Regional approach for mapping climatological snow water equivalent over the mountainous regions of the western United States

This paper presents that a simple regional regression-based approach with readily available geographic and meteorological parameters as predictors could be a viable method for mapping snow water equivalent (SWE) climatology in the mountainous areas of the western United States. Such climatological information is potentially useful for several hydrologic applications, including estimation of real-time SWE grids and calibration and evaluation of SWE estimates obtained from remote sensing or through various snow models. Regional delineation for the mountainous regions of the western United States was done through cluster analysis in consideration of the characteristics of seasonal snow pack accumulation and ablation processes. Various geographic and meteorological parameters were further investigated through stepwise regression as potential predictors of monthly changes in climatological SWE in each delineated region. In-situ measurements of SWE, obtained from the Natural Resources Conservation Service's Snow Telemetry network for the period from 1980 to 2004, were used to calibrate regional equations. The spatial and temporal resolution of the analysis was based on the resolution of available meteorological data - 4 km and 1 month, respectively. For a monthly time step, the reliability of the SWE estimates did not significantly increase when the number of regions was more than five. The performance of the developed regional equations was evaluated via a jack-knife technique. The regional equations developed using monthly resolution and large regions provided reliable estimates for the majority of regions from October to March, but not in April, especially in the North Pacific and Southwest regions. Sub-regionalization, a finer time step for the analysis and/or inclusion of additional SWE predictor variables may increase the reliability of SWE climatological estimates for warmer months. (C) 2011 Elsevier B.V. All rights reserved.