Regionale Alterung in Deutschland unter besonderer Berücksichtigung der Binnenwanderungen: Teil A und B

"Der vorliegende Materialienband beinhaltet eine detaillierte Beschreibung der demographischen Alterung und der Binnenwanderung in den Bundesländern. Hierzu werden die einzelnen Komponenten der Bevölkerungsentwicklung (Fertilität, Mortalität und Migration) sowohl im gesamtdeutschen Überblick als auch in den jeweiligen Bundesländern analysiert. Ergänzt werden die demographischen Daten mit ökonomischen Kurzbeschreibungen der einzelnen Bundesländer, die einen Bezug zu den Binnenwanderungen herstellen. Als Datenbasis für die hier vorliegende Auswertung dienen, wenn nicht anders vermerkt, Materialien und Daten des Statistischen Bundesamtes und der Statistischen Landesämter. Die demographische Entwicklung in Deutschland insgesamt und in den einzelnen Bundesländern wird innerhalb der Jahre 1991 bis 2004 dargestellt und miteinander verglichen. Als Ausblick dienen die Länderergebnisse der 10. koordinierten Bevölkerungsvorausberechnung des Statistischen Bundesamtes und der Statistischen Landesämter bis zum Jahr 2050. Die vorliegende Arbeit ist in zwei Teile (A und B) unterteilt. In Teil A, bearbeitet von Ralf Mai, Juliane Roloff und Frank Micheel, wird der Verlauf der demographischen Alterung in Deutschland und den Bundesländern innerhalb des gewählten Zeitraums dargestellt. In Teil B (Ralf Mai, unter Mitarbeit von Manfred Scharein) stehen die Binnenwanderungen im Mittelpunkt. Es werden die Trends der Binnenwanderungen zwischen den Bundesländern analysiert und in Modellrechnungen ihr Einfluss auf Bevölkerungsbestand, Geburtenzahlen und Alterung in den einzelnen Ländern berechnet." (Autorenreferat

Enhancing long short-term memory (LSTM)-based streamflow prediction with a spatially distributed approach

Deep learning (DL) algorithms have previously demonstrated their effectiveness in streamflow prediction. However, in hydrological time series modelling, the performance of existing DL methods is often bound by limited spatial information, as these data-driven models are typically trained with lumped (spatially aggregated) input data. In this study, we propose a hybrid approach, namely the Spatially Recursive (SR) model, that integrates a lumped long short-term memory (LSTM) network seamlessly with a physics-based hydrological routing simulation for enhanced streamflow prediction. The lumped LSTM was trained on the basin-averaged meteorological and hydrological variables derived from 141 gauged basins located in the Great Lakes region of North America. The SR model involves applying the trained LSTM at the subbasin scale for local streamflow predictions which are then translated to the basin outlet by the hydrological routing model. We evaluated the efficacy of the SR model with respect to predicting streamflow at 224 gauged stations across the Great Lakes region and compared its performance to that of the standalone lumped LSTM model. The results indicate that the SR model achieved performance levels on par with the lumped LSTM in basins used for training the LSTM. Additionally, the SR model was able to predict streamflow more accurately on large basins (e.g., drainage area greater than 2000 km2), underscoring the substantial information loss associated with basin-wise feature aggregation. Furthermore, the SR model outperformed the lumped LSTM when applied to basins that were not part of the LSTM training (i.e., pseudo-ungauged basins). The implication of this study is that the lumped LSTM predictions, especially in large basins and ungauged basins, can be reliably improved by considering spatial heterogeneity at finer resolution via the SR model.</p

Combining satellite data and appropriate objective functions for improved spatial pattern performance of a distributed hydrologic model

Abstract. Satellite-based earth observations offer great opportunities to improve spatial model predictions by means of spatial-pattern-oriented model evaluations. In this study, observed spatial patterns of actual evapotranspiration (AET) are utilised for spatial model calibration tailored to target the pattern performance of the model. The proposed calibration framework combines temporally aggregated observed spatial patterns with a new spatial performance metric and a flexible spatial parameterisation scheme. The mesoscale hydrologic model (mHM) is used to simulate streamflow and AET and has been selected due to its soil parameter distribution approach based on pedo-transfer functions and the build in multi-scale parameter regionalisation. In addition two new spatial parameter distribution options have been incorporated in the model in order to increase the flexibility of root fraction coefficient and potential evapotranspiration correction parameterisations, based on soil type and vegetation density. These parameterisations are utilised as they are most relevant for simulated AET patterns from the hydrologic model. Due to the fundamental challenges encountered when evaluating spatial pattern performance using standard metrics, we developed a simple but highly discriminative spatial metric, i.e. one comprised of three easily interpretable components measuring co-location, variation and distribution of the spatial data. The study shows that with flexible spatial model parameterisation used in combination with the appropriate objective functions, the simulated spatial patterns of actual evapotranspiration become substantially more similar to the satellite-based estimates. Overall 26 parameters are identified for calibration through a sequential screening approach based on a combination of streamflow and spatial pattern metrics. The robustness of the calibrations is tested using an ensemble of nine calibrations based on different seed numbers using the shuffled complex evolution optimiser. The calibration results reveal a limited trade-off between streamflow dynamics and spatial patterns illustrating the benefit of combining separate observation types and objective functions. At the same time, the simulated spatial patterns of AET significantly improved when an objective function based on observed AET patterns and a novel spatial performance metric compared to traditional streamflow-only calibration were included. Since the overall water balance is usually a crucial goal in hydrologic modelling, spatial-pattern-oriented optimisation should always be accompanied by traditional discharge measurements. In such a multi-objective framework, the current study promotes the use of a novel bias-insensitive spatial pattern metric, which exploits the key information contained in the observed patterns while allowing the water balance to be informed by discharge observations.</jats:p

The pie sharing problem: Unbiased sampling of N+1 summative weights

A simple algorithm is provided for randomly sampling a set of N+1 weights such that their sum is constrained to be equal to one, analogous to randomly subdividing a pie into N+1 slices where the probability distribution of slice volumes are identically distributed. The cumulative density and probability density functions of the random weights are provided. The algorithmic implementation for the random number sampling are made available. This algorithm has potential applications in calibration, uncertainty analysis, and sensitivity analysis of environmental models. Three example applications are provided to demonstrate the efficiency and superiority of the proposed method compared to alternative sampling methods.Global Water Futures Project || Integrated Modeling Program for Canada

Hydrologic parameter sensitivity across a large-domain

Canada First Research Excellence FundNon-Peer ReviewedDue to the computational demands of modelling large domains, model calibration of Land Surface Models (LSMs) typically involves adjustment of only a small subset of parameters. • Majority of parameters that can potentially contribute to the model output variance remain fixed/ hard coded. • Spatial variability of the parameter sensitivity over large domains with heterogenous climatic and physiographic conditions is largely ignored during the calibration process. This work, carried out for parts of the Fraser and Columbia river basins, explored how parameter sensitivity varies spatially with the dominant physical and climatic conditions, and how dominant model parameters change depending on the simulated hydrologic process. The study found that parameter sensitivity varies both geographically and with the process being simulated

Cardiovalve in mitral valve position—Additional solution for valve replacement

We report on a 72 years old male patient with recurrent heart failure hospitalizations caused by severe mitral regurgitation due to severe restriction of the posterior mitral leaflet treated with the transfemoral mitral valve replacement (TMVR) system Cardiovalve. Immediate interventional success was obtained resulting in a quick mobilization and discharge

Efficient treatment of climate data uncertainty in ensemble Kalman filter (EnKF) based on an existing historical climate ensemble dataset

The final publication is available at Elsevier via https://doi.org/10.1016/j.jhydrol.2018.11.047. © 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/Successful data assimilation depends on the accurate estimation of forcing data uncertainty. Forcing data uncertainty is typically estimated based on statistical error models. In practice, the hyper-parameters of statistical error models are often estimated by a trial-and-error tuning process, requiring significant analyst and computational time. To improve the efficiency of forcing data uncertainty estimation, this study proposes the direct use of existing ensemble climate products to represent climate data uncertainty in the ensemble Kalman filter (EnKF) of flow forecasting. Specifically, the Newman et al. (2015) dataset (N15 for short), covering the contiguous United States, northern Mexico, and southern Canada, is used here to generate the precipitation and temperature ensemble in the EnKF application. This study for the first time compares the N15 generated climate ensemble with the carefully tuned hyper-parameters generated climate ensemble in a real flow forecasting framework. The forecast performance comparison of 20 Québec catchments shows that the N15 generated climate ensemble yields improved or similar deterministic and probabilistic flow forecasts relative to the carefully tuned hyper-parameters generated climate ensemble. Improvements are most evident for short lead times (i.e., 1–3 days) when the influence of data assimilation dominates. However, the analysis and computational time required to use N15 is much less compared to the typical trial-and-error hyper-parameter tuning process.Natural Sciences and Engineering Research Council [NETGP 451456

On how data are partitioned in model development and evaluation: Confronting the elephant in the room to enhance model generalization

Models play a pivotal role in advancing our understanding of Earth\u27s physical nature and environmental systems, aiding in their efficient planning and management. The accuracy and reliability of these models heavily rely on data, which are generally partitioned into subsets for model development and evaluation. Surprisingly, how this partitioning is done is often not justified, even though it determines what model we end up with, how we assess its performance and what decisions we make based on the resulting model outputs. In this study, we shed light on the paramount importance of meticulously considering data partitioning in the model development and evaluation process, and its significant impact on model generalization. We identify flaws in existing data-splitting approaches and propose a forward-looking strategy to effectively confront the “elephant in the room”, leading to improved model generalization capabilities

Repression and 3D-restructuring resolves regulatory conflicts in evolutionarily rearranged genomes

Regulatory landscapes drive complex developmental gene expression, but it remains unclear how their integrity is maintained when incorporating novel genes and functions during evolution. Here, we investigated how a placental mammal-specific gene, Zfp42, emerged in an ancient vertebrate topologically associated domain (TAD) without adopting or disrupting the conserved expression of its gene, Fat1. In ESCs, physical TAD partitioning separates Zfp42 and Fat1 with distinct local enhancers that drive their independent expression. This separation is driven by chromatin activity and not CTCF/cohesin. In contrast, in embryonic limbs, inactive Zfp42 shares Fat1’s intact TAD without responding to active Fat1 enhancers. However, neither Fat1 enhancer-incompatibility nor nuclear envelope-attachment account for Zfp42’s unresponsiveness. Rather, Zfp42’s promoter is rendered inert to enhancers by context-dependent DNA methylation. Thus, diverse mechanisms enabled the integration of independent Zfp42 regulation in the Fat1 locus. Critically, such regulatory complexity appears common in evolution as, genome wide, most TADs contain multiple independently expressed genes.We thank the Montpellier Ressources Imagerie facility (BioCampus Montpellier, Centre National de la Recherche Scientifique [CNRS], INSERM, University of Montpellier) and for computer resources from CINECA (ISCRA grant thanks to computer resources from INFN and CINECA [ISCRA Grant HP10C8JWU7]). G.C., Q.S., and F.B. were supported by a grant from the European Research Council (Advanced Grant 3DEpi, 788972) and by the CNRS. This work was funded by EMBO and the Wellcome Trust (ALTF1554-2016 and 206475/Z/17/Z; to M.I.R.) as well as the Deutsche Forschungsgemeinschaft (KR3985/7-3 and MU 880/16-1 to S.M.)

Great Lakes Runoff Intercomparison Project Phase 3: Lake Erie (GRIP-E)

Hydrologic model intercomparison studies help to evaluate the agility of models to simulate variables such as streamflow, evaporation, and soil moisture. This study is the third in a sequence of the Great Lakes Runoff Intercomparison Projects. The densely populated Lake Erie watershed studied here is an important international lake that has experienced recent flooding and shoreline erosion alongside excessive nutrient loads that have contributed to lake eutrophication. Understanding the sources and pathways of flows is critical to solve the complex issues facing this watershed. Seventeen hydrologic and land-surface models of different complexity are set up over this domain using the same meteorological forcings, and their simulated streamflows at 46 calibration and seven independent validation stations are compared. Results show that: (1) the good performance of Machine Learning models during calibration decreases significantly in validation due to the limited amount of training data; (2) models calibrated at individual stations perform equally well in validation; and (3) most distributed models calibrated over the entire domain have problems in simulating urban areas but outperform the other models in validation