Max-stable process (MSP) models can be fit to data collected over a spatial domain to estimate areal-based exceedances while accounting for spatial dependence in extremes. They have theoretical grounding within the framework of extreme value theory (EVT). In this work, we fit MSP models to three-day duration cool season precipitation maxima in the Willamette River Basin (WRB) of Oregon and to 48 h mid-latitude cyclone precipitation annual maxima in the Upper Trinity River Basin (TRB) of Texas. In total, 14 MSP models were fit (seven based on the WRB data and seven based on the TRB data). These MSP model fits were developed and applied to explore how user choices of study area sampling density, gage extent, and model fitting method impact areal precipitation-frequency calculations. The impacts of gage density were also evaluated. The development of each MSP involved the application of a recently introduced trend surface modeling methodology. Significant reductions in computing times were achieved, with little loss in accuracy, applying random sample subsets rather than the entire grid when calculating areal exceedances for the Cougar dam study area in the WRB. Explorations of gage extent revealed poor consistency among the TRB MSPs with modeling the generalized extreme value (GEV) marginal distribution scale parameter. The gauge density study revealed the robustness of the trend surface modeling methodology. Regardless of the fitting method, the final GEV shape parameter estimates for all fourteen MSPs were greater than their prescribed initial values which were obtained from spatial GEV fits that assumed independence among the extremes. When two MSP models only differed by their selected fitting method, notable differences were observed with their dependence and trend surface parameter estimates and resulting areal exceedances calculations.

Extreme precipitation events are often localized, difficult to predict, and available records are often sparse. Improving frequency analysis and describing the associated uncertainty are essential for regional hazard preparedness and infrastructure design. Our primary goal is to evaluate incorporating Bayesian model averaging (BMA) within a spatial Bayesian hierarchical model framework (BHM). We compare results from two distinct regions in Oregon with different dominating rainfall generation mechanisms, and a region of overlap. We consider several Bayesian hierarchical models from relatively simple (location covariates only) to rather complex (location, elevation, and monthly mean climatic variables). We assess model predictive performance and selection through the application of leave-one-out cross-validation; however, other model assessment methods were also considered. We additionally conduct a comprehensive assessment of the posterior inclusion probability of covariates provided by the BMA portion of the model and the contribution of the spatial random effects term, which together characterize the pointwise spatial variation of each model’s generalized extreme value (GEV) distribution parameters within a BHM framework. Results indicate that while using BMA may improve analysis of extremes, model selection remains an important component of tuning model performance. The most complex model containing geographic and information was among the top performing models in western Oregon (with relatively wetter climate), while it performed among the worst in the eastern Oregon (with relatively drier climate). Based on our results from the region of overlap, site-specific predictive performance improves when the site and the model have a similar annual maxima climatology—winter storm dominated versus summer convective storm dominated. The results also indicate that regions with greater temperature variability may benefit from the inclusion of temperature information as a covariate. Overall, our results show that the BHM framework with BMA improves spatial analysis of extremes, especially when relevant (physical and/or climatic) covariates are used.

In this study, we consider the complete archive of the 32 Coupled Model Intercomparison Project Phase 5 (CMIP5) daily Localized Constructed Analogs (LOCA) downscaled historic datasets and their observational data that were used for downscaling and bias corrections to develop an ensemble that optimises calculation of the growing season average temperature (GST) viticulture climate classification index throughout the Willamette Valley (WV) American Viticultural Area (AVA). Ensemble directed spatiotemporal calculations, using LOCA CMIP5 historic and RCP4.5 future datasets of minimum and maximum daily temperature, were performed throughout the WV AVA for the GST index and Pinot noir specific applications of the grapevine sugar ripeness (GSR) model at a 220 g/L target sugar concentration. The GST index and GSR model evaluations were calculated on a mean decadal basis from the 1950s to the 2090s for the WV AVA. The GST index and GSR model calculations both revealed a warming trend with time for the WV AVA. A 3.1 °C increase in the GST index is predicted from the 1950s to the 2090s. The GSR model indicated a rate advance of 2.9 days a decade from the 1960s to the 2080s. However, the application of the GST index and the GSR model portray markedly different characterisations about the suitability of Pinot noir throughout the WV AVA with time. A strong invertible relationship between the GST index and GSR model calculations is observed and exploited to update the Pinot noir specific lower and upper bounds (14.8 °C, 17.6 °C) for the GST index throughout the WV AVA. Pinot noir specific applications of the GSR model or the GST index with updated bounds indicate that the percent of the WV AVA area suitable for Pinot noir production is currently at or near its peak value in the upper 80s to the lower 90s.

Future climate projections provide an opportunity to evaluate cultivar climate classification and preferred styles of wine production for a wine grape growing region. However, ensemble selection must account for downscaled archive model skills and interdependence rather than be arbitrary and subjective. Relatedly, methods for generalizing climate model choice remain uncertain, particularly for identifying optimal ensemble subsets. In this study we consider the complete archive of the thirty-two Coupled Model Intercomparison Project Phase 5 (CMIP5) daily Localized Constructed Analogs (LOCA) downscaled historic datasets and their observational data that were used for downscaling and bias corrections. We apply four model averaging methods to determine optimal ensembles for the computation of six common climate classification indices for the Willamette Valley (WV) American Viticultural Area (AVA). Among the four methods evaluated, elastic-net regularization consistently performed best with identifying optimal ensemble subsets. Variation exists among the optimal ensembles computed for each of the six bioclimatic indices. However, a subset of approximately seven to ten climate models were consistently excluded across all six indices’ ensembles. While specific to the archive and wine region, optimal ensemble sizes were noticeably larger than ensemble sizes commonly employed in published studies. Results are reported such that they can be used by researchers to independently perform analyses involving any one of the six bioclimatic indices throughout the WV AVA while using historic and future LOCA CMIP5 climate projections. The data and methods employed herein are applicable for other wine regions.

An unbiased MACA CMIP5 ensemble that optimized calculation of the growing season average temperature (GST) viticulture climate classification index throughout Northern California’s Fort Ross-Seaview (FRS), Los Carneros (LC), Petaluma Gap (PG), and Russian River Valley (RRV) American Viticultural Areas (AVAs) was applied to compute the GST index and Pinot noir specific applications of the grapevine sugar ripeness (GSR) model on a mean decadal basis from the 1950s to the 2090s using RCP4.5 and RCP8.5 projections of minimum and maximum daily temperature. From the 1950s to the 2090s, a 2.1/3.6, 2.4/4.2, 2.3/4.0, 2.3/4.0, and 2.3/4.0 °C increase in the GST index and a rate advance of 1.3/1.9, 1.1/1.8, 1.3/2.0, 1.2/1.9, and 1.2/1.9 days a decade was computed for FRS, LC, PG, RRV, and across all four AVAs while using the RCP4.5/RCP8.5 climate projections, respectively. The GST index and GSR model calculations were highly correlated across both climate projections and their fitted models were used to update the Pinot noir specific upper bound for the GST index throughout each AVA using a published optimal harvest window for the northern hemisphere. At a 220 g/L target sugar concentration, the updated upper bound was 17.6, 17.5, 17.6, 17.5, and 17.6 °C for FRS, LC, PG, RRV, and across all four AVAs. For a 240 g/L sugar concentration, it was 17.9, 17.8, 17.9, 17.8, and 17.9 °C. The results from this study together with comparable results recently reported for the Willamette Valley AVA of Oregon using a different downscaled CMIP5 model archive suggest spatial invariance, albeit sugar concentration dependent, for the updated Pinot noir specific upper bound for the GST climate index.