Jesper Rydén

Estimation of return levels is central to risk assessment in the environmental sciences. Distributional assumptions determine tail behaviour and influence the magnitude of extreme quantiles. In this paper, we study the flexible four-parameter generalised F (GenF) distribution and derive first- and second-order asymptotic expansions for high return levels using its Beta representation. The expansions clarify the roles of the GenF shape parameters and the approach towards the asymptotic regime. Numerical illustrations show the relation between exact GenF quantiles and their asymptotic approximations, and demonstrate how finite-level accuracy depends on the parameter configuration.

Estimation of return levels is crucial for risk assessment in the environmental sciences. Distributional assumptions influence tail behaviour and may affect both point estimates of return levels and their associated confidence intervals. In this paper, we investigate the flexible four-parameter Generalised F (GenF) distribution, which includes several conventional models as special cases. With emphasis on return levels, we compare GenF with the generalised extreme value (GEV) distribution through simulation studies and analyses of river discharge and significant wave height data. Upper-tail asymptotics of the GenF are examined in relation to GEV. Potential numerical challenges in fitting the GenF are discussed. Overall, both models yield similar return-level estimates up to 200 years, while differences mainly concern tail diagnostics and uncertainty quantification; GenF provides a flexible complement to the classical GEV framework.

Statistical models for precipitation is of importance in several applications in climatology and agriculture. In this paper, we study a statistical distribution, the Tweedie distribution, to analyse monthly precipitation. Results are found in the literature for sites found in Australia. Here we investigate the amount of precipitation at several sites in Sweden, where various climate zones are found according to the Köppen–Geiger climate classification. The Tweedie distribution was fitted by maximum-likelihood techniques and the behaviour of the so-called index parameter was assessed on a monthly basis for the various sites. Moreover, a clustering procedure was applied to possibly cluster the sites according to the index parameter. We found similar features as for the Australian studies concerning magnitude of the estimated index parameter and influence of coastlines. The results indicate that the Tweedie distribution is a relevant model also for sites in Northern Europe.

At nuclear power plants, risk analysis concerning environmental extremes is crucial. Based on historical data, estimation of return levels is usually performed. For long return periods, a problem is that the related uncertainties of the return levels often get large. Moreover, models need to take into account possible effects of climate change. In this paper, extreme sea levels close to Swedish nuclear power plants are considered. Non-stationary statistical models and the related results of conditional prediction during a typical time horizon of an infrastructure are studied. The influences of parameters in extreme-value distributions and the lengths of observation records are discussed. The effect of land uplift in parts of the Baltic Sea is seen.

Estimation of so-called return levels for environmental extremes is of importance for risk assessment. A particular challenge is to find estimates corresponding to long return periods, as uncertainties in the form of confidence intervals became too wide for practical use when applying conventional methodology where large portions of data are not used. A recently proposed technique, the Average Conditional Exceedance Rate (ACER), makes effective use of all available data. For risk analysis related to nuclear infrastructure, usually located along a coastline, extreme sea levels are of concern. We demonstrate, for measurements of the sea level along the Swedish coast at locations close to nuclear power plants, that the methodology results in considerably shorter confidence intervals compared to conventional approaches.

The existence of an upper limit for extremes of quantities in the earth sciences, e.g. for river discharge or wind speed, is sometimes suggested. Estimated parameters in extreme-value distributions can assist in interpreting the behaviour of the system. Using simulation, this study investigated how sample size influences the results of statistical tests and related interpretations. Commonly used estimation techniques (maximum likelihood and probability-weighted moments) were employed in a case study; the results were applied in judging time series of annual maximum river flow from two stations on the same river, but with different lengths of observation records. The results revealed that sample size is crucial for determining the existence of an upper bound.

With ongoing climate change, analysis of trends in maximum annual daily river flow is of interest. Flow magnitude and timing during the year were investigated in this study. Observations from 11 unregulated rivers in northern Sweden were analysed, using extreme‐value distributions with time‐dependent parameters. The Mann–Kendall test was used to investigate possible trends. The extreme‐value statistics revealed no significant trends for the stations considered, but the Mann–Kendall test showed a significant upward trend for some stations. For timing of maximum flow (day of the year), the Mann–Kendall test revealed significant downward trends for two stations (with the longest records). This implies that the day of the maximum flow is occurring earlier in the year in northern Sweden.

Silk fibers derived from the cocoon of silk moths and the wide range of silks produced by spiders exhibit an array of features, such as extraordinary tensile strength, elasticity, and adhesive properties. The functional features and mechanical properties can be derived from the structural composition and organization of the silk fibers. Artificial recombinant protein fibers based on engineered spider silk proteins have been successfully made previously and represent a promising way towards the large-scale production of fibers with predesigned features. However, for the production and use of protein fibers, there is a need for reliable objective quality control procedures that could be automated and that do not destroy the fibers in the process. Furthermore, there is still a lack of understanding the specifics of how the structural composition and organization relate to the ultimate function of silk-like fibers. In this study, we develop a new method for the categorization of protein fibers that enabled a highly accurate prediction of fiber tensile strength. Based on the use of a common light microscope equipped with polarizers together with image analysis for the precise determination of fiber morphology and optical properties, this represents an easy-to-use, objective non-destructive quality control process for protein fiber manufacturing and provides further insights into the link between the supramolecular organization and mechanical functionality of protein fibers.