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The potential weight of accumulated snow on the roof of a structure has long been an important consideration in structure design. However, the historical approach of modeling the weight of snow on structures is incompatible for structures with surfaces and geometry where snow is expected to slide off of the structure, such as standalone solar panels. This paper proposes a “storm-level” adaptation of previous structure-related snow studies that is designed to estimate short-term, rather than season-long, accumulations of the snow water equivalent or snow load. One key development associated with this paper includes a climate-driven random forests model to impute missing snow water equivalent values at stations that measure only snow depth in order to produce continuous snow load records. Additionally, the paper compares six different approaches of extreme value estimation on short-term snow accumulations. The results of this study indicate that, when considering the 50-year mean recurrence interval (MRI) for short-term snow accumulations across different weather station types, the traditional block maxima approach, the mean-adjusted quantile method with a gamma distribution approach, and the peak over threshold Bayesian approach tend to most often provide MRI estimates near the median of all six approaches considered in this study. Further, this paper also shows, via bootstrap simulation, that the peak over threshold extreme value estimation using automatic threshold selection approaches tend to have higher variance compared to the other approaches considered. The results suggest that there is no one-size-fits-all option for extreme value estimation of short-term snow accumulations, but highlights the potential value from integrating multiple extreme value estimation approaches.

Flourishing, a primary outcome of rehabilitation psychology, is understudied among adults with disabilities. Gratitude has emerged as an individual strength that is both malleable and robust in predicting flourishing and adaptation to disability. The purpose of this study was to assess the influence of gratitude on flourishing over time and to analyze the potential mediating role of adaptation to disability on this relationship for a group of adults with disabilities.Data were collected at 3 time points over 21 months (Gratitude predicted later flourishing and adaptation to disability accounted for a significant portion of this relationship, accounting for 27% of the total effect.Results of this single mediator model indicate that adaptation to disability serves as a partial mediator of the relationship between gratitude and flourishing, with both gratitude and adaptation to disability having a significantly positive influence on flourishing. Understanding gratitude's influence on later adaptation and flourishing provides data to inform rehabilitation psychology interventions. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

As part of an ongoing effort to update the ground snow load maps in the US, this paper presents an investigation into snow densities for the purpose of predicting ground snow loads for str...

We investigate historical and projected precipitation in Tanzania using observational and climate model data. Precipitation in Tanzania is highly variable in both space and time due to topographical variations, coastal influences, and the presence of lakes. Annual and seasonal precipitation trend analyses from 1961 to 2016 show maximum rainfall decline in Tanzania during the long rainy season in the fall (March&ndash, May), and an increasing precipitation trend in northwestern Tanzania during the short rainy season in the spring (September&ndash, November). Empirical orthogonal function (EOF) analysis applied to Tanzania&rsquo, s precipitation patterns shows a stronger correlation with warmer temperatures in the western Indian Ocean than with the eastern-central Pacific Ocean. Years with decreasing precipitation in Tanzania appear to correspond with increasing sea surface temperatures (SST) in the Indian Ocean, suggesting that the Indian Ocean Dipole (IOD) may have a greater effect on rainfall variability in Tanzania than the El Ni&ntilde, o-Southern Oscillation (ENSO) does. Overall, the climate model ensemble projects increasing precipitation trend in Tanzania that is opposite with the historical decrease in precipitation. This observed drying trend also contradicts a slightly increasing precipitation trend from climate models for the same historical time period, reflecting challenges faced by modern climate models in representing Tanzania&rsquo, s precipitation.

Snow loads in the western United States are largely undefined due to complex geography and climates, leaving the individual states to publish detailed studies for their region, usually through the local Structural Engineers Association (SEAs). These associations are typically made up of engineers not formally trained to develop or evaluate spatial statistical methods for their regions and there is little guidance from ASCE 7. Furthermore, little has been written to compare the independently developed design ground snow load prediction methods used by various western states. This paper addresses this topic by comparing the accuracy of a variety of spatial methods for predicting 50-year (i.e., design) ground snow loads in Utah and Idaho. These methods include, among others, the current Utah snow load equations, Idaho’s normalized ground snow loads based on inverse distance weighting, two forms of kriging, and the authors’ adaptation of the Parameter-elevation Relationships on Independent Slopes Model (PRISM). The accuracy of each method is evaluated by measuring the mean absolute error using 10-fold cross validation on data sets obtained from Idaho’s 2015 snow load report, Utah’s 1992 snow load report, and a new Utah ground snow load data set. These results show that regression-based kriging and PRISM methods have the lowest cross-validated errors across all three data sets. These results also show that normalized ground snow loads, which are a common way of accounting for elevation in traditional interpolation methods, do not fully account for the effect of elevation on ground snow loads within the considered data sets. The methodologies and cautions outlined in this paper provide a framework for an objective comparison of snow load estimation methods for a given region as state SEAs look to improve their future design ground snow predictions. Such comparisons will aid states looking to amend or improve their current ground snow load requirements.

Despite their importance in building design, ground snow load requirements remain largely undefined for the western United States. This paper proposes an adaptation of the Parameter-Elevati...