[1] Mountains within alpine regions can have a significant influence on the geographic distribution of precipitation and on local-to regional-scale climatic and meteorological conditions. Consequently, the oxygen and hydrogen isotope compositions of precipitation are affected by alpine topography as well. The Austrian Network of Isotopes in Precipitation, one of the oldest and densest networks in the world, is therefore an excellent source of isotope data of the last 30 years for investigating the complex processes governing the isotopic composition of precipitation in alpine regions. Fractionation during phase change processes leads to significant isotopic variation in precipitation. Here we show that the spatial isotopic variability, especially in alpine regions, is to a large extent due to nonequilibrium fractionation. Additionally, we conclude that meteorological conditions prevailing at the sampling site are mainly responsible for the observed seasonal pattern in deuterium excess. These results are validated by stepwise multiple regression as well as by applying an empirical model for deuterium excess, which is an indicator of the differential behavior of D and18 O values in vapor and precipitation. The present results enhance the development of models based on detailed statistical assessment of local climatic conditions to improve the quantitative interpretation of isotope data for paleoclimatology delivered by glacial ice of alpine regions.

Recent exploratory analysis of a data set from the Austrian Network of Isotopes in Precipitation (ANIP) together with climatic data revealed significant correlations between isotopic compositions in precipitation and climatic conditions (A. Liebminger et al., 2006). Based on these results multivariate models have been developed in order to predict the oxygen 18 concentration from local climatic and geographic parameters. The best two models are applied to 201 new locations for which long term climatic and geographic parameters are available. The resulting modeled oxygen 18 values exhibit good compliance with known correlations of oxygen 18 with temperature and altitude. Isotope distribution charts lead to a reasonable picture of the distribution of oxygen 18 in Austrian precipitation.

Abstract. In the last decades, the 18O/16O signature of meteoric water became a key tracer intensively used both in hydrology and in paleoclimatology, based primarily on the correlation of the 18O/16O ratio in precipitation with temperature. This correlation with temperature is generally well understood as a result of Rayleigh processes of atmospheric vapour during the formation of precipitation. The resulting isotopic signals in precipitation are also transferred into the groundwater body since the isotopic composition of groundwater is determined by the precipitation infiltrating into the ground. However, the whole variability of the 18O/16O ratio especially in temporal data series of precipitation and groundwater can not be explained with temperature alone. Here we show that certain interactions between different climate induced changes in local parameters prevailing during precipitation events are able to explain a significant part of the observed deviation. These effects are superimposed by an overall isotopic pattern representing the large scale climate input primarily based on temperature. The intense variability of isotopes due to the particular topography of Austria recorded over a time period of 40 years provides an unique possibility to uncover this hidden information contributed by relative humidity and type of precipitation. Since there is a growing need to predict the variation of climate together with its associated potential hazards like floods and dry periods the results of this work are contributing to a better overall understanding of the complex interaction of climate with the corresponding water cycle.

No abstract

Process monitoring is a critical task in ensuring the consistent quality of the final drug product in biopharmaceutical formulation, fill, and finish (FFF) processes. Data generated during FFF monitoring includes multiple time series and high-dimensional data, which is typically investigated in a limited way and rarely examined with multivariate data analysis (MVDA) tools to optimally distinguish between normal and abnormal observations. Data alignment, data cleaning and correct feature extraction of time series of various FFF sources are resource-intensive tasks, but nonetheless they are crucial for further data analysis. Furthermore, most commercial statistical software programs offer only nonrobust MVDA, rendering the identification of multivariate outliers error-prone. To solve this issue, we aimed to develop a novel, automated, multivariate process monitoring workflow for FFF processes, which is able to robustly identify root causes in process-relevant FFF features. We demonstrate the successful implementation of algorithms capable of data alignment and cleaning of time-series data from various FFF data sources, followed by the interconnection of the time-series data with process-relevant phase settings, thus enabling the seamless extraction of process-relevant features. This workflow allows the introduction of efficient, high-dimensional monitoring in FFF for a daily work-routine as well as for continued process verification (CPV).