Extreme rainstorms: Comparing regional envelope curves to stochastically generated events

The depth-duration envelope curves (DDECs) are regional upper bounds on observed rainfall maxima for several durations. Recently, a probabilistic interpretation has been proposed in the literature in order to associate a recurrence interval T to the DDECs and, consequently, to retrieve point rainfall quantiles for ungauged sites. Alternatively, extreme rainfall quantiles can be retrieved from long synthetic rainfall series obtained with stochastic rainfall generators calibrated to local time series of rainfall events. While DDECs are sensitive to outliers and data errors, the stochastic rainfall generator performance is affected by the limited record lengths used for calibration. The objective of this study is to assess the reliability of the two alternative methods by verifying if they give consistent results for a wide study region in Austria. Relative to previous studies, we propose some generalizations of the DDEC procedure in order to better represent the Austrian data. The comparison of rainfall quantiles estimated with the two methods for large T shows an excellent agreement for intermediate durations (from 1 to 6 h), while the agreement worsen for very short (15 min) and long (24 h) durations. The results are scrupulously analyzed and discussed in light of the exceptionality of rainfall events that set the regional envelopes and the characteristics of the stochastic generator used. Our study points out that the combined use of these regional and local methods can be very useful for estimating reliable point rainfall quantiles associated with large T within regions where many rain gauges are available, but with limited record lengths

Preface: HS01 - Changes in flood risk and perception in catchments and cities

Preface of the Editors to the volume: Changes in Flood Risk and Perception in Catchments and Cities

Estimating the reproducibility of psychological science

One of the central goals in any scientific endeavor is to understand causality. Experiments that seek to demonstrate a cause/effect relation most often manipulate the postulated causal factor. Aarts et al. describe the replication of 100 experiments reported in papers published in 2008 in three high-ranking psychology journals. Assessing whether the replication and the original experiment yielded the same result according to several criteria, they find that about one-third to one-half of the original findings were also observed in the replication study

The AlpArray Seismic Network: A Large-Scale European Experiment to Image the Alpine Orogen

The AlpArray programme is a multinational, European consortium to advance our understanding of orogenesis and its relationship to mantle dynamics, plate reorganizations, surface processes and seismic hazard in the Alps-Apennines-Carpathians-Dinarides orogenic system. The AlpArray Seismic Network has been deployed with contributions from 36 institutions from 11 countries to map physical properties of the lithosphere and asthenosphere in 3D and thus to obtain new, high-resolution geophysical images of structures from the surface down to the base of the mantle transition zone. With over 600 broadband stations operated for 2 years, this seismic experiment is one of the largest simultaneously operated seismological networks in the academic domain, employing hexagonal coverage with station spacing at less than 52 km. This dense and regularly spaced experiment is made possible by the coordinated coeval deployment of temporary stations from numerous national pools, including ocean-bottom seismometers, which were funded by different national agencies. They combine with permanent networks, which also required the cooperation of many different operators. Together these stations ultimately fill coverage gaps. Following a short overview of previous large-scale seismological experiments in the Alpine region, we here present the goals, construction, deployment, characteristics and data management of the AlpArray Seismic Network, which will provide data that is expected to be unprecedented in quality to image the complex Alpine mountains at depth