Estimating Climate-Change Impacts on Colorado Plateau Snowpack Using Downscaling Methods

This study develops observed climate-based downscaling transfer functions that are used with general circulation model (GCM) output to assess potential global-change impacts on Upper Colorado Plateau, USA, water resources. Daily automated snow water equivalent stations are used with 700 mb atmospheric circulation to determine empirical transfer functions. Downscaling methodologies using multiple regression and neural networks are evaluated, with the neural network results explaining approximately 70%of the daily snowfall variance. The neural network-based transfer functions are used with the GENESIS GCM to simulate snowfall characteristics in both a 1xCO₂ and a 2xCO₂ climate. While the total precipitation simulated by the 2xCO₂ analysis remains nearly the same as today, less will fall as snow; specifically, snow water equivalence shows significant reduction (except in the Green River Basin) as the snow season is reduced by 58 days on average.