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Background: One of the numerous challenges preterm birth poses for parents and physicians is prognostic disclosure. Prognoses are based on scientific evidence and medical experience. They are subject to individual assessment and will generally remain uncertain with regard to the individual. This can result in differences in prognostic framing and thus affect the recipients' perception. In neonatology, data on the effects of prognostic framing are scarce. In particular, it is unclear whether parents prefer a more optimistic or a more pessimistic prognostic framing.Objective: To explore parents' preferences concerning prognostic framing and its effects on parent-reported outcomes and experiences. To identify predictors (demographic, psychological) of parents' communication preferences.Design, Setting, Participants: Unblinded, randomized controlled crossover trial (RCT) at the Division of Neonatology of the University Medical Center Mainz, Germany, including German-speaking parents or guardians of infants born preterm between 2010 and 2019 with a birth weight < 1500 g. Inclusion of up to 204 families is planned, with possible revision according to a blinded sample size reassessment.Intervention: Embedded in an online survey and in pre-specified order, participants will watch two video vignettes depicting a more optimistic vs. a more pessimistic framing in prognostic disclosure to parents of a preterm infant. Apart from prognostic framing, all other aspects of physician-parent communication are standardized in both videos.Main Outcomes and Measures: At baseline and after each video, participants complete a two-part online questionnaire (baseline and post-intervention). Primary outcome is the preference for either a more optimistic or a more pessimistic prognostic framing. Secondary outcomes include changes in state-anxiety (STAI-SKD), satisfaction with prognostic framing, evaluation of prognosis, future optimism and hope, preparedness for shared decision-making (each assessed using customized questions), and general impression (customized question), professionalism (adapted from GMC Patient Questionnaire) and compassion (Physician Compassion Questionnaire) of the consulting physician.Discussion: This RCT will explore parents' preferences concerning prognostic framing and its effects on physician-parent communication. Results may contribute to a better understanding of parental needs in prognostic disclosure and will be instrumental for a broad audience of clinicians, scientists, and ethicists.Trial Registration: German Clinical Trials Register DRKS00024466 . Registered on April 16, 2021. [ABSTRACT FROM AUTHOR]

Pluvial floods in urban areas are caused by local, fast storm events with very high rainfall rates, which lead to inundation of streets and buildings before the storm water reaches a watercourse. An increase in frequency and intensity of heavy rainfall events and an ongoing urbanization may further increase the risk of pluvial flooding in many urban areas. Currently, warnings for pluvial floods are mostly limited to information on rainfall intensities and durations over larger areas, which is often not detailed enough to effectively protect people and goods. We present a proof‐of‐concept for an impact‐based forecasting system for pluvial floods. Using a model chain consisting of a rainfall forecast, an inundation, a contaminant transport and a damage model, we are able to provide predictions for the expected rainfall, the inundated areas, spreading of potential contamination and the expected damage to residential buildings. We use a neural network‐based inundation model, which significantly reduces the computation time of the model chain. To demonstrate the feasibility, we perform a hindcast of a recent pluvial flood event in an urban area in Germany. The required spatio‐temporal accuracy of rainfall forecasts is still a major challenge, but our results show that reliable impact‐based warnings can be forecasts are available up to 5 min before the peak of an extreme rainfall event. Based on our results, we discuss how the outputs of the impact‐based forecast could be used to disseminate impact‐based early warnings. Plain Language Summary: Pluvial floods are caused by local rain storms with extreme rainfall rates, which leads to immediate flooding of streets and buildings in urban areas. These events are expected to increase in the future due to climate change and growing urban areas. Pluvial floods are directly caused by a rainstorm, which gives citizens and emergency responders usually only a few minutes to act. Existing forecasting systems for pluvial floods are limited to rainfall forecasts that neither provide information about where a flood might occur nor how severe the impacts will be. Here, the main challenge is that current computer models that predict inundation take too long to run to release flood forecasts early enough. We present a new inundation model that can predict inundation for an upcoming flood event in a fraction of the time of existing models. We combine this model with models that predict the spreading of contamination (e.g., from a car accident) and the damage to residential buildings. For a real flood event we can show that this information can be released up to 5 min before the rainfall peak, which gives citizens and emergency responders the opportunities to safe lives and protect important valuables. Key Points: First impact‐based forecasting for pluvial foodsArtificial neural network inundation model significantly cuts calculation time to 0.1% of a physically based model with comparable accuracyForecast with estimates for inundated areas, spreading of contaminants and expected damage could be released 5 min before peak rainfall [ABSTRACT FROM AUTHOR]

Coupled numerical models, which simulate water and energy fluxes in the subsurface-land surface-atmosphere system in a physically consistent way are a prerequisite for the analysis and a better understanding of heat and matter exchange fluxes at compartmental boundaries and interdependencies of states across these boundaries. Complete state evolutions generated by such models may be regarded as a proxy of the real world, provided they are run at sufficiently high resolution and incorporate the most important processes. Such a virtual reality can be used to test hypotheses on the functioning of the coupled terrestrial system. Coupled simulation systems, however, face severe problems caused by the vastly different scales of the processes acting in and between the compartments of the terrestrial system, which also hinders comprehensive tests of their realism. We used the Terrestrial Systems Modeling Platform TerrSysMP, which couples the meteorological model COSMO, the land-surface model CLM, and the subsurface model ParFlow, to generate a virtual catchment for a regional terrestrial system mimicking the Neckar catchment in southwest Germany. Simulations for this catchment are made for the period 2007-2015, and at a spatial resolution of 400 m for the land surface and subsurface and 1.1 km for the atmosphere. Among a discussion of modelling challenges, the model performance is evaluated based on real observations covering several variables of the water cycle. We find that the simulated (virtual) catchment behaves in many aspects quite close to observations of the real Neckar catchment, e.g. concerning atmospheric boundary-layer height, precipitation, and runoff. But also discrepancies become apparent, both in the ability of the model to correctly simulate some processes which still need improvement such as overland flow, and in the realism of some observation operators like the satellite based soil moisture sensors. The whole raw dataset is available for interested users. The dataset described here is available via the CERA database (Schalge et al., 2020): https://doi.org/10.26050/WDCC/Neckar%5FVCS%5Fv1. [ABSTRACT FROM AUTHOR]

The simulation of pluvial urban flood events is often based on time series of spatially uniformly distributed rain events. The aim of the present study is to investigate the impact of temporally and spatially variable rain events ('moving' rain events) on the flood volume. Therefore, a method to generate moving rain events from gauge data was developed and tested in a case study on a smaller urban catchment. The moving rain events are generated on a spatial grid with one kilometre resolution, which is the spatial resolution of the radar based rainfall prediction in Germany. In order to ensure the comparability of results to those of a non-moving rain scenario, the rain volume in the whole catchment is maintained and the time series at location of the rain gauge are the same in the moving and in the static event. In the current implementation of the approach, wind directions from north, south, east and west are tested. The generated moving rain events were used as input for the coupled 1D/2D pipe flow and surface flow model HYSTEM-EXTRAN 2D.The tested rain events had return periods in a range from 10 to 20 years. Generally, results from the moving rain and the static rain scenarios differ. First results have shown a difference in peak of flood volume of more than 40 percent comparing different wind directions. The big difference is due to inhomogeneity in building density and the drainage direction of the pipe network. The difference of the time of the peak of flood volume never exceeded five minutes. The highest flood volumes occurred with moving direction of the rain event equal to the drainage direction of the pipe network. These first results from the case study leading to the conclusion that moving rain events instead of static rain events should be used as input for realistic urban flood simulations. [ABSTRACT FROM AUTHOR]

Opinion. Discusses the German system of granting tenure to postdoctoral researchers. The problem of habilitation; What habilitation is; Why it was introduced by Wilhelm von Humboldt during the Prussian university reforms; The effect of the Max Planck Society upon the universities' science monopoly.