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Abstract There is high confidence that extreme precipitation will increase in most areas if the globe continues to warm. In the US, NOAA Atlas 14 (NA14) is the most authoritative source for heavy rainfall frequency values used in infrastructure planning and design. However, NA14 assumes a stationary climate and uses only historical observations to estimate values. Thus, use of such values for design may lead to underperformance of long-lived infrastructure, thereby placing people and property at increased risk from flooding. Analyses of global climate model (GCM) simulations suggest that projected extreme precipitation changes will be positive nearly everywhere in the US and will be larger for shorter durations, lower annual exceedance probabilities (AEPs), and higher emissions. Herein, we provide adjustment factors that can be applied to observations-based precipitation frequency values to estimate potential future changes under selected global warming levels. These are derived from two statistically downscaled daily precipitation datasets (STAR and LOCA2) developed using modern methods that focus in part on modeling the high tail of the precipitation distribution with a high degree of fidelity. These datasets, each consisting of 16 ensemble members downscaled from a common set of 16 CMIP6 GCMs, provide estimates for durations of daily and longer. The set of adjustment factors are extended using seven models from the NA-CORDEX suite of dynamically downscaled simulations by analyzing the change in adjustment factors from daily to hourly durations. There is an average increase in the adjustment factors of about 1.3. This factor is applied to the daily adjustment factors from STAR and LOCA2 to produce estimates for the hourly duration. 

Hydrogels showing strong adhesion to different substrates have garnered significant attention for engineering applications. However, the current development of such hydrogel-based adhesive is predominantly limited to synthetic polymers, owing to their exceptional performance and an extensive array of chemical options. To advance the development of sustainable hydrogel-based adhesives, we successfully create a highly robust all-cellulose hydrogel-based adhesive, which is composed of concentrated dialcohol cellulose nanorods (DCNRs) and relies on enhanced hydrogen bonding interactions between cellulose and the substrate. We implement a sequential oxidization-reduction process to achieve this high-performance all-cellulose hydrogel, which is realized by converting the two secondary hydroxyl groups within an anhydroglucose unit into two primary hydroxyl groups, while simultaneously linearizing the cellulose chains. Such structural and chemical modifications on cellulose chains increase out-of-plane interactions between the DCNRs hydrogel and substrate, as simulations indicate. Additionally, these modifications enhance the flexibility of the cellulose chains, which would otherwise be rigid. The resulting all-cellulose hydrogels demonstrate injectability and strong adhesion capability to a wide range of substrates, including wood, metal, glass, and plastic. This green and sustainable all-cellulose hydrogel-based adhesive holds great promise for future bio-based adhesive design. 

In this study, nine different statistical models are constructed using different combinations of predictors, including models with and without projected predictors. Multiple machine learning (ML) techniques are employed to optimize the ensemble predictions by selecting the top performing ensemble members and determining the weights for each ensemble member. The ML-Optimized Ensemble (ML-OE) forecasts are evaluated against the Simple-Averaging Ensemble (SAE) forecasts. The results show that for the response variables that are predicted with significant skill by individual ensemble members and SAE, such as Atlantic tropical cyclone counts, the performance of SAE is comparable to the best ML-OE results. However, for response variables that are poorly modeled by individual ensemble members, such as Atlantic and Gulf of Mexico major hurricane counts, ML-OE predictions often show higher skill score than individual model forecasts and the SAE predictions. However, neither SAE nor ML-OE was able to improve the forecasts of the response variables when all models show consistent bias. The results also show that increasing the number of ensemble members does not necessarily lead to better ensemble forecasts. The best ensemble forecasts are from the optimally combined subset of models.