A Bayesian hierarchical model for forecasting intermountain snow dynamics

作者：Odei James B^*; Symanzik Juergen; Hooten Mevin B

来源：Environmetrics, 2014, 25(5): 324-340.

DOI：10.1002/ENV.2275

• empirical orthogonal functions
• time series
• water-year
• snow water equivalent

Because of a continual increase in the demand for water as well as an ongoing regional drought, there is an imminent need to monitor and forecast water resources in the Western United States. In particular, water resources in the Intermountain West rely heavily on snow water storage. Thus, the need to improve seasonal forecasts of snowpack and considering new techniques would allow water resources to be more effectively managed throughout the entire water-year. Many available models used in forecasting snow water equivalent (SWE) measurements require delicate calibrations. In contrast to the physical SWE models most commonly used for forecasting, we offer a statistical model. We present a data-based statistical model that characterizes seasonal SWE in terms of a nested time series, with the large scale focusing on the inter-annual periodicity of dominant signals and the small scale accommodating seasonal noise and autocorrelation. This model provides a framework for independently estimating mainly the temporal dynamics of SWE for the various snow telemetry sites. We use snow telemetry data from 10 stations in Utah over 34 water-years to implement and validate this model.

• 出版日期2014-8