SUBMITTED BY SHREYA GUPTA (ROLL NO.11101189), CIVIL ENGG. MMEC, MULLANA-AMBALA UNDER THE GUIDANCE OF MR.PRAMOD KUMAR, SCIENTIST-SF, IIRS, DEHRADUNREPORT ON Assessment Of Snowmelt Runoff For A Himalayan Basin Using Remote Sensing Techniques

DeclarationI, Shreya Gupta, hereby declare that matter embodied in this Project Report, submitted by me in partial fulfillment of the requirement of the award of B. Tech degree in Civil of Maharishi Markandeshwar University, Mullana-Ambala is a record of my own work carried out under the supervision Mr. Pramod Kumar, Scientist-SF, Indian Institute of Remote Sensing, Dehradun. However, to do justice with the nature, scope and extent of this work, the available published/unpublished data have been incorporated/utilized, with due acknowledgement, to draw my own interpretation and conclusion.I, further, declare that the present Project Report has not been published or submitted elsewhere for the award of any other degree or diploma.

Shreya GuptaRoll No. 11101189Civil Engg. DeptMMEC, Mullana-Ambala

AcknowledgementThis project is output of the essence of blessings and good wishes of almighty and my elders as well as support, love and cooperation of my near and dear ones. It gives me immense pleasure to express my appreciation for the assistance rendered to me by all those who helped me in completion of this report.I would like to acknowledge the Maharishi Markandeshwar University, Mullana-Ambala that nominated me for summer-training at the Indian Institute of Remote Sensing, Dehradun, and their support during the whole period of the training.It is a great pleasure to express my deep gratitude to my guide, Mr. Pramod Kumar, Scientist-SF, for his kind support, comments and suggestions in solving the problems and writing the present report, and for his valuable advice on the SRM model.I would like to address my thanks to IIRS faculties and students, especially to Mr. Jakir Husain, for their help and support.A special thanks goes to my parents and siblings for their motivation and spiritual supports.

Shreya GuptaRoll No. 11101189Civil Engg. DeptMMEC, Mullana-Ambala

AbstractSnowmelt is a major source of water in the region, therefore discharge forecasting and estimation of snowmelt for this basin is essential. From the water harvesting point of view for irrigation and drinking, the shortage of water is one of the most important problems in the dry seasons. As snowmelt runoff is one of the main factors in the water budget of the river, a special attention and recognition of the runoff processes is needed. Remote Sensing and Geographical Information System are very important tools in snow monitoring. Calibration of the Snowmelt Runoff Model (SRM) for the study area is the main objective of this internship that simulates the daily snowmelt runoff.Present study describes the use of Snowmelt Runoff Model (SRM - a degree-day-driven snowmelt model) aided with remote sensing satellite and meteorological data for the estimation of snowmelt runoff in the Joshimath watershed of Himalayas for the year 2003-04. ArcGIS 9.3 have been used for creation of database required for simulation of streamflow. The watershed was divided into 13 elevation zones each with elevation difference of 500 meters. MODIS snow data products have been used to obtain SCA maps. Daily temperature (min and max) and precipitation data has been extrapolated to each zone using observed data obtained from Indian Meteorological Department. All the data was used to create the WinSRM database. Measured Runoff data for the year 2003-04 has been used as an input for the running the simulation and validating the model results. The simulation revealed the volume difference of 6.6338% between measured runoff (5381.651106 m3) and computed runoff (5024.645106 m3). Results showed that the simulation was successful, because the coefficient of determination (R2) was 0.5489. Keeping in view the agreement between measured runoff and computed runoff, SRM proved to be a reliable tool for estimating the snowmelt runoff especially in the un-gauged mountainous snow covered catchments.

ContentsS.No.ChaptersPage No.

1.List of Abbreviations Used7

2.List of Figures8-9

3.Chapter 1 Introduction10-24

1.1 Project Rationale10

1.2 Introduction to the Indian Institute of Remote Sensing11

1.3 Introduction to ARC Hydro12

1.4 Introduction to Snowmelt Runoff Model12

1.4.1 Model Structure13

1.4.2 Input Data for Running the Model15

1.4.3 Snowmelt-Runoff Model for Windows21

1.5 Objective of the Report21

1.6 Study Area22

1.6.1 Location22

1.6.2 Climate23

1.6.3 Temperature23

1.6.4 Rainfall23

1.6.5 Humidity23

1.6.6 Winds23

4.Chapter 2 Review of Literature25-26

2.1 General25

2.2 Snowmelt studies for different basins25

5.Chapter 3 Methodology and Data Used27-39

3.1 Data Used27

3.1.1 DEM of Rudraprayag27

3.1.2 Snow covered Area27

3.1.3 Temperature Data28

3.1.4 Precipitation Data28

3.2 Methodology29

6.Chapter 4 Results and Discussions40-47

7.Conclusion48

8.References49-50

List of Abbreviations UsedASTERAdvanced Spaceborne Thermal Emission and Reflection Radiometer m.s.l mean sea levelCSSTEAPCentre for Space Science and Technology Education in Asia and the Pacific CMG Climate Modeling GridDEMDigital Elevation ModelEOSEarth Observation SystemFSCFractional Snow Cover GDEMGlobal Digital Elevation ModelGISGeographic Information System IIRSIndian Institute of Remote SensingIPIIndian Photo-interpretation InstituteISROIndian Space Research OrganisationISRSIndian Society of Remote Sensing METIMinistry of Economy, Trade and IndustryMODISModerate Resolution Imaging SpectroradiometerNASANational Aeronautics and Space AdministrationSWIRShortwave InfraredSCASnow Covered AreaSRMSnowmelt Runoff ModelTIFF Tagged Image File FormatTIR Thermal InfraredVNIR Visible and Near Infraredi.e. that isR2 Nash-Sutcliff coefficient of determinationDv volume difference

List of Tables & FiguresTABLES:Pg. No.Table 1.1 Win SRM capabilities and limitations.Table 1.2 Mountains close to Joshimath...FIGURES:Fig. 1.1 Study Area (India > Uttarakhand > Chamoli District > Joshimath).Fig. 1.2 The topographic map of Joshimath and the surrounding area derived from satellite mapping.The topographic data has been illuminated by a light source corresponding to the position of the sun at midafternoon in summer. Fig. 3.1 Window of Arc Hydro showing DEM of Rudraprayag..Fig. 3.2 Flow Direction...Fig. 3.3 Flow Accumulation..........Fig. 3.4 Stream Definition..Fig. 3.5 Stream SegmentationFig. 3.6 Catchment Grid DelineationFig. 3.7 Catchment Polygon Processing..Fig. 3.8 Drainage Line Processing........Fig. 3.9 Adjoint Catchment Processing...Fig. 3.10 Drainage Point ProcessingFig. 3.11 Longest Flow Path for Catchments.Fig. 3.12 Longest Flow Path for Adjoint CatchmentsFig. 3.13(a) Batch Point Generation..Fig. 3.13(b) Batch Point Generation.Fig. 3.14 Batch Watershed DelineationFig. 3.15 Basin Description Table (Basin Definition).Fig. 3.16 Zone Description Table (Zone Definition)..Fig. 3.17 Basin Simulation GridFig. 3.18 Workflow.Fig. 4.1 Output of Flow DirectionFig. 4.2 Output of Flow AccumulationFig. 4.3 Output of Stream DefinitionFig.4.4 Output of Stream Segmentation..Fig. 4.5 Output of Catchment Grid DelineationFig. 4.6 Output of Catchment Polygon ProcessingFig. 4.7 Output of Drainage Line Processing..Fig. 4.8 Output of Adjoint Catchment Processing..Fig. 4.9 Output of Drainage Point Processing.Fig. 4.10 Output of Longest Flow Path for CatchmentsFig. 4.11 Output of Longest Flow Path for Adjoint Catchments..Fig. 4.12(a) Output of Batch Watershed Delineation (watershed point).Fig. 4.12(b) Output of Batch Watershed Delineation (watershed)Fig. 4.13 Masked DEM of Joshimath from the DEM of Rudraprayag..Fig. 4.14 DEM of Joshimath showing its 13 elevation zones..Fig. 4.15 Statistics, Year Round Simulation (Final Result)

Chapter 1Introduction1.1 PROJECT RATIONALESnow is a form of precipitation, but, in hydrology it is treated somewhat differently because of the lag between when it falls and when it produces runoff, groundwater recharge, and is involved in other hydrologic processes. The hydrologic interest in snow is mostly in mid- to higher latitudes and in mountainous areas where a seasonal accumulation of a snowpack is followed by an often lengthy melt period that sometimes lasts months. During the accumulation period there is usually little or no snowmelt. Precipitation falling as snow (and sometimes rain) is temporarily stored in the snowpack until the melt season begins.In hydrology, snowmelt is surface runoff produced from melting snow. It can also be used to describe the period or season during which such runoff is produced. Water produced by snowmelt is an important part of the annual water cycle in many parts of the world, in some cases contributing high fractions of the annual runoff in a watershed. Snow and glacier melt runoff is very important, particularly, in lean season and it plays a vital role in making nearly all the rivers originating in Himalayas which are perennial. It is also important especially for water supply, flood control, hydropower generation and irrigation. Snow starts melting just after winter season is over, i.e., in the month of March and reaches minimum after monsoon season. Snowmelt can substantially contribute to the overall discharge of a river and hence is the importance of estimating snowmelt runoff. Rapid snowmelt can cause flooding. If the snowmelt is then frozen, very dangerous conditions and accidents can occur. Snow forms a natural reservoir, storing water for weeks or months; if properly harnessed it can be used for water supply, agriculture and industry and energy production. The majority of rivers originating from the Himalayas have their upper catchment in the snow covered areas. The solid precipitation results in temporary storage and this storage supply water in the river system during snowmelt season. Because of the above stated uses, it is important to estimate the snowmelt runoff.It is important for hydrologist to know how much water is stored in a basin in the form of snow and the areal distribution of the snow, its condition and the presence of liquid water in it. In general, all these indicators of snow are difficult to measure and are likely to vary considerably from point to point, especially in mountainous terrain.In the last 20 years remote sensing research has led to significant progress in monitoring and measuring certain snow hydrology processes. There are numerous applications of snow detecting, snow water equivalent assessments and snowmelt runoff predictions. For each study, detecting snow cover with remote sensing is very vital and can be achieved by gamma rays, visible and near infrared, thermal infrared and microwaves (Rango, 1993). Snow distribution in a drainage basin can be adequately assessed by visible sensors. Remote sensing offers a new and valuable tool for obtaining snow data for predicting snowmelt runoff. Historically, snow data have been obtained manually by means of snow courses, which are extremely labour intensive, expensive and potentially dangerous. Even when available, snow course data represent only a point and, at best, can only be used as an index of available snow water content. Recent use of telemetering of snow pillow and storage gauge measurements of precipitation has reduced the need for some fieldwork but has not overcome the problem of the point measurements of snow. That is, although measurements are automated, there is still the problem that a single point measurement may or may not be representative of a large area or basin. From a remote sensing perspective, snow cover is one of the most readily identifiable measures of water resources from aerial photography or satellite imagery. Present operational satellite systems are limited to determining only the area of snow cover, snow depth and snow water equivalent; snow physical parameters cannot be measured directly by these systems. Snow cover can be detected and monitored with a variety of remote sensing devices.1.2 INTRODUCTION TO INDIAN INSTITUTE OF REMOTE SENSINGLocated in Dehradun, Uttarakhand, it functions as a constituent unit of Indian Space Research Organisation (ISRO), Department of Space, Government of India. Formerly known as Indian Photo-interpretation Institute (IPI), founded in 1966, the Institute boasts to be the first of its kind in entire South-East Asia. While nurturing its primary endeavour to build capacity among the user community by training mid-career professionals, the Institute has enhanced its capability and evolved many training & education programmes that are tuned to meet the requirements of various target groups, ranging from fresh graduates to policy makers including academia.Indian Institute of Remote Sensing (IIRS) under Indian Space Research Organisation, Department of Space, Govt. of India is a premier Training and Educational Institute set up for developing trained professionals in the field of Remote Sensing, Geoinformatics and GPS Technology for Natural Resources, Environmental and Disaster Management. It is the institution where the present study was done in the laboratory of Digital Image Processing and Facility Management Group under the guidance of Mr. Pramod Kumar, Scientist SF.The Institute campus also houses the headquarters of the Centre for Space Science and Technology Education in Asia and The Pacific (CSSTEAP), affiliated to the United Nations and first of its kind established in the region in 1995. IIRS provides support to conduct all its remote sensing and GIS training & education programmes at postgraduate level. The headquarters of Indian Society of Remote Sensing (ISRS), the largest non-governmental scientific society in the country, is also located in the Institute campus.1.3 INTRODUCTION TO ARC HYDROArcGIS is a geographic information system (GIS) for working with maps and geographic information. It is used for: creating and using maps; compiling geographic data; analyzing mapped information; sharing and discovering geographic information; using maps and geographic information in a range of applications; and managing geographic information in a database.Arc Hydro is a set of data models and tools that operates within ArcGIS to support geospatial and temporal data analyses. Arc Hydro is used to delineate and characterize watersheds in raster and vector formats, define and analyze hydro geometric networks, manage time series data, and configure and export data to numerical models.Arc Hydro is an ArcGIS-based system geared to support water resources applications. It consists of two key components: Arc Hydro Data Model Arc Hydro ToolsThese two components, together with the generic programming framework, provide a basic database design and a set of tools that facilitate the analyses often performed in the water resources arena. Arc Hydro is intended to provide the initial functionality that can then be expanded by adding to it database structures and functions required by a specific task or application.The Arc Hydro tools operate in the ArcGIS environment. Some of the functions require the Spatial Analyst extension. The majority of the tools are accessed through the Arc Hydro Tools toolbar, where they are grouped by functionality into six menus and nine tools. Additional tools have been developed in the geoprocessing environment and are available in the Arc Hydro Tools toolbox that can be used both in ArcMap and in ArcCatalog.The Arc Hydro tools have two key purposes. The first purpose is to manipulate (assign) key attributes in the Arc Hydro data model. These attributes form the basis for further analyses. They include the key identifiers (such as HydroID, DrainID, NextDownID, etc.) and the measure attributes (such as LengthDown). The second purpose for the tools is to provide some core functionality often used in water resources applications. This includes DEM-based watershed delineation, network generation, and attribute-based tracing.1.4 INTRODUCTION TO SNOWMELT RUNOFF MODELIt is known that the effect of global climate change on hydrologic systems, especially on mountain snow and glacier melt, can modify the timing and amount of runoff in mountainous watersheds. Therefore, accurate streamflow simulation and forecast is of great importance to water resources management and planning, and can provide a firm basis for forecasts of water resources availability while minimizing the risk and loss from floods caused by rapid snow and glacier melt. Several snowmelt forecasting models have been developed to suit specific needs and hydrologic conditions. They are data-intensive and/or complicated to handle. The snowmelt runoff model (SRM), which uses snow cover information and meteorological data as input variables, has been most widely used both in simulation and forecast. The Snowmelt-Runoff Model (SRM) is designed to simulate and forecast daily streamflow in mountain basins where snowmelt is a major runoff factor. Most recently, it has also been applied to evaluate the effect of a changed climate on seasonal snow cover and runoff. SRM was developed by Martinec (1975) in 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. Contrary to theoriginal assumptions, there appear to be no limits for application with regard to the basin size and the elevation range. Also, a dominant role of snowmelt does not seem to be a necessary condition. It is, however, advisable to carefully evaluate the formula for the recession coefficient.

SRM can be used for the following purposes: Simulation of daily flows in a snowmelt season, in a year, or in a sequence of years. The results can be compared with the measured runoff in order to assess the performance of the model and to verify the values of the model parameters. Simulations can also serve to evaluate runoff patterns in ungauged basins using satellite monitoring of snow covered areas and extrapolation of temperatures and precipitation from nearby stations. Short term and seasonal runoff forecasts. The computer program (WinSRM) includes a derivation of modified depletion curves which relate the snow covered areas to the cumulative snowmelt depths as computed by SRM. These curves enable the snow coverage to be extrapolated manually by the user several days ahead by temperature forecasts so that this input variable is available for discharge forecasts. The modified depletion curves can also be used to evaluate the snow reserves for seasonal runoff forecasts. The model performance may deteriorate if the forecasted air temperature and precipitation deviate from the observed values, but the inaccuracies can be reduced by periodic updating. In recent years, SRM was applied to the new task of evaluating the potential effect of climate change on the seasonal snow cover and runoff. The microcomputer program has been modified and supplemented accordingly.

1.4.1 Model structureEach 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 according to Equation (1): (1)

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 oC-1d-1] indicating the snowmelt depth resulting from 1 degree-dayT = number of degree-days [oC 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 [oC d]S = ratio of the snow covered area to the total areaP = precipitation contributing to runoff [cm]. A preselected threshold temperature, TCRIT, determines whether this contribution is rainfall and immediate. If 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: (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.

T, S and P are variables to be measured or determined each day, cR, cS, lapse rate to determine T, TCRIT, k and the lag time are parameters which are characteristic for a given basin or, more generally, for a given climate. If the elevation range of the basin exceeds 500 m, it is recommended that the basin be subdivided into elevation zones of about 500 m each. For an elevation range of 1500 m and three elevation zones A, B and C, the model equation becomes:

The indices A, B and C refer to the respective elevation zones and a time lag of 18 hours is assumedThe above two equations are written for the metric system but an option for model operation in English units is also provided in the computer program.1.4.2 Input Data for Running the Model The input data can be grouped as follows: Basin Characteristics Variables Parameters1.4.2.1 Basin characteristics1.4.2.1.(a) Basin and zone areas The basin boundary is defined by the location of the stream gauge (or some arbitrary point on the streamcourse) and the watershed divide is identified on a topographic map. The basin boundary can be drawn at a variety of map scales. For the larger basins, a 1:250,000 scale map is adequate. After examining the elevation range between the stream gauge and the highest point in the basin (total basin relief), elevation zones can be delineated in intervals of about 500 m or 1500 ft. In addition to drawing the basin and zone boundaries, several intermediate topographic contour lines should be highlighted for later use in constructing the area-elevation curve. Once the boundaries and the contours have been determined, the areas formed by these boundaries should be planimetered manually or automatically. And after defining the zones, the various model variables and parameters are applied to each zone for the calculation of snowmelt runoff. To facilitate this application, the mean hypsometric elevation of the zone must be determined through use of an area-elevation curve. Many of these steps can be expedited through the use of computer analysis and a Digital Elevation Model (DEM).1.4.2.1.(b) Area-elevation curveBy using the zone boundaries plus other selected contour lines in the basin, the areas enclosed by various elevation contours can be determined by planimetering. These data can be plotted (area vs elevation) and an area-elevation (hypsometric) curve is derived. The zonal mean hypsometric elevation, h can then be determined from this curve by balancing the areas above and below the mean elevation. The h value is used as the elevation to which base station temperatures are extrapolated for the calculation of zonal degree-days.

1.4.2.2 VariablesVariables basically describe the actual meteorological conditions for the simulated period. These data have to be obtained preferably from in situ measurements, but the snow cover, for example, can be also defined by remote sensing.1.4.2.2.(a) Temperature and degree-days, TIn order to compute the daily snowmelt depths, the number of degree-days must be determined from temperature measurements or, in a forecasting mode, from temperature forecasts.Temperature average Program options:0 = daily mean1 = Min, MaxThe program accepts either the daily mean temperature (option 0) or two temperature values on each day: TMax, TMin (option 1). The temperatures are extrapolated by the program from the base station elevation to the hypsometric mean elevations of the respective elevation zones. For option 1, the average temperature is computed in each zone as: (3) When using daily means (option 0) or when using TMax, TMin (option 1), it is recommended that negative temperature values (when they occur) be used in the calculation.Because the average temperatures refer to a 24 hour period starting always at 0600 hrs, they become degree-days T [Cd]. The altitude adjustment T in Equation (1) is computed as follows: (4)Where altitude of the temperature station[m] Whenever the degree-day numbers (T + T in Equation (1)) become negative, they are automatically set to zero so that no negative snowmelt is computed.1.4.2.2.(b) Precipitation, PThe evaluation of representative areal precipitation is particularly difficult in mountain basins. Also, quantitative precipitation forecasts are seldom available for the forecast mode, although current efforts in this field are improving this situation. Fortunately, snowmelt generally prevails over the rainfall component in the mountain basins. However, sharp runoff peaks from occasional heavy rainfalls must be given particular attention and the program includes a special treatment of such events.Rainfall input Program options:0 = basin wide1 = by zoneThe program accepts either a single, basin-wide precipitation input (from one station or from a "synthetic station" combined from several stations, that is, option 0) or different precipitation inputs zone by zone (option 1). If the program is switched to option 1 and only one station happens to be available, for example in the zone A, precipitation data entered for zone A must be copied to all other zones. Otherwise no precipitation from these zones is taken into account by the program. A critical temperature is used to decide whether a precipitation event will be treated as rain (T>=Tcrit) or as new snow (T Uttarakhand > Chamoli District > Joshimath)

1.6.1 LocationJyotirmath, also known as Joshimath is a city and a municipal board in Chamoli District in the Indian state of Uttarakhand. Located at a height of 6150 feet, it is gateway to several Himalayan mountain climbing expeditions, trekking trails and pilgrim centres like Badrinath. The area is situated at 30.57 N latitude, 79.57 E longitude and 2073m elevation above the sea level. Situated in the Himalayan ranges above the rivers Alaknanda and Dhauliganga, Joshimath is the base for travelers visiting Hemkund Sahib and Valley of flowers. Joshimath is connected to Auli by 4 km cable car, making it the longest and highest trolley of Asia. Joshimath offers scenic views of the Himalayas, greenery and pleasant weather conditions. There are several ancient temples for tourists to visit. Joshimath is also the winter home of Lord Badrinarayn when the temple at Badrinath closes.

S. No.Name of the peakLocation

1.Hathi Parvat (closest mountain peak)18km NE

2.Nanda Ghunti (second closest mountain peak)18km SE

3.Nilkantha (third closest mountain peak)23km WNW

4.Dunagiri (fourth closest mountain peak)26km E

Table 1.2 Mountains close to Joshimath1.6.2 ClimateThe climate in the town of Joshimath is cold throughout the year. Even the summers are mildly cold in the town of Joshimath. Heavy rainfall can be expected during the monsoon season and extensive showers can be witnessed through the day. Winters are extremely chilly with temperatures touching the freezing point. Snowfall can also be expected in this area during the winter season. 1.6.3 TemperatureThe temperatures during the summer season are mild and they can rise up to a maximum of around 34C. However the average day temperatures are usually in the mid twenties for the most part. The months of April, May and June form the summer season. The winter season starts in December and ends in the month of February. Temperatures can drop down to around 0C during the winter months and snowfall can be seen from the month of December. The temperatures rarely exceed 8C during the winter months. 1.6.4 RainfallThe monsoon season is from July to September. The place receives rainfall from the South West Monsoon and travelling during this time is difficult. Conditions can be cold during the nights as well. 1.6.5 HumidityThe relative humidity is high during monsoon season, generally exceeding 70% on the average. The driest part of the year is the pre-monsoon period when the humidity may drop to 35% during the afternoon. The normal annual average humidity is 64% during morning and 56% during evening.1.6.6 WindsOwing to the nature of terrain local affect are pronounced and when the general prevailing winds not too strong to mask these effect, there is a tendency for diurnal reversal of winds, the flow being anabatic during the day and katabatic at night, the latter being of considerable force.

Fig. 1.2 The above topographic map of Joshimath and the surrounding area has been derived from satellite mapping. The topographic data has been illuminated by a light source corresponding to the position of the sun at mid afternoon in summer. Major roads, railways rivers and other water features are derived from global GIS data. ( Source: http://www.weather-forecast.com/locations/Joshimath )

Chapter 2 Review of Literature2.1 GENERALGlaciers in the Himalayas have been reported to be receding rapidly due to the Global climatic change. In light of the economic and ecological importance of the glaciers, it is imperative to assess these resources and their water potential periodically. In the mountainous and inaccessible areas the satellite images accompanied with GIS tools and simulation models have proved to be of great importance to determine and establish a link between theoretical principles and ground situations. The Snowmelt Runoff Model (SRM) is designed to compute daily stream discharge using snow cover data for a basin divided into elevation zones. It has been successfully employed in different catchment areas worldwide in mountain basins of almost any size (so far from 0.76 to 120,000 km2) and any elevation range (for example 305-7690 m a.s.l.) to estimate and forecast snowmelt runoff (Martinec and Rango, 1998). Snowmelt runoff forecasts are of high interest for flood warning and for the management of reservoirs for hydropower generation in drainage basins with significant snowmelt contribution, (Thomas Nagler, 2006). As snowmelt runoff is one of the main factors in the water budget of the river, a special attention and recognition of the runoff processes is needed. Good accuracy in snowmelt runoff analysis is needed for water resource management in snowy mountainous regions, (Norio Hayakawa and Minjiao Lu 2000). Besides estimation and prediction of snowmelt runoff and resulting stream flow from various catchments is of considerable importance for planning and for understanding various hydro-meteorological processes. This chapter gives the brief details of the studies done in past to estimate the snowmelt runoff using SRM.2.2 SNOWMELT STUDIES FOR DIFFERENT BASINSPrasad and Roy (2005) estimated snowmelt runoff on a 10 day average basis in Beas Basin upto Pandoh Dam during May, 1998 and November, 1999 using a Snowmelt Runoff Model. The input parameters for the model were derived from existing maps, satellite data, metrological and hydrological data. Snow covered area in the basin was determined using Indian Remote Sensing Satellites IRS - 1C / 1D Wide Field Sensor (WIFS). The model parameter degree-day factor was taken from literature and runoff coefficients for snow and rain were derived using the observed data. The total discharge at the dam site was computed by a weighted sum of runoff components from all the elevation zones. There was a good agreement between the observed and computed runoff with a coefficient of determination of 0.854 and the difference in volume was + 4.6 %.Jain, Lohani and Singh made an attempt to estimate snowmelt runoff in Chamkharchu basin, Bhutan. The information on snow cover area was determined from the satellite data. After successful calibration of the model for a period of two years (2004-2005 and 2005-2006), the model was run to simulate daily streamflow up to Kurjey site using independent data for two years (2006-2007 and 2007-2008). The overall efficiency of the model over the study period of two years was 0.91 and 0.90 for 2006-07 and 2007-08 respectively and the difference in volume of computed and observed streamflow was 9.17% and 1.62%. The results indicate that the model performed equally well for the simulation years as for the calibration years.Fuladipanah and Jorabloo used SRM to estimate daily discharge in Gharasoo basin, West-North of Iran during the 20th of February 1998 to 5th of June 1998 using Snowmelt Runoff Modeling(SRM) which is based on a degree-day method. Data from the 19th of February 1997 to 21st of May 1997 were used for calibration period. Snow cover percentage for each zone was calculated using snow line equation. Isohyetal map was applied to estimate mean precipitation for each zone. Critical temperature was considered 2C for the catchment. Snow cover percentage for each zone was estimated using snow-line method. Exponential regression equation was used to estimate Degree-Day factor for zone areas. Snowmelt and rainfall runoff coefficients were calculated using calibration period data. R2 and Dv were the two criteria used to estimate model accuracy. These criteria were (0.7811, -14.24%) and (0.7956,-9.21%) during calibration and validation periods, respectively. There was a good agreement between measured and calculated daily discharge.Alam(et. al) described the use of Snowmelt Runoff Model (SRM - a degree-day-driven snowmelt model) aided with remote sensing satellite data for the estimation of snowmelt runoff in the Kolahoi watershed of western Himalayas for the year 2001. The simulation revealed the volume difference of only 7.8 % between measured runoff (79.1m 3/sec) and computed runoff (72.9m 3/sec). Keeping in view the agreement between measured runoff and computed runoff, SRM proved to be a reliable tool for estimating the snowmelt runoff especially in the un-gauged mountainous snow covered catchments.Hong Ma and Guodong Cheng, in order to verify the applicability of SRM in an environment of continental climate, performed a test of SRM for the Gongnaisi River basin in the western Tianshan Mountains. The results showed that two SRM average goodness-of-fit statistics for simulations, Nash-Sutcliff coefficient (R2) and volume difference (Dv), were 0.87 and 0.90%, respectively. As compared with the application results over 80 basins in 25 different countries around the world, SRM performed well in the Gongnaisi River basin. The results also showed that SRM can be a validated snowmelt runoff model capable of being applied in the western Tianshan Mountains. On the basis of snowmelt runoff simulation, together with a set of simplified hypothetical climate scenarios, SRM was also used to simulate the effects of climate change on snow cover and the consecutive snowmelt runoff. For a given hypothetical temperature increase of 4C, the snow coverage and snowmelt season shifted towards earlier dates, and the snowmelt runoff, as a result, was changed significantly at the same time. The simulation results showed that the snow cover is sensitive to changes of climate, especially to the increase of temperature.Chapter 3Methodology & Data Used3.1 DATA USED For simulations of runoff using SRM, the main data used include daily data on temperature (min, max), precipitation, snow cover and discharge.3.1.1 DEM of RudraprayagTo obtain elevation zones for snowmelt runoff study, Digital Elevation Model (DEM) is required. Due to non availability of topographic maps, DEM generated using ASTER has been used. ASTER (Advanced Space-borne Thermal Emission and Reflection Radiometer sensor) is an imaging instrument that is flying on the TERRA satellite launched in December 1999 as part of NASA's Earth Observing System (EOS). ASTER represents a revolution in the remote sensing community because of the availability of its imagery and its superior resolution. ASTER resolution ranges from 15m to 90m, depending on the wavelength. The instrument records in three bands: the Visible and Near Infrared (VNIR), the Shortwave Infrared (SWIR), and the Thermal Infrared (TIR), oriented on the nadir and backward looking. There are 14 spectral bands all together spanning the visible and infrared spectra, so the sensor is susceptible to cloud cover and cannot record images at night. Because of its off-nadir sensor pointing capability, ASTER can collect the stereo pairs necessary to generate high resolution DEMs (using bands 3N and 3B).The ASTER onboard the Terra satellite has produced 30 m resolution elevation data. There is a fairly complete coverage of world at this high resolution and data are free. Japans Ministry of Economy, Trade and industry (METI) and NASA announced the release of the ASTER Global Digital Elevation Model (GDEM) on June 29, 2009. The GDEM was created by stereo-correlating the 1.3 million scene ASTER VNIR archive, covering the Earths land surface between 83N and 83S latitudes. The GDEM is produced with 30 meter postings, and is formatted in 1x1 degree tiles as Geo TIFF files. The GDEM is referenced to the WGS84/EGM96 geoid. The GDEMs pre-production accuracy estimates were 20 meters at 95% confidence for vertical data, and 30 meters at 95% confidence for horizontal data. For this study, grids covering the study area have been obtained from internet and DEM of the study area has been prepared.3.1.2 Snow Covered AreaBeing one of the three main input variables of SRM, SCA is of great significance for model accuracy. Errors in determining SCA are directly proportional to the resulting errors in the calculated snowmelt .In order to obtain SCA, high temporal resolution optical remote sensing data: Terra Moderate Resolution Imaging Spectroradiometer (MODIS) was used in the present study. The MODIS is a 36-channel visible to thermal-infrared sensor that was first launched as part of the Earth Observing System (EOS) Terra payload on 18 December 1999. A second MODIS was launched as part of the payload on the Aqua satellite on May 4, 2002. A variety of snow and ice products is produced from the MODIS sensors, and the products are available at a variety of spatial and temporal resolutions as shown in the table below. The MODIS snow product suite begins with a 500-m resolution, 2330-km swath snow-cover map which is then gridded to a sinusoidal grid. The standard MODIS daily global fractional snow cover (FSC) climate modeling grid (CMG) map product is generated at 0.05 resolution (about 5 km at the equator).The first product, MOD10_L2, is a snow cover map at 500 m spatial resolution for a swath. The snow map is the result of the algorithm identifying snow and other features in the scene. Geolocation data (latitude and longitude) at 5 km resolution is stored in the product. The second product, MOD10L2G, is a multidimensional data product created by mapping the pixels from the MOD10_L2 granules for a day to their Earth locations on the sinusoidal map projection, thus multiple observations, i.e. pixels, covering a geographic location (cell) in the tile are "stacked" on one another. Information on the pixels mapped into a cell is stored in pointer and geolocation products associated with the L2G product. The third product, MOD10A1, is a tile of daily snow cover at 500 m spatial resolution. The daily observation that is selected from multiple observations in a MOD10L2G cell is selected using a scoring algorithm. The fourth product, MOD10C1, is a daily global snow cover map in a geographic map projection. It is 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. The fifth product, MOD10A2, is an eight-day composite of snow cover. It is made by compositing from two to eight days of the MOD10A1 product. The sixth product, MOD10C2, is an eight-day composite of snow cover at the same resolution as MOD10C1. It is made by compositing the MOD10A2 product. In the present study, MOD10A2 snow product was used.3.1.3 Temperature DataTemperature is one of the most important climatologic parameters in SRM. It is used to determine snowmelt process and type of precipitation. It also affects snow accumulation. The temperature data for the studied area was collected from the Indian Meteorological Department. There are two choices how temperature measurements or temperature forecasts can be entered to model:(a) Maximum and minimum temperature values in a day (e.g. Max 34C, Min 24C)(b) Daily average temperature values (e.g. Avg 29C).3.1.4 Precipitation DataPrecipitation is again one of the most important climatological parameters, which includes- as the two most important types- rain and snow. Because the study area is mountainous, a large part of the precipitation is in the form of snow that, by gradual increase in temperature in the spring, melts and causes runoff. And the data was obtained from the Indian Meteorological Department which was extrapolated. 3.2 METHODOLOGYThe methodology adopted for the present work involved following steps:1. DEM of Rudraprayag was accessed as an ASTER data from http://earthexplorer.usgs.gov/ .2. DEM of Rudraprayag was used in Arc Hydro.

Fig. 3.1 Window of Arc Hydro showing DEM of Rudraprayag3. Following terrain preprocessing were applied:3.(a) Flow Direction: This tool computes the flow direction grid for an input Hydro DEM. The values in the cells of the flow direction grid indicate the direction of the steepest descent from that cell. Select Terrain Preprocessing > Flow Direction. Specify rudraprayag.img as input Hydro DEM and rename the output flow direction grid Fdr. Leave the optional input Outer Wall Polygon blank or Null and click OK. The tool generates the Flow Direction Grid associated to the input Hydro DEM.

Fig. 3.2 Flow Direction3.(b) Flow Accumulation: This tool generates the Flow Accumulation grid that contains the accumulated number of cells upstream of a cell, for each cell in the input grid. Select Terrain Preprocessing > Flow Accumulation. Select Fdr as input Flow Direction Grid. Click OK.

Fig. 3.3 Flow Accumulation The tool generates the output flow accumulation grid and adds it to the map.3.(c) Stream Definition: This tool generates the stream grid for an input flow accumulation grid and a threshold. Select Terrain Preprocessing > Stream Definition. Select Fac as input Flow Accumulation Grid and Str as input Stream Grid.

Fig. 3.4 Stream Definition

The tool generates the output stream grid and adds it to the map.3.(d) Stream Segmentation: This function creates a grid of stream segments that have a unique identification. A segment may either be a head segment, or it may be defined as a segment between two segment junctions. All the cells in a particular segment have the same grid code that is specific to that segment. The input Sink Watershed Grid and Sink Link Grid are optional and may be used to mask the input stream grid so that no stream links are created in those areas. Select Terrain Preprocessing > Stream Segmentation. Specify the input stream grid and flow direction grid and the output stream link grid, and keep the optional Link Grid input blank. Click OK.

Fig. 3.5 Stream Segmentation

The tool generates the output stream link grid Lnk and adds it to the map.3.(e) Catchment Grid Delineation: This function creates a grid in which each cell carries a value (grid code) indicating to which catchment the cell belongs. The value corresponds to the value carried by the stream segment that drains that area, defined in the input stream segment link grid (Stream Segmentation). Select Terrain Preprocessing > Catchment Grid Delineation. Select Fdr as input Flow Direction grid and Lnk as input Link Grid. Click OK.

Fig. 3.6 Catchment Grid Delineation The tool generates the output catchment grid and adds it to the map.3.(f) Catchment Polygon Processing: This tool generates the catchment polygon feature classes corresponding to the input catchment grid. Select Terrain Preprocessing > Catchment Polygon Processing. Specify the input catchment grid and output catchment polygon feature class. Click OK.

Fig. 3.7 Catchment Polygon Processing

The tool generates the output catchment polygon feature class and adds it to the map.3.(g) Drainage Line Processing: This function converts the input Stream Link grid usually created with the Stream Segmentation function into a Drainage Line feature class. Each line in the feature class carries the identifier of the catchment in which it resides. Select Terrain Preprocessing > Drainage Line Processing. Select Lnk as input Stream Link Grid and Fdr as input Flow Direction Grid. Rename the output DrainageLine and click OK.

Fig. 3.8 Drainage Line Processing The tool generates the output drainage line feature class and adds it to the map. 3.(h) Adjoint Catchment Processing: This tool generates the adjoint catchment polygon feature class associated to an input catchment and drainage line feature classes, i.e. this function generates the aggregated upstream catchments from the "Catchment" feature class. For each catchment that is not a head catchment, a polygon representing the whole upstream area draining to its inlet point is constructed and stored in a feature class that has an "Adjoint Catchment" tag. This feature class is used to speed up the point delineation process. Select Terrain Preprocessing > Adjoint Catchment Processing and click OK. The tool generates the output adjoint catchment polygon feature class and adds it to the map.

Fig. 3.9 Adjoint Catchment Processing

3.(i) Drainage Point Processing: This tool generates the drainage point feature class associated to an input catchment feature class and flow accumulation grid. Drainage Point features represent the location of the cell with the maximum flow accumulation value within each catchment.

Select Terrain Preprocessing > Drainage Point Processing and click OK.

Fig. 3.10 Drainage Point Processing The tool generates the output Drainage Point feature class and adds it to the map.3.(j) Longest Flow Path for Catchments: This function allows generating the longest flow paths associated to the catchments. This is required to speed up the generation of Longest Flow Paths. Select Terrain Preprocessing > Longest Flow Path for Catchments Confirm that the input to Flow Direction Grid is Fdr, and the input to Catchment is Catchment. Click OK.

Fig. 3.11 Longest Flow Path for Catchments Upon successful completion of the process, the longest flow path for catchments feature class is added to the map.3.(k) Longest Flow Path for Adjoint Catchments: This function allows generating the longest flow paths associated to the adjoint catchments. Select Terrain Preprocessing > Longest Flow Path for Adjoint Catchments. Confirm that the input to Flow Direction Grid is Fdr, the input to Adjoint Catchment is watershed. Click OK.

Fig. 3.12 Longest Flow Path for Adjoint Catchments The tool generates the output Longest Flow Path for Adjoint Catchments feature class and adds it to the map.4. Apply following Watershed Processing: The steps in Terrain Preprocessing need to be performed before the watershed delineation functions may be used. The preprocessing functions partition terrain into manageable units to allow fast delineation operations.4.(a) Batch Watershed Delineation: This function performs batch watershed delineation for points in an input Batch Point feature class.To create the Batch Point input file: Click on the icon in the Arc Hydro Tools toolbar. Rename the batch point feature class is BatchPoint1.

Fig. 3.13(a) Batch Point Generation

Click with the mouse on the map with the help of guide map to create a point. The following window is displayed.

Fig. 3.13(b) Batch Point Generation Fill in the fields Name and Description. Both are string fields. The BatchDone option indicates whether the Batch Watershed Delineation functions will perform a delineation for that point (0: delineate, 1: do not delineate). The SnapOn option indicates whether the Batch Watershed Delineation function will try to snap the point to the closest stream. The Type field indicates whether the point is an outlet or an inlet, and default is outlet.To perform batch watershed delineation: Select Watershed Processing > Batch Watershed Delineation. Confirm that Fdr is the input to Flow Direction Grid, Str to Stream Grid, Catchment to Catchment, watershed to Adjoint Catchment, and BatchPoint1 to Batch Point. Click OK. Fig. 3.14 Batch Watershed Delineation5. DEM of Joshimath was extracted by mask from the DEM of Rudraprayag.6. 13 elevation zones were generated each with elevation difference of 500 meters.7. SCA obtained from MODIS as satellite images from 151st day of 2003 (i.e. 31st May, 2003) to 152nd day of 2004 (i.e. 31st May, 2004) was added. All the MODIS images and ASTER- DEM were geo-referenced.8. Attribute tables generated from the steps 6 and 7 were analysed. With the help of these tables SCA is converted into digital form in the spreadsheets and then certain interpolation is carried out to obtain successively daily snow cover data.9. Hydrological and meteorological data (daily) was analysed and then the calculation for model parameters (such as rainfall and temperature), for SRM was performed in the MS Excel sheets.10. Building a WinSRM basin database: Click File > New option on the main window menu bar.11.(a) Basin Definition: The first step in the definition process was entering several values that were unique and would typically not change. This was accomplished by typing the values in text boxes in the Basin Definition portion of the main window.

Fig. 3.15 Basin Description Table (Basin Definition)11.(b) Zone Definition: At this point, the user provides the critical values required to describe the zones that will subdivide the basin. Each line in the grid represents a single elevation zone in the new basin. This model required values for zone elevation and zone area only. The remaining columns in the grid may be provided for descriptive purposes but were not used by models processing algorithms.

Fig. 3.16 Zone Description Table (Zone Definition)11.(c) Basin Simulation: This table is the index of all simulations that exist in the basin database. Each record in the table represents a unique basin simulation, and stores the control information (simulation title, run#, starting date, ending date, temperature values, etc.). In this grid, simulations were added by clicking on Add Simulation. Then the name of simulation was added in the window appeared asking for a unique name to associate with the data. Then other required enteries were made in the next window appeared. After making the values accepted by the system, the details automatically appeared on the main window in the Basin Simulation Grid. These simulations can either be deleted or edited as per the requirement.

Fig. 3.17 Basin Simulation Grid 11.(d) Database creation process: The data storage structures (tables) required by the model was created and initialized. Before any meaningful modeling can be performed, real data and parameters must be provided. The initial step in this process was to import the digital data into a spreadsheet program (e.g. MS Excel). Once the data was in such a form, it became a simple task to copy columns of values to corresponding positions in the WinSRM. Data analysed in steps 8 and 9 was used here. For this, select Data > Variables > Period of Record and data from the spreadsheet was entered zone-wise using Copy/ Paste commands.12. Then the model was allowed to run. Click Run > Simulation > Year round Simulation. 13. Finally, the report was generated. Click Report on the Menu bar. A window of Output Definition was displayed. Out of the given options, tick the Measured vs Computed Snowmelt Runoff and view this report.14. After analysing the report, number of attempts were made to adjust the values of various parameters to reduce the difference between measured runoff volume and computed runoff volume, if required.Workflow is illustrated in Figure 3.18.

Result of above last two steps generated over the DEM of RudraprayagApply Watershed Preprocessing (Batch Watershed Delineation)Create Batch Input filePerform Watershed DelineationApply Terrain Preprocessing from ArcHydro toolInput DEM of Rudraprayag in ArcHydro toolGeneration of 13 elevation zones with elevation difference of 500 metersInput Snow Covered Area for the year 2003- 2004DEM of Joshimath extracted from DEM of RudraprayagAAccess of DEM of Rudraprayag as an ASTER data from http://earthexplorer.usgs.gov/Start

AAttribute table generatedAnalysis of Attribute tableAnalyses of hydrological and meteorological dataFeed the values to create WinSRM databaseRun the modelOKSimulation AnalysisAdjust the values of parametersStopYesNo

Figure 3.18 Workflow

Chapter 4Results & DiscussionsBelow is the list of results observed while following the above stated steps in Chapter 3:1. Fig. 4.1 Output of Flow DirectionThe Flow Direction function analysed a grid and computed the Flow Direction Grid associated to the input Hydro DEM. The values calculated by process indicated the steepest decent from a cell. Figure 4.1 shows a Flow Direction Grid.

2. Fig. 4.2 Output of Flow AccumulationThe Flow Accumulation tool generated the output flow accumulation grid and added it to the map. In other words, this tool resulted in a formation of raster of accumulated flow to each cell, as determined by accumulating the weight for all cells that flow into each downslope cell. It is basically an indirect way of measuring drainage areas (in units of grid cells). Figure 4.2 shows the result of the Flow Accumulation function.

3. Upon successful completion of the Stream Definition process, the stream grid Str is added to the map. This Str grid contains a value of "1" for all the cells in the input flow accumulation grid (Fac) that have a value greater than the given threshold. All other cells in the Stream Grid contain no data. The cells in Str grid with a value of 1 are symbolized with black colour to get a stream network as shown in the Figure 4.3.

Fig. 4.3 Output of Stream Definition

4. Fig.4.4 Output of Stream SegmentationThe Stream Segmentation tool generated the output steam grid and added it to the map as shown in Figure 4.4. This function created a grid of stream segments that have a unique identification. A segment may either be a head segment, or it may be defined as a segment between two segment junctions. All the cells in a particular segment had the same grid code that is specific to that segment.

5. Upon successful implication of the Catchment Grid Delineation process, the catchment grid (Cat) is added to the map. This function created a grid in which each cell carries a value (grid code) indicating to which catchment the cell belongs. The value corresponds to the value carried by the stream segment or sink link that drains that area, defined in the input stream segment link grid (Stream Segmentation) or sink link grid (Sink Segmentation). This tool basically indicates the exact position of a boundary of various catchment grids.

Fig. 4.5 Output of Catchment Grid Delineation

6. Fig. 4.6 Output DEM of Catchment Polygon ProcessingThe Catchment Polygon Processing function took as input a catchment grid and converts it into a catchment polygon feature class as shown in Figure 4.6. The adjacent cells in the grid that have the same grid code are combined into a single area, whose boundary is vectorized. The single cell polygons and the orphan polygons generated as the artifacts of the vectorization process are dissolved automatically, so that at the end of the process there is just one polygon per catchment.

7. Fig. 4.7 Output of Drainage Line ProcessingThe Drainage Line function converted the input Stream Link grid usually created with the Stream Segmentation function into a Drainage Line feature class. Each line in the feature class carries the identifier of the catchment in which it resides. Figure 4.7 shows violet coloured lines indicating the course of major streams in a drainage system.

8. Fig. 4.8 Output of Adjoint Catchment ProcessingThe Adjoint Catchment Processing function generated the aggregated upstream catchments from the catchment feature class. For each catchment that is not a head catchment, a polygon representing the whole upstream area draining to its inlet point is constructed and stored in a feature class that has an "Adjoint Catchment" tag. Adjoint catchment is basically a unique ID assigned to sub-basins which share a larger watershed (i.e., same end outlet).

9. The Drainage Point Processing tool generated the output Drainage Point feature class and was added it to the map. This feature represented the location of the cell with the maximum flow accumulation value within each catchment. The drainage point is a single point where surface water from rain and melting snow or ice from an enclosed area converges. This point is usually the exit of the basin, where the stream joins another stream or any other waterbody, such as a river, lake, sea or ocean.

Fig. 4.9 Output of Drainage Point Processing

10. The Longest Flow Path for Catchments function generated the longest flow path associated to the catchments. This is required to speed up the generation of Longest Flow Paths.

Fig. 4.10 Output of Longest Flow Path for Catchments11. Fig. 4.11 Output of Longest Flow Path for Adjoint CatchmentsThe Longest Flow Path for Adjoint Catchments function generated the longest flow path associated to the adjoint catchments. Like, the Longest Flow Path for Catchments, this is also required to speed up the generation of Longest Flow Paths.

12. The Batch Watershed Delineation process was performed to plot the watershed point as well as watershed. Figure 4.12 and 4.13 shows the result of this process. It indicated the exact position of a border of the watershed.

Fig. 4.12(a) Output of Batch Watershed Delineation (watershed point)

Fig. 4.12(b) Output of Batch Watershed Delineation (watershed)

13. Figure 4.13 shows the resulting DEM of Joshimath after extracting it from the DEM of Rudraprayag,

Fig. 4.13 Masked DEM of Joshimath from the DEM of Rudraprayag

14. The Joshimath watershed was devided into 13 elevation zones, each with the elevaton difference of 500 m as shown in Figure 4.14.

Fig. 4.14 DEM of Joshimath showing its 13 elevation zones

15. Then finally, after creating the WinSRM database and running it, a report was generated which was analysed for reducing the difference between measured runoff volume and computed runoff volume a shown in Figure 4.15.

Fig. 4.15 Statistics, Year Round Simulation (Final Result)ConclusionDue to global climate change and global warming, snowmelt runoff research is considered more essential than ever before for predicting water resources availability, programming water usage and management, and designing water allocation on a sustainable and long-term basis. Improving the accuracy of snowmelt runoff forecasts diminishes the loss from floods caused by rapid snow and glacier melt. In arid lands where mountain-fed rivers are the only available water resources for the needs of the public, agricultural irrigation, hydropower, and other uses, this situation seems more urgent and necessary. In the Himalayan region, mountain snowmelt water is regarded as the main water source of piedmont regions where human existence and production activities are mainly distributed in the oases scattered throughout the river basins. Therefore, snowmelt water is a vital resource to the safety of people living there. Hence, accurate simulation and forecast of snowmelt runoff has become a real necessity for this region.Because of the above stated reasons, this study was done to evaluate the snowmelt runoff for the hydrological year of 2003-04 using SRM in the catchment of Joshimath. ArcGIS 9.3 have been used for the creation of database required for the simulation of streamflow. The watershed was divided into 13 elevation zones each with elevation difference of 500 meters. Selection of input variables and parameterization of SRM are described in detail. Previous studies suggest that the temperature and precipitation are the major factors dominating the process of snowmelt. While performing the extrapolation of temperature and precipitation, close attention should be paid. A certain interpolation needs to be carried out to obtain successive daily snow cover data between acquired satellite images based on the factors such as temperature, discharge data, and the seasonal trend of a basin climate.The simulation revealed the volume difference of 6.6338% between measured runoff (5381.651106 m3) and computed runoff (5024.645106 m3). Results showed (refer Figure 4.15) that the simulation was successful, because the coefficient of determination (R2) was 0.5489. Keeping in view the aggrement between measured runoff and computed runoff, SRM proved to be a reliable tool for estimating the snowmelt runoff in the mountainous snow covered catchments.

References1. Vajja Hari Prasad and Partha Sarathi Roy (2005). Estimation of Snowmelt Runoff in Beas Basin, India. Geocarto International, Vol. 20, No. 2, June 2005. http://www.geocarto.com.hk/cgi-bin/pages1/june05/41_vajja.pdf

2. Sanjay K. Jain, A. K. Lohani and R.D.Singh (2012). Snowmelt Runoff Modelling In A Basin Located In Bhutan Himalaya. India Water Week, 2012- Water, Energy and Food Security.https://indiawaterweek.water.tallyfox.com/filefield-private/files/9244/field_document_plus/5480

3. Mehdi Fuladipanah and Mehdi Jorabloo (2012). The Estimation of Snowmelt Runoff Using SRM, Case Study (Gharasoo Basin, Iran). World Applied Sciences Journal 17(4): 433-438, 2012.http://idosi.org/wasj/wasj17(4)12/3.pdf 4. Akhtar Alam, Shakil A. Romshoo and M. Sultan Bhat (2011). Estimation of Snowmelt Runoff Using Snowmelt Runoff Model (SRM) In A Himalayan Watershed. World Journal of Scince and Technology 2011, 1(9): 37-42. http://worldjournalofscience.com/article/download/8799/4886

5. Hong Ma and Guodong Cheng (2003). A test of Snowmelt Runoff Model (SRM) for the Gongnaisi River basin in the western Tianshan Mountains, China. Chinese Science Bulletin 2003, Vol. 48 No. 20 2253-2259.http://link.springer.com/article/10.1007%2FBF03182862

6. J. Martinec, A. Rango and R. Roberts (2008). Snowmelt Runoff Model (SRM) Users Manual.http://www.mastergardeners.nmsu.edu/pubs/research/water/srmspecrep100.pdf

7. Thomas Nagler (2006). Real time runoff forecasts in mountain basins using remote sensing data. Geophysical Research Abstracts, Vol. 8, 09432, 2006. http://meetings.copernicus.org/www.cosis.net/abstracts/EGU06/09432/EGU06-J-09432.pdf 8. Norio Hayakawa and Minjiao Lu (2000). Distribution of Temperature and Precipitation in the Snowmelt Runoff Analysis.http://ascelibrary.org/doi/abs/10.1061/40517%282000%29142

9. Arc Hydro Geoprocessing Tools Tutorial, Version 1.4 - May 2010.

10. About IIRS http://www.iirs.gov.in/about-iirs

11. MODIS datahttp://nsidc.org/data/modis/order_data.html?file=/data/modis/order.html

12. ASTER Data (DEM of Rudraprayag)http://earthexplorer.usgs.gov/

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