The DataHubCommunity – fosteringFAIR and sustainable research data management across our research field

The DataHub of the Research Field Earth and Environment is a joint initiative of all centers of the Helmholtz Association participating in the research program \u27Changing Earth’. Within the DataHub, data management tools are developed and made available, and data products are offered in thematic viewers. The continuous and long-term development process of these solutions is the DataHub\u27s task. The here published poster has been presented during the General Assembly of the Program "Changing Earth - Sustaining our Future" (May 15-16, 2023) in Karlsruhe

Photography-aided gravity modeling of solid bodies

Not all secrets of the The Great Pyramid of Giza were revealed, even after centuries of observation and research. One of the main questions concerns the construction of the pyramid. The most popular and reasonable theory assumes the old Egyptians to use an exterior ramp in the lower third and an interior ramp in the upper two thirds of the pyramid on which the stones were carried upstairs. However, there is no evidence that this is really true. Microgravimetry-measuring techniques are able to give information about the inner mass distribution of the pyramid and hence reveal yet unknown facts about the inner structure. Therefore, a reference gravity signal must be computed in order to detect mass deviations in the inside. In this work, an approach is discussed which uses photographs to construct a three-dimensional model of a body. It is shown that the information gained from three-dimensional reconstruction can be used to construct a solid body. For the computation of the gravity signal of this solid body an algorithm is applied which transforms the volume integral in Newton's law of gravity into line integrals, which allows the computation of gravitational quantities for arbitrary polyhedra. With the help of a small section of the Great Pyramid it is shown that detecting inner mass deviations from a reference body requires detailed knowledge about the surface. As the errors in the measured gravity signal caused by a mis-modeled body might have a high magnitude, the signal from inner mass deviations might completely vanish. However, if the surface of an object is well known it is indeed possible to make a statement about the inner structure of a body based on close-mesh measurements on its surface.Trotz jahrelanger Beobachtungen und Nachforschungen wurden nicht alle Geheimnisse der Großen Pyramide von Gizeh gelöst. Eine der Hauptfragen betrifft den Bau der Pyramide. Die gängigste Theorie besagt, dass die alten ägypter im unteren Drittel eine äußere aber in den zwei oberen Dritteln der Pyramide eine innere Rampe benutzt haben, um die Steine nach oben zu befördern. Allerdings gibt es bisher keinen Beweis, ob diese Theorie der Wahrheit entspricht. Aber es wird angenommen, dass mit mikrogravimetrischen Beobachtungen Informationen über die innere Massenverteilung der Pyramide gesammelt werden können um dadurch bisher unbekannte Eigenschaften über die innere Zusammensetzung aufzudecken. Daher muss ein Referenzsignal berechnet werden um Massenabweichungen im Inneren entdecken zu können. In dieser Arbeit wird ein Ansatz behandelt, bei dem Photographien genutzt werden, um ein drei-dimensionales Oberflächenmodell eines Körpers zu berechnen. Es wird gezeigt, dass die bei der Rekonstruktion enstandenden Daten genutzt werden können, um einen festen Körper zu konstruieren. Zur Berechnung des Schweresignals dieses Körpers wird ein Ansatz genutzt, bei dem die Volumenintegrale in Newton's Gravitationsgesetz in Linienintegrale transformiert werden, was die Berechnung von gravitationellen Größen beliebiger Polyhedren erlaubt. Mit Hilfe eines kleinen Ausschnittes der Großen Pyramide von Gizeh wird gezeigt, dass die Detektion von inneren Massenvariationen ein genaues Oberflächenmodell benötigt. Da die durch eine unsaubere Modellierung des Objekts hervorgerufenen Fehler bereits eine hohe Signalstärke haben können, ist es möglich dass das wahre Signal von inneren Massevariationen komplett darin verschwindet. Allerdings ermöglichen ein genaues Oberflächenmodell sowie engmaschige Messungen auf der Oberfläche die Detektion und Beschreibung von Massevariationen im Inneren

Theory and Algorithms

The article surveys and extends variational formulations of the thermodynamic free energy and discusses their information-theoretic content from the perspective of mathematical statistics. We revisit the well-known Jarzynski equality for nonequilibrium free energy sampling within the framework of importance sampling and Girsanov change-of-measure transformations. The implications of the different variational formulations for designing efficient stochastic optimization and nonequilibrium simulation algorithms for computing free energies are discussed and illustrated. View Full-Tex

Bias-corrected and spatially disaggregated seasonal forecasts: a long-term reference forecast product for the water sector in semi-arid regions

Seasonal forecasts have the potential to substantially improve water management particularly in water-scarce regions. However, global seasonal forecasts are usually not directly applicable as they are provided at coarse spatial resolutions of at best 36 km and suffer from model biases and drifts. In this study, we therefore apply a bias-correction and spatial-disaggregation (BCSD) approach to seasonal precipitation, temperature and radiation forecasts of the latest long-range seasonal forecasting system SEAS5 of the European Centre for Medium-Range Weather Forecasts (ECMWF). As reference we use data from the ERA5-Land offline land surface rerun of the latest ECMWF reanalysis ERA5. Thereby, we correct for model biases and drifts and improve the spatial resolution from 36 km to 0.1∘. This is performed for example over four predominately semi-arid study domains across the world, which include the river basins of the Karun (Iran), the São Francisco River (Brazil), the Tekeze–Atbara river and Blue Nile (Sudan, Ethiopia and Eritrea), and the Catamayo–Chira river (Ecuador and Peru). Compared against ERA5-Land, the bias-corrected and spatially disaggregated forecasts have a higher spatial resolution and show reduced biases and better agreement of spatial patterns than the raw forecasts as well as remarkably reduced lead-dependent drift effects. But our analysis also shows that computing monthly averages from daily bias-corrected forecasts particularly during periods with strong temporal climate gradients or heteroscedasticity can lead to remaining biases especially in the lowest- and highest-lead forecasts. Our SEAS5 BCSD forecasts cover the whole (re-)forecast period from 1981 to 2019 and include bias-corrected and spatially disaggregated daily and monthly ensemble forecasts for precipitation, average, minimum, and maximum temperature as well as for shortwave radiation from the issue date to the next 215 d and 6 months, respectively. This sums up to more than 100 000 forecasted days for each of the 25 (until the year 2016) and 51 (from the year 2017) ensemble members and each of the five analyzed variables. The full repository is made freely available to the public via the World Data Centre for Climate at https://doi.org/10.26050/WDCC/SaWaM_D01_SEAS5_BCSD (Domain D01, Karun Basin (Iran), Lorenz et al., 2020b), https://doi.org/10.26050/WDCC/SaWaM_D02_SEAS5_BCSD (Domain D02: São Francisco Basin (Brazil), Lorenz et al., 2020c), https://doi.org/10.26050/WDCC/SaWaM_D03_SEAS5_BCSD (Domain D03: basins of the Tekeze–Atbara and Blue Nile (Ethiopia, Eritrea, Sudan), Lorenz et al., 2020d), and https://doi.org/10.26050/WDCC/SaWaM_D04_SEAS5_BCSD (Domain D04: Catamayo–Chira Basin (Ecuador, Peru), Lorenz et al., 2020a). It is currently the first publicly available daily high-resolution seasonal forecast product that covers multiple regions and variables for such a long period. It hence provides a unique test bed for evaluating the performance of seasonal forecasts over semi-arid regions and as driving data for hydrological, ecosystem or climate impact models. Therefore, our forecasts provide a crucial contribution for the disaster preparedness and, finally, climate proofing of the regional water management in climatically sensitive regions

Modelling opinion dynamics under the impact of influencer and media strategies

Digital communication has made the public discourse considerably more complex, and new actors and strategies have emerged as a result of this seismic shift. Aside from the often-studied interactions among individuals during opinion formation, which have been facilitated on a large scale by social media platforms, the changing role of traditional media and the emerging role of “influencers” are not well understood, and the implications of their engagement strategies arising from the incentive structure of the attention economy even less so. Here we propose a novel framework for opinion dynamics that can accommodate various versions of opinion dynamics as well as account for different roles, namely that of individuals, media and influencers, who change their own opinion positions on different time scales. Numerical simulations of instances of this framework show the importance of their relative influence in creating qualitatively different opinion formation dynamics: with influencers, fragmented but short-lived clusters emerge, which are then counteracted by more stable media positions. The framework allows for mean-field approximations by partial differential equations, which reproduce those dynamics and allow for efficient large-scale simulations when the number of individuals is large. Based on the mean-field approximations, we can study how strategies of influencers to gain more followers can influence the overall opinion distribution. We show that moving towards extreme positions can be a beneficial strategy for influencers to gain followers. Finally, our framework allows us to demonstrate that optimal control strategies allow other influencers or media to counteract such attempts and prevent further fragmentation of the opinion landscape. Our modelling framework contributes to a more flexible modelling approach in opinion dynamics and a better understanding of the different roles and strategies in the increasingly complex information ecosystem

Does the Admixture of Forage Herbs Affect the Yield Performance, Yield Stability and Forage Quality of a Grass Clover Ley?

It is unclear whether the use of multi-species swards is a suitable measure for climate change adaptation by achieving high and stable dry matter (DM) production and good forage quality in grazing systems. The objective of the study is to evaluate whether a complex rather than a simple grass clover mixture enhances performance under nitrogen (N)-deficient conditions due to greater diversity in plant functional traits. During a four-year field experiment, a three-species and a seven-species grass clover mixture were compared under one cutting-for-conservation and two simulated grazing (defoliation every three or four weeks) treatments. The results revealed a similarity in the DM yields of both seed mixtures, indicating that in the given conditions the species in the simple mixture already offered crucial yield-determining functional traits. Different growth patterns, however, led to higher intra-annual yield stability in the complex mixture. In the cutting-for-conservation system, DM yields were higher, but this came at the expense of reduced metabolisable energy and crude protein contents and lower inter-annual yield stability. We conclude that higher seeding costs for multi-species mixtures are compensated by greater yield stability while offering the potential for additional eco-system services like enhanced carbon sequestration and diverse food for pollinators

Basin-scale runoff prediction: An Ensemble Kalman Filter framework based on global hydrometeorological data sets

In order to cope with the steady decline of the number of in situ gauges worldwide, there is a growing need for alternative methods to estimate runoff. We present an Ensemble Kalman Filter based approach that allows us to conclude on runoff for poorly or irregularly gauged basins. The approach focuses on the application of publicly available global hydrometeorological data sets for precipitation (GPCC, GPCP, CRU, UDEL), evapotranspiration (MODIS, FLUXNET, GLEAM, ERA interim, GLDAS), and water storage changes (GRACE, WGHM, GLDAS, MERRA LAND). Furthermore, runoff data from the GRDC and satellite altimetry derived estimates are used. We follow a least squares prediction that exploits the joint temporal and spatial auto- and cross-covariance structures of precipitation, evapotranspiration, water storage changes and runoff. We further consider time-dependent uncertainty estimates derived from all data sets. Our in-depth analysis comprises of 29 large river basins of different climate regions, with which runoff is predicted for a subset of 16 basins. Six configurations are analyzed: the Ensemble Kalman Filter (Smoother) and the hard (soft) Constrained Ensemble Kalman Filter (Smoother). Comparing the predictions to observed monthly runoff shows correlations larger than 0.5, percentage biases lower than ± 20%, and NSE-values larger than 0.5. A modified NSE-metric, stressing the difference to the mean annual cycle, shows an improvement of runoff predictions for 14 of the 16 basins. The proposed method is able to provide runoff estimates for nearly 100 poorly gauged basins covering an area of more than 11,500,000 km2 with a freshwater discharge, in volume, of more than 125,000 m3/s

Competing exciton localization effects due to disorder and shallow defects in semiconductor alloys

We demonstrate that excitons in semiconductor alloys are subject to competing localization effects due to disorder (random potential fluctuations) and shallow point defects (impurities). The relative importance of these effects varies with alloy chemical composition, impurity activation energy as well as temperature. We evaluate this effect quantitatively for MgxZn1−xO : Al (0 6 x 6 0.058) and find that exciton localization at low (2 K) and high (300 K) temperatures is dominated by shallow donor impurities and alloy disorder, respectively