Session: Earth System Sciences 1 Energy Balance Approach for Snowmelt Runoff Estimation TRIPTI DIMRI 1 , PRAVEEN K. THAKUR 2 , AND S.P.AGGARWAL 3 1 [Water Resources Division, Indian Institute of Remote Sensing, Kalidas Road, Dehradun, Uttarakhand, India; Email: tripti.dimri@gmail.com] 2 [Water Resources Division, Indian Institute of Remote Sensing, Kalidas Road, Dehradun, Uttarakhand, India; Email: praveen@iirs.gov.in] 3 [Water Resources Division, Indian Institute of Remote Sensing, Kalidas Road, Dehradun, Uttarakhand, India; Email: spa@iirs.gov.in] Abstract — A physically based UEB model is used to simulate the energy exchange processes and estimate the amount of snowmelt generated in Manali sub-watershed. The SCA was estimated from remote sensing technique by calculating NDSI with incorporation of NDVI to account for the snow over vegetation and weekly SCA maps are obtained by linearly interpolating these snow cover maps. LULC map was obtained from IRS P-6 LISS-III using visually enhanced fused ALOS PRISM and IRS P-6 LISS- III image. The UEB model’s initial results showed diurnal variations in melt which were in accordance with the flow trends in the study area. Index Terms — Snowmelt, Energy Balance, Snow Cover, Image Fusion, UEB Model. I. INTRODUCTION Snow water is stored on watersheds in various forms, which range from newly fallen crystalline snow to glacial ice. The release of this water results from increased temperature, but the rate of melting is different for different forms of snow and ice. Fresh snow melts faster than old snow that has been altered to ice. Snowmelt runoff estimates are needed for forecasting seasonal water yields, river regulation and storage works, determination of design floods, planning flood control programs, etc. Generally, computation of snowmelt from a watershed is made using either an energy balance approach or using some index approach. [1] An energy balance method requires information on radiation energy, sensible and latent heat, energy transferred through rainfall over snow and heat conduction from ground to the snowpack. On the other hand, an index method uses one or more variables in an empirical expression to estimate snow-cover energy exchange. Energy balance approach considers the actual energy exchanges taking place in the process of snowmelt. This method involves an accounting for a given period of time of incoming energy, outgoing energy, and the change in energy storage for a snow pack. The net energy is then expressed as the heat equivalent of snowmelt. The presence of cloud cover and vegetation cover significantly affects the energy balance of a snow surface. The seasonal variability in the energy inputs available for melt in general increases towards the poles; the difference between summer and winter is minimized at the equator. The differences in energy receipt on the north and south facing slopes can be critical in influencing the time of snowmelt. [1] A. Model Description The Utah Energy Balance (UEB) snow model is an energy balance snowmelt model developed by David G. Tarboton, and Charlie H. Luce, in the year 1996, for the prediction of snowmelt surface water input rates. The model focuses on a variety of locations due to its physically-based snowpack dynamics and minimal calibration requirements. [2] The snowpack is characterized by three state variables, water equivalence W [m], energy content U [kJ m -2 ], and the age of the snow surface which is only used for albedo calculations. The energy content is used to determine snow pack average temperature or liquid fraction. The model is driven by inputs of air temperature, precipitation, wind speed, humidity and radiation at time steps sufficient to resolve the diurnal cycle. The model uses physically-based calculations of radiative, sensible, latent and advective heat exchanges. An equilibrium parameterization of snow surface temperature accounts for differences between snow surface temperature and average snow pack temperature without having to introduce additional state variables. Melt outflow is a function of the liquid fraction, using Darcy's law. This allows the model to account for continued outflow even when the energy balance is negative. Because of its parsimony (only three state variables) this model is suitable for application in a distributed fashion on a grid over a watershed. The basic mathematical equations governing the model are given by the expressions: Where, Q sn , Q li , Q le , Q p , Q g , Q h , Q e and Q m are net shortwave radiation, incoming longwave radiation,