The robustness of the derived design life levels of heavy precipitation events in the pre-alpine Oberland region of Southern Germany

Extreme value analysis (EVA) is well-established to derive hydrometeorological design values for infrastructures that have to withstand extreme events. Since there is concern about increased extremes with higher hazard potential under climate change, alterations of EVA are introduced for which statistical properties are no longer assumed to be constant but vary over time. In this study, both stationary and non-stationary EVA models are used to derive design life levels (DLLs) of daily precipitation in the pre-alpine Oberland region of Southern Germany, an orographically complex region characterized by heavy precipitation events and climate change. As EVA is fraught with uncertainties, it is crucial to quantify its methodological impacts: two theoretical distributions (i.e., Generalized Extreme Value (GEV) and Generalized Pareto (GP) distribution), four different parameter estimation techniques (i.e., Maximum Likelihood Estimation (MLE), L-moments, Generalized Maximum Likelihood Estimation (GMLE), and Bayesian estimation method) are evaluated and compared. The study reveals large methodological uncertainties. Discrepancies due to the parameter estimation methods may reach up to 45% of the mean absolute value, while differences between stationary and non-stationary models are of the same magnitude (differences in DLLs up to 40%). For the end of this century in the Oberland region, there is no robust tendency towards increased extremes found

The Robustness of the Derived Design Life Levels of Heavy Precipitation Events in the Pre-Alpine Oberland Region of Southern Germany

Extreme value analysis (EVA) is well-established to derive hydrometeorological design values for infrastructures that have to withstand extreme events. Since there is concern about increased extremes with higher hazard potential under climate change, alterations of EVA are introduced for which statistical properties are no longer assumed to be constant but vary over time. In this study, both stationary and non-stationary EVA models are used to derive design life levels (DLLs) of daily precipitation in the pre-alpine Oberland region of Southern Germany, an orographically complex region characterized by heavy precipitation events and climate change. As EVA is fraught with uncertainties, it is crucial to quantify its methodological impacts: two theoretical distributions (i.e., Generalized Extreme Value (GEV) and Generalized Pareto (GP) distribution), four different parameter estimation techniques (i.e., Maximum Likelihood Estimation (MLE), L-moments, Generalized Maximum Likelihood Estimation (GMLE), and Bayesian estimation method) are evaluated and compared. The study reveals large methodological uncertainties. Discrepancies due to the parameter estimation methods may reach up to 45% of the mean absolute value, while differences between stationary and non-stationary models are of the same magnitude (differences in DLLs up to 40%). For the end of this century in the Oberland region, there is no robust tendency towards increased extremes found

Near surface roughness estimation: A parameterization derived from artificial rainfall experiments and two-dimensional hydrodynamic modelling for multiple vegetation coverages

Roughness is the key parameter for surface runoff simulations. This study aims to determine robust Manning resistance coefficients () on the basis of consecutive artificial rainfall experiments on natural hillslopes available in literature, obtained at 22 different sites with different degrees of vegetation cover and type. The Manning resistance coefficient is particularly important in the context of two dimensional (2D) hydraulic heavy rainfall simulations. Since there is a wide range of possible resistance values available leading to significantly different results regarding the accumulation of surface runoff, especially for shallow water depths. The planning of flood protection structures is directly affected by these uncertainties. This work also improves the knowledge between roughness and the shape of the hydrograph allowing a better calibration of infiltration models. As flow velocity, water depth, and infiltration rate were not observed during the rainfall experiments, only the outflow of the test field and rain intensity are known. For this purpose, a framework was developed to parameterize shallow water depth (< 1 cm) -dependent roughness coefficients. To test the robustness of the framework, three different formulations of depth-dependent roughness and a constant Manning coefficient are used by comparing the measured discharge under different rainfall intensities with simulations in a 2D-hydraulic model. We identified a strong dependency of Manning’s on the degree of vegetation cover and -type as well as an influence of consecutive rainfall events. This finally leads to a more robust parameterization of near surface roughness for hydrodynamic modelling, which is particularly important for the simulation of heavy rainfall events

Near surface roughness estimation: a parameterization derived from artificial rainfall experiments and two-dimensional hydrodynamic modelling for multiple vegetation coverages

Roughness is the key parameter for surface runoff simulations. This study aims to determine robust Manning resistance coefficients on the basis of consecutive artificial rainfall experiments on natural hillslopes available in literature, obtained at 22 different sites with different degrees of vegetation cover and type. The Manning resistance coefficient is particularly important in the context of two-dimensional (2D) hydraulic heavy rainfall simulations. Since there is a wide range of possible resistance values available leading to significantly different results regarding the accumulation of surface runoff, especially for shallow water depths. The planning of flood protection structures is directly affected by these uncertainties. This work also improves the knowledge between roughness and the shape of the hydrograph allowing a better calibration of infiltration models. As flow velocity, water depth, and infiltration rate were not observed during the rainfall experiments, only the outflow of the test field and rain intensity are known. For this purpose, a framework was developed to parameterize shallow water depth ( cm) -dependent roughness coefficients. To test the robustness of the framework, three different formulations of depth-dependent roughness and a constant Manning coefficient are used by comparing the measured discharge under different rainfall intensities with simulations in a 2D-hydraulic model. We identified a strong dependency of Manning’s on the degree of vegetation cover and -type as well as an influence of consecutive rainfall events. This finally leads to a more robust parameterization of near surface roughness for hydrodynamic modelling, which is particularly important for the simulation of heavy rainfall events

Comment on the letter of the Society of Vertebrate Paleontology (SVP) dated April 21, 2020 regarding “Fossils from conflict zones and reproducibility of fossil‑based scientific data”: Myanmar amber

Non peer reviewe

Pulmonary Hypertension in Adults with Congenital Heart Disease: Real-World Data from the International COMPERA-CHD Registry

Introduction: Pulmonary hypertension (PH) is a common complication in patients with congenital heart disease (CHD), aggravating the natural, post-operative, or post-interventional course of the underlying anomaly. The various CHDs differ substantially in characteristics, functionality, and clinical outcomes among each other and compared with other diseases with pulmonary hypertension. Objective: To describe current management strategies and outcomes for adults with PH in relation to different types of CHD based on real-world data. Methods and results: COMPERA (Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension) is a prospective, international PH registry comprising, at the time of data analysis, >8200 patients with various forms of PH. Here, we analyzed a subgroup of 680 patients with PH due to CHD, who were included between 2007 and 2018 in 49 specialized centers for PH and/or CHD located in 11 European countries. At enrollment, the patients’ median age was 44 years (67% female), and patients had either pre-tricuspid shunts, post-tricuspid shunts, complex CHD, congenital left heart or aortic disease, or miscellaneous other types of CHD. Upon inclusion, targeted therapies for pulmonary arterial hypertension (PAH) included endothelin receptor antagonists, PDE-5 inhibitors, prostacyclin analogues, and soluble guanylate cyclase stimulators. Eighty patients with Eisenmenger syndrome were treatment-naïve. While at inclusion the primary PAH treatment for the cohort was monotherapy (70% of patients), with 30% of the patients on combination therapy, after a median observation time of 45.3 months, the number of patients on combination therapy had increased significantly, to 50%. The use of oral anticoagulants or antiplatelets was dependent on the underlying diagnosis or comorbidities. In the entire COMPERA-CHD cohort, after follow-up and receiving targeted PAH therapy (n = 511), 91 patients died over the course of a 5-year follow up. The 5-year Kaplan–Meier survival estimate for CHD associated PH was significantly better than that for idiopathic PAH (76% vs. 54%; p < 0.001). Within the CHD associated PH group, survival estimates differed particularly depending on the underlying diagnosis and treatment status. Conclusions: In COMPERA-CHD, the overall survival of patients with CHD associated PH was dependent on the underlying diagnosis and treatment status, but was significantly better as than that for idiopathic PAH. Nevertheless, overall survival of patients with PAH due to CHD was still markedly reduced compared with survival of patients with other types of CHD, despite an increasing number of patients on PAH-targeted combination therapy

Determining crystal structures through crowdsourcing and coursework

We show here that computer game players can build high-quality crystal structures. Introduction of a new feature into the computer game Foldit allows players to build and real-space refine structures into electron density maps. To assess the usefulness of this feature, we held a crystallographic model-building competition between trained crystallographers, undergraduate students, Foldit players and automatic model-building algorithms. After removal of disordered residues, a team of Foldit players achieved the most accurate structure. Analysing the target protein of the competition, YPL067C, uncovered a new family of histidine triad proteins apparently involved in the prevention of amyloid toxicity. From this study, we conclude that crystallographers can utilize crowdsourcing to interpret electron density information and to produce structure solutions of the highest quality