(Srm) model application: SRM was developed by Martinec (1975) in small European basins. With the progress of satellite remote sensing of snow cover, SRM has been applied to larger and larger basins. The Snowmelt-Runoff Model (SRM) is designed to simulate and forecast daily stream flow 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. basin of the Ganges River Recently, the runoff was modeled in the basin of the Ganges River, which has an area of 917,444 km 2 and an elevation range from 0 to 8,840 m, m.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.

INPUTSTAGE DERIVATIONSTAGE MODELSTAGE OUTPUTSTAGE Daily Discharge Simulation Watershe d 0.25˚ gridded Daily Rainfall Data_2013 Daily Temperatur e for 2013 Snow cover Mapping for 2013 Slope & Aspect. Derivation of Parameters, Variables & Constants Landsat 8 (OLI/TIRS Bands) Ancillary Data SRTM Digital Elevation Model (DEM) Q n+1 = {[ C Sn.a n (T n + ΔT n ) S n + C Rn.P n ].A.(10000/86400).(1-k n+1 )}+ Q n.k n+1. Basin Elevation Snowmelt Runoff Modeling

Snowmelt runoff model: Q n+1 = {[C Sn.a n (T n + Δ T n ) S n + C Rn.P n ].A.(10000/86400).(1-k n+1 ) }+ Q n k n+1. where: Q = average daily discharge [m 3 s -1 ] C = runoff coefficient expressing the losses as a ratio (runoff/precipitation), with C S referring to snowmelt and C R to rain a = degree-day factor [cm o C -1 d -1 ] indicating the snowmelt depth resulting from 1 degree-day S = ratio of the snow covered area to the total area P = precipitation contributing to runoff [cm]. A preselected threshold temperature, T CRIT, determines whether this contribution is rainfall and immediate. If precipitation is determined by T CRIT 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 [km 2 ] Source: Jaroslav Martinec, Albert Rango & Ralph Roberts., S R M - SNOWMELT RUNOFF MODEL, USER’S MANUAL - UPDATED EDITION FOR WINDOWS, WinSRM Version 1.11., February, 2008.

T= number of degree-days [ o C d] Δ T= the adjustment by temperature lapse rate when extrapolating the temperature from the station to the average hypsometric elevation of the basin or zone [ o C d] (0.67 o C lapse per 100m rise in elevation) (17) k = recession coefficient indicating the decline of discharge in a period without snowmelt or rainfall: k = (m, m + 1 are the sequence of days during a true recession flow period). n = sequence of days during the discharge computation period. Equation 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. conversion from cmkm 2 d -1 to m 3 s -1

 T, S and P are variables to be measured or determined each day, c R, c S, lapse rate to determine T, T CRIT, 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:

Snowmelt runoff model necessities for execution: 1. Basin characteristics 1.1 Basin and zone areas 1.2 Aspect 1.2 Area-elevation curve (Hypsometric Mean) 2. Variables 2.1 Temperature, ˚C 2.2 Precipitation, P 2.3 Snow covered area, S 3. Parameters 3.1 Runoff coefficient, c 3.2 Degree-day factor, a 3.3 Temperature lapse rate, 3.4 Critical temperature, T CRIT 3.5 Rainfall contributing area, RCA 3.6 Recession coefficient, k 3.7 Time Lag, L 4. SRM For Windows (Winsrm 1.11)

Basin Characteristics Table 6: Showing area and % area of 6 elevation zones S.noElevation ZonesArea in Sq.km% Area

Area Elevation curve – hypsometric mean elevation Area of Different Elevation Zones showing hypsometric mean elevation of each Zones Zone Elevation in m Area in Sq.km% Area Mean Area Hypsometric Mean Elev.(m)

zone wise aspect and its corresponding area

Daily temperature data Daily minimum, Maximum values are collected and average is taken as input

Temperature - Extrapolation = temperature lapse rate [  C per 100 m] = altitude of the temperature station [m] = hypsometric mean elevation of a zone [m]

Rainfall Distribution of rainfall varies in the different area of the catchment degree gridded rainfall gives number of observations in the different zones of the catchment. Each elevation zone covers 2 or 3 grids in the catchment. In that case average rainfall of number of grids is considered for each elevation zones in rainfall derivations.

Snow cover Area Multi temporal Satellite data used

APPLICATION OF SRM Step 1: Entering of basin characteristics derived using DEM. Step 2: It is add stimulation stage which is the second stage of adding constant parameters. Step 3: Addition of variables are proceeded through clicking Data -> Variables-> period of record, the table opened, from which the records prepared for 365 days san be added zone wise. Step 4: It involves in running the simulation to get the Discharge for the entire year. It can be done by clicking Run -> Simulation -> Year Round Simulation using the model.

Discharge calculated for the year 2013 using this model and software application gives quantum of annual discharge of million cubic meters in the Dhauliganga catchment. Average discharge of about m³/s inferred from the simulated values.

Basin Discharge Simulation Results The discharge derived from the software for the year 2013 shows peak discharge of 1110 m³/s in the mid of June where the peaks of snow melt as well as rainfall occurs together with total yearly discharge of Million m³. Daily average discharge for the year is m³/s where as 80% discharge occurs in the month of June, July and August at an average rate of 200 to 450 m³/s.

conclusion Snowmelt runoff modeling for the Dhauliganga catchment area is done effectively by integrated Remote Sensing based snow cover assessment, DEM and hydro meteorological data in temperature index model. Further improvements can be made for achieving better results by validating the model with observed discharge data, running the model for longer duration about 5 to 10 years and usage of high resolution optical cloud free data or microwave data for snow melt runoff assessment.