A SIMPLE GIS-BASED SNOW ACCUMULATION AND MELT MODEL

A simple distributed snow accumulation and melt (SAM) model was developed for use in GIS-based hydrologic models. Snow drifting is simulated using observed wind speed and distributed snow drift factors, which are generated as a pre-processing step using the SnowTran-3d model. Snowmelt is simulated through a mass and energy balance applied to a single layered snow pack. Snow surface temperatures are simulated without the requirement of an iterative solution using a simple damping depth approach. The SAM model was applied to a 2 ha watershed in Troy, ID having three years of detailed snow water equivalent (SWE) measurements. Snow surface temperatures predicted by the SAM model using a fixed snow damping depth of 0.05 m showed good agreement with SHAW model predictions. The root mean square error (ERMS) between simulated and observed SWE across the site (10–46 mm) was consistent with and in some cases smaller than the ERMS at points where the maximum predicted SWE was fit to field observations (21-26 mm). The SAM model is a relatively simple model that can be coupled to GIS, relies on readily available data, and requires limited parameter estimation. INTRODUCTION The transfer and release of water from a spatially distributed snow pack (snow accumulation and melt) is a key hydrologic process affecting the generation of surface runoff and pollutant transport. For practical watershed management purposes, the ‘ideal’ distributed hydrologic model strikes a balance between data availability and model complexity (Grayson et al., 1992), is coupled to geographic information systems (GIS), relies on readily available data, and requires little calibration so that transfer to watershed managers becomes feasible (Frankenberger et al., 1999). In this paper, we present a GIS-based snow accumulation and melt model (SAM) ideally suited for integration with distributed hydrologic models for watershed management applications. Snow accumulation can be described well with complex blowing snow models (e.g. SnowTran-3D, Liston and Sturm, 1998), however the complexity of these models limit their direct application as practical management tools in simple distributed snowmelt models. For watershed management purposes a simple and more practical method for distributing snow over a landscape is by using static snow drift factors (the ratio between actual deposition of snowfall at a point to the measured snowfall in a local precipitation gage). Snow drift factors can either be estimated directly based on observed data, or generated through correlation between measured points and topographic indices, or generated directly using complex snow drift models (Prasad, et al., 2001). Snow melt models can generally be lumped into four categories: 1. “Complex” mass and energy balance models (e.g., SHAW by Flerchinger and Saxton, 1989); 2. Temperature index models (see Maidment, 1993, page 7-24); 3. Hybrid temperature index models (Kustas, et al., 1994); and 4. “Simple” mass and energy balance models (Tarboton and Luce, 1996; Marks, et al., 1998; Wigmosta, et al., 1994). The “simple” mass and energy balance models fit the ‘ideal’ distributed hydrologic model as described above. The advantage of these models is that they physically represent the mass and energy transfer in a snow pack without requiring detailed, multi-layered, and in some cases, iterative numeric solutions. They provide a detailed representation of the spatial variability of the release water from a snow pack required in distributed hydrologic runoff and pollutant transport modeling. Typically, the level of complexity inherent in these models is determined by how they simulate the surface and snow pack temperatures. The SAM model, presented in this paper is a “simple” model which is unique in that it does not require any iterative solutions, accounts for snow drifting using snow drift factors and predicts snow surface temperature using a simple damping depth approximation. The specific objectives of the paper are to describe the unique features of _______________________________________ Paper presented Western Snow Conference 2005 Research Support Scientist, Department of Agricultural and Biological Engineering, Moscow, ID 83844-2060 Associate Professor, Department of Agricultural and Biological Engineering, Moscow, ID 83844-2060