drainage network extraction, drainage

network ordering, catchment extraction and catchment merge. Detail discussion on each of these

tools and their out put results has been given below.


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Figure 4.1:DEM of the study area obtained from Shuttle Radar Topography Mission (SRTM)

4.2.1. Fill Sink

Before using the Flow Direction operation, clean up of local depressions from DEM data is of

utmost importance. This is done by the Fill Sink operation which performs the following on a

Digital Elevation Model (DEM):

Any pixel with a smaller height value than all of its 8 neighbouring pixels, will be

increased in height to the smallest value of its 8 neighbour pixels

Any group of adjacent pixels where the pixels that have smaller height values than all

pixels that surround a depression will be increased to the smallest value of a pixel that isboth adjacent to the outlet for the depression, and that would discharge into the initial

depression.

The resulting output map of the Fill Sink operation is a so-called sink-free or depression-free

DEM. This means that for every pixel in the DEM, a flow direction will be found towards the

edges of the map.


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4.2.2. Flow Direction

In a (sink-free) Digital Elevation Model (DEM), the Flow Direction operation determines into

which neighbouring pixel any water in a central pixel will flow naturally.

Flow Direction is calculated for every central pixel of input blocks of 3 by 3 pixels, each time

comparing the value of the central pixel with the value of its 8 neighbours. The output map

contains flow direction as N (to the North), NE (to the North East), etc.

4.2.3. Flow Accumulation

The Flow accumulation operation performs a cumulative count of the number of pixels that

naturally drain into outlets. The operation can be used to find the drainage pattern of a terrain.

As input the operation uses the output map of the flow direction operation.

The output map contains cumulative hydrologic flow values that represent the number of

input pixels which contribute any water to any outlets; the outlets of the largest streams,

rivers etc. will have the largest values.

4.2.4. Drainage Network Extraction

The Drainage Network Extraction operation extracts a basic drainage network (Boolean raster

map). The output raster map will show the basic drainage as pixels with value True, while other

pixels have value False.

The output raster map of the Flow Accumulation which is required as input map. This map

contains a cumulative drainage count for each pixel.

4.2.5. Drainage Network Ordering

The Drainage network ordering operation:

Examines all drainage lines in the drainage network map

Finds the nodes where two or more streams meet

Assigns a unique ID to each stream in between these nodes, as well as to the streams that

only have a single node


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discharge measurement station was selected to merge all catchments of the drainages that drain

into this discharge measurement station into a bigger catchment (Figure 4.3).

Figure 4.3: Extracted catchment above Kalam station.

4.3 Remote Sensing of Snow Cover

Snow cover is an important variable for climate and hydrologic models due to its effects on

energy and moisture budget (Najafi Eigdir 2003). Remote sensing is a valuable tool for snow

cover mapping which can be used for predicting snowmelt runoff. From a remote sensing

perspective, snow cover is one of the most readily identifiable measures of water resources from

aerial photography or satellite imagery (Engman 1991).

4.4 Moderate Resolution Imaging Spectroradiometer (MODIS)

With the launch of MODIS in December 1999, a new era in hyperspectral satellite remote

sensing began. MODIS makes it possible to monitor the environment by measuring atmospheric

trace gases and aerosol density, and mapping the surface of clouds, land and sea in a variety of

spectral ranges from the blue to the thermal infra-red.

The first MODIS sensor went into orbit with the launch of the TERRA satellite on December 18,1999. With the successful launch of AQUA from Vandenberg Air Force Base, CA, on May 4,


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2002, a second MODIS sensor was put into orbit for studying the Earth's water cycle and our

environment. TERRA and AQUA (both with a 705km orbit) have a sun-synchronous, near polar,

circular orbit. AQUA will cross the equator daily at 1:30 p.m. as it heads north (ascending mode)

in contrast to TERRA, which crosses the equator at 10:30 a.m. daily (descending mode). With

this formation it is expected that AQUA's afternoon observations combined with TERRA's

morning observations will provide important insights into the daily cycling of global

precipitation and ocean circulation.

MODIS is a 36 band spectrometer providing a global data set every 1-2 days with a 16-day repeat

cycle. The spatial resolution of MODIS (pixel size at nadir) is 250m for channel 1 and 2 (0.6m -

0.9m), 500m for channel 3 to 7 (0.4m - 2.1m) and 1000m for channel 8 to 36 (0.4m -

14.4m), respectively.

Satellite images are made by combining the reflected light detected by the sensor at various

wavelengths (spectral bands) and making them into a single image. The MODIS Rapid Response

System makes use of MODIS broad range of spectral observations by creating both true-color

and false-color images, each tailored to highlight different land surface, atmospheric, and oceanic

features. One such band combination is 721. In this composite, MODIS Bands 7, 2, and 1, areassigned to the red, green, and blue portions of the digital image. Vegetation appears bright green

and bare soil red, and water appears dark black in this combination leaving snow light blue very

prominent and hence making it easy to distinguish snow from all three classes i.e. vegetation,

bare soil, and water.

MODIS Rapid Response System provides on its website a number of image subsets (Figure 4.4)

that are automatically generated in near-real-time for various applications users.


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Figure 4.4: MODIS subset of Afghanistan covering parts of Pakistan including the study area ofKalam. Grey white color is showing snow and ice.

The archive imagery is available online; images of the area for 2004, 2005, and 2006 have been

downloaded from MODIS rapid response system web site (http://rapidfire.sci.gsfc.nasa.gov/subsets).

Georeferencing information is available on the same web site, and those have been used to for

georeferencing. Projection: Plate Carree and ellipsoid: WGS84. Georeferencing has been done

using ERDAS image processing software. To see the snow cover the images should be cloud

free. Normally one can get around 140-150 cloud free images throughout the year, and for four

years the total number of images to be processed for snow extraction was up to 600.


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Figure 4.5:Subset of Afghanistan has been geo-referenced and re-projected to UTM co-ordinates.

Extraction of daily snow cover in the study area has been done in a number of steps using both

ERDAS and ArcGIS image processing softwares.

The MODIS jpeg format images were selected for this study inspite of their limitation due to the

following reasons;

1. MODIS snow cover products were available but with 500m pixel resolution, which is

poor as compared to 250m jpeg images.

2. There was a possibility of getting HDF file format by ordering to MODIS Science Data

Support Team, but the file size was too large, and so were the number of images required

for the study. Secondly they have removed all 250m resolution HDF files from their data

base and they were recreating each file, which needed a lot of time to wait.

3. Georeferencing were available with jpeg images and therefore, it was relatively easy to

carry out the georeferencing.

4.5 Extraction of Study Area Subset

It is clear to see that the subset Afghanistan is covering a large area, but our study area is too

small. This means that subset of study area should be extracted from the whole subset before

going to snow cover mapping. A lot of time on image processing is saved this way. The merged

catchment polygon is used for masking only the image of the study area (Figure 4.6). Extract by

Mask toll in ArcGIS extracts the cells of a raster that correspond with the areas defined by a

mask, which in the present case is the polygon in the form of merged catchment.


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Figure 4.6:Satellite image of study area on the right after masking it from Afghanistan subset.

4.6 Classification of images for extraction of snowAfter all the subsets of the study area, they are classified into snow and snow free classes (Figure

4.7). Both supervised and unsupervised methods have been applied. Classification has been done

in ERDAS.

Figure 4.7:Classified image of the study area. Blue color is showing snow whereas dark brown is

snow free.


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4.7 Extraction of Snow for Each Elevation Zone

Snow cover mapping is the most laborious job in this research, because daily satellite images are

downloaded and if the study area is cloud free then it is processed for the extraction of snow

cover. This leads to processing of almost 400 satellite images for 3 years i.e. 2004-2006. MODIS

images are available in jpeg format as subsets of different regions of the world. The following

steps are involved in getting snow cover from an image.

1. Downloading MODIS subsets (Afghanistan) from the rapid fire website.

2. Rectification of the satellite image using the available corner coordinates from info file

with the image.

3. Classification of each image into

Snowmelt Runoff Investigation in Swat River Upper Basin Using SRM Model

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