Hyaluronic acid (HA) fillers have become the most popular material for facial volume augmentation and wrinkle correction. Several filler brands are currently on the market all around the world and their features are extremely variable; for this reason, most users are unaware of their differences. The study of filler rheology has become a wellspring of knowledge, differentiating HA fillers, although these properties are not described thoroughly by the manufacturers. The authors of this review describe the more useful rheological properties that can help clinicians understand filler characteristics and the likely correlation of these features with clinical outcomes.

Abstract. Convection-permitting climate models have been recommended for use in projecting future changes in local-scale, short-duration rainfall extremes that are of the greatest relevance to engineering and infrastructure design, e.g., as commonly summarized in intensity–duration–frequency (IDF) curves. Based on thermodynamic arguments, it is expected that rainfall extremes will become more intense in the future. Recent evidence also suggests that shorter-duration extremes may intensify more than longer durations and that changes may depend on event rarity. Based on these general trends, will IDF curves shift upward and steepen under global warming? Will long-return-period extremes experience greater intensification than more common events? Projected changes in IDF curve characteristics are assessed based on sub-daily and daily outputs from historical and late 21st century pseudo-global-warming convection-permitting climate model simulations over North America. To make more efficient use of the short model integrations, a parsimonious generalized extreme value simple scaling (GEVSS) model is used to estimate historical and future IDF curves (1 to 24 h durations). Simulated historical sub-daily rainfall extremes are first evaluated against in situ observations and compared with two high-resolution observationally constrained gridded products. The climate model performs well, matching or exceeding performance of the gridded datasets. Next, inferences about future changes in GEVSS parameters are made using a Bayesian false discovery rate approach. Large portions of the domain experience significant increases in GEVSS location (>99 % of grid points), scale (>88 %), and scaling exponent (>39 %) parameters, whereas almost no significant decreases are projected to occur (<1 %, <5 %, and <5 % respectively). The result is that IDF curves tend to shift upward (increases in location and scale), and, with the exception of the eastern US, steepen (increases in scaling exponent), which leads to the largest increases in return levels for short-duration extremes. The projected increase in the GEVSS scaling exponent calls into question stationarity assumptions that form the basis for existing IDF curve projections that rely exclusively on simulations at the daily timescale. When changes in return levels are scaled according to local temperature change, median scaling rates, e.g., for the 10-year return level, are consistent with the Clausius–Clapeyron (CC) relation at 1 to 6 h durations, with sub-CC scaling at longer durations and modest super-CC scaling at sub-hourly durations. Further, spatially coherent but small increases in dispersion – the ratio of scale and location parameters – of the GEVSS distribution are found over more than half of the domain, providing some evidence for return period dependence of future changes in extreme rainfall.

Abstract. Extreme precipitation is highly variable in space and time. It is therefore important to characterize precipitation intensity distributions on several temporal and spatial scales. This is a key issue in infrastructure design and risk analysis, for which intensity–duration–frequency (IDF) curves are the standard tools used for describing the relationships among extreme rainfall intensities, their frequencies, and their durations. Simple scaling (SS) models, characterizing the relationships among extreme probability distributions at several durations, represent a powerful means for improving IDF estimates. This study tested SS models for approximately 2700 stations in North America. Annual maximum series (AMS) over various duration intervals from 15 min to 7 days were considered. The range of validity, magnitude, and spatial variability of the estimated scaling exponents were investigated. Results provide additional guidance for the influence of both local geographical characteristics, such as topography, and regional climatic features on precipitation scaling. Generalized extreme-value (GEV) distributions based on SS models were also examined. Results demonstrate an improvement of GEV parameter estimates, especially for the shape parameter, when data from different durations were pooled under the SS hypothesis.

The characterization of extreme precipitation at fine spatiotemporal scale represents a paramount challenge in hydroclimate sciences due to large uncertainties affecting the precipitation estimation from existing datasets. Comparing the spatiotemporal structure of precipitation extremes estimated from different datasets thus represents an essential step for climate model evaluation, as it provides insight into a model’s ability to simulate atmospheric processes occurring at different scales. This study compares the probability distributions and the annual and diurnal cycles of occurrence of daily and subdaily precipitation annual maxima (AM) estimated over northeastern North America from five observed and simulated datasets: meteorological station series, the bias-corrected (CRT) satellite CMORPH, version 1.0, and the Multi-Source Weighted-Ensemble Precipitation (MSWEP), version 2, gridded datasets, various Canadian RCM, version 5 (CRCM5), simulations, and a 13-yr convection-permitting WRF, version 3.4.1, simulation. ERA-Interim-driven CRCM5 and WRF simulations well reproduced subdaily extreme quantiles and the AM annual and diurnal cycles observed at stations, while CMORPH and MSWEP displayed good performance only for daily and longer extreme statistics. The spatiotemporal statistical structure of precipitation extremes is then assessed considering the variation of AM quantiles across various spatial scales and durations. The results suggest that a two-parameter analytical relationship well describes the AM spatiotemporal structure at the regional scale, allowing us to approximate some crucial properties of point precipitation extremes from gridded datasets. Averaging the estimates from various members of the initial-condition CRCM5 Large Ensemble (CRCM5-LE) also made it possible to reduce the sampling errors and robustly estimate the AM spatiotemporal structure at the local scale of each model grid box.

Global warming is expected to produce modifications in the intensity, as well as in the seasonality and spatiotemporal structure of extreme precipitation. In the present study, the temporal evolution of simulated daily and subdaily precipitation extremes was analyzed to assess how they respond to climate warming over different time horizons. Pooling series from the recent 50‐member Canadian Regional Climate Model v5 Large Ensemble, the probability distributions, date and time of occurrences, and spatiotemporal structure of simulated Annual Maxima (AM) precipitation were analyzed at various spatial scales and for durations between 1 hr and 3 days. In agreement with previous studies, the results underline the large increases in AM precipitation quantiles, especially for the shortest durations and for the more extreme events (i.e., longest return periods), and modifications in their spatiotemporal scaling properties and annual and diurnal cycles. For instance, subdaily AM extremes are expected to occur later in the evening, while, no matter the duration, the extremes are expected to occur over a wider period of the year in future climate. Finally, the analysis of projected AM probability distributions showed that heavy‐tail Generalized Extreme Value (GEV) distributions will most likely be observed in the future climate, with some model grid boxes experiencing a significant increase of GEV shape parameters. These results may have major consequences in terms of the occurrence and possible impacts of the most extreme precipitation events.