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An assumption of stationarity in climate-related processes is often made in the risk assessment of civil infrastructure systems. Such an assumption is difficult to justify in a changing climate. In this study, to optimally adapt to a changing climate, given time series data, we propose a computationally efficient algorithm called Greedy Copula Segmentation, GCS, that could potentially be used in a climate change adaptation (CCA) strategy. The GCS algorithm partitions a multivariate time series into disjoint segments such that each of the segments is described by a stationary copula process, but independence is assumed across segments. An optimal strategy for climate change adaptation, which we will refer to as GCS-CCA, considers the last or most recent segment as containing the most informative data for near future climate pattern prediction. By only using such informative data to build a probabilistic model for the near future, our method effectively accounts for climate change. We provide an algorithmic formulation for greedy segmentation and validate the performance of our GCS-based strategy by applying it to an illustrative benchmark problem and a realistic drought example. 

Accurate and efficient power demand forecasting in urban settings is essential for making decisions related to planning, managing and operations in electricity supply. This task, however, is complicated due to many sources of uncertainty such as due to the variation in weather conditions and household or other needs that influence the inherent stochastic and nonlinear characteristics of electricity demand. Due to the modeling flexibility and computational efficiency afforded by it, a Gaussian process model is employed in this study for energy demand prediction as a function of temperature. A Gaussian process model is a Bayesian non-parametric regression method that models data using a joint Gaussian distribution with mean and covariance functions. The selected mean function is modeled as a polynomial function of temperature, whereas the covariance function is appropriately selected to reflect the actual data patterns. We employ real data sets of daily temperature and electricity demand from Austin, Texas, USA to assess the effectiveness of the proposed method for load forecasting. The accuracy of the model prediction is evaluated using metrics such as mean absolute error (MAE), root mean squared error (RMSE), mean absolute percentage error (MAPE) and 95% confidence interval (95% CI). A numerical study undertaken demonstrates that the proposed method has promise for energy demand prediction. 

Climate studies based on global climate models (GCMs) project a steady increase in annual average temperature and severe heat extremes in central North America during the mid-century and beyond. However, the agreement of observed trends with climate model trends varies substantially across the region. The present study focuses on two different locations: Des Moines, IA and Austin, TX. In Des Moines, annual extreme temperatures have not increased over the past three decades unlike the trend of regionally-downscaled GCM data for the Midwest, likely due to a “warming hole” over the area linked to agricultural factors. This warming hole effect is not evident for Austin over the same time period, where extreme temperatures have been higher than projected by regionally-downscaled climate (RDC) forecasts. In consideration of the deviation of such RDC extreme temperature forecasts from observations, this study statistically analyzes RDC data in conjunction with observational data to define for these two cities a 95% prediction interval of heat extreme values by 2040. The statistical model is constructed using a linear combination of RDC ensemble-member annual extreme temperature forecasts with regression coefficients for individual forecasts estimated by optimizing model results against observations over a 52-year training period.