639 Nonstationary Daily Stochastic Weather Generator for the Great Basin Region

Wednesday, 13 January 2016
Kimberly Smith, University of Utah, Salt Lake City, UT; and C. Strong and F. Rassoul-Agha

Northern Utah—and the Great Basin as a whole—is facing an uncertain water future due to climate change. Because the region is comprised of mainly complex terrain, dynamical downscaling or stochastic modeling are needed to reliably capture the spatial patterns of climate change at watershed scales. In this study, a nonstationary, daily stochastic weather generator, which produces precipitation occurrence and amount as well as maximum and minimum temperature, is applied to a single site in the Salt Lake Valley and a single site in the Wasatch Mountain Range. A novel treatment of nonstationary temperature variability is introduced that simulates temperature directly, circumventing the conventional approach of simulating standardized anomalies of temperature. Temperature is influenced by whether a day is wet or dry, and it is determined by calculating parameters associated with harmonics in the system. Precipitation occurrence is modeled using a two-state, second-order Markov chain process, and nonstationarity is assessed by adding perturbations due to ENSO-like and PDO-like Pacific modes of variability, which directly influence the Great Basin.
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