资源环境科技发展态势分析平台

Vulnerability-based frameworks are increasingly used to better understand water system performance under climate change. This work advances the use of stochastic weather generators for climate vulnerability assessments that simulate weather based on patterns of regional atmospheric flow (i.e., weather regimes) conditioned on global-scale climate features. The model is semiparametric by design and includes (1) a nonhomogeneous Markov chain for weather regime simulation; (2) block bootstrapping and a Gaussian copula for multivariate, multisite weather simulation; and (3) modules to impose thermodynamic and dynamical climate change, including Clausius-Clapeyron precipitation scaling, elevation-dependent warming, and shifting dynamics of the El Nino-Southern Oscillation (ENSO). In this way, the model can be used to evaluate climate impacts on water systems based on hypotheses of dynamic and thermodynamic climate change. The model is developed and tested for cold-season climate in the Tuolumne River Basin in California but is broadly applicable across the western United States. Results show that eight weather regimes exert strong influences over local climate in the Tuolumne Basin. Model simulations adequately preserve many of the historical statistics for precipitation and temperature across sites, including the mean, variance, skew, and extreme values. Annual precipitation and temperature are somewhat underdispersed, and precipitation spell statistics are negatively biased by 1-2 days. For simulations of future climate, the model can generate a range of Clausius-Clapeyron scaling relationships and modes of elevation-dependent warming. Model simulations also suggest a muted response of Tuolumne climate to changes in ENSO variability.