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Coastal areas are highly diverse, ecologically rich, regions of key socio-economic activity, and are particularly sensitive to sea-level change. Over most of the 20th century, global mean sea level has risen mainly due to warming and subsequent expansion of the upper ocean layers as well as the melting of glaciers and ice caps. Over the last three decades, increased mass loss of the Greenland and Antarctic ice sheets has also started to contribute significantly to contemporary sea-level rise. The future mass loss of the two ice sheets, which combined represent a sea-level rise potential of similar to 65 m, constitutes the main source of uncertainty in long-term (centennial to millennial) sea-level rise projections. Improved knowledge of the magnitude and rate of future sea-level change is therefore of utmost importance. Moreover, sea level does not change uniformly across the globe and can differ greatly at both regional and local scales. The most appropriate and feasible sea level mitigation and adaptation measures in coastal regions strongly depend on local land use and associated risk aversion. Here, we advocate that addressing the problem of future sea-level rise and its impacts requires (i) bringing together a transdisciplinary scientific community, from climate and cryospheric scientists to coastal impact specialists, and (ii) interacting closely and iteratively with users and local stakeholders to co-design and co-build coastal climate services, including addressing the high-end risks.
An accurate estimation of crop yield under climate change scenarios is essential to quantify our ability to feed a growing population and develop agronomic adaptations to meet future food demand. A coordinated evaluation of yield simulations from process-based eco-physiological models for climate change impact assessment is still missing for soybean, the most widely grown grain legume and the main source of protein in our food chain. In this first soybean multi-model study, we used ten prominent models capable of simulating soybean yield under varying temperature and atmospheric CO2 concentration [CO2] to quantify the uncertainty in soybean yield simulations in response to these factors. Models were first parametrized with high quality measured data from five contrasting environments. We found considerable variability among models in simulated yield responses to increasing temperature and [CO2]. For example, under a + 3 degrees C temperature rise in our coolest location in Argentina, some models simulated that yield would reduce as much as 24%, while others simulated yield increases up to 29%. In our warmest location in Brazil, the models simulated a yield reduction ranging from a 38% decrease under + 3 degrees C temperature rise to no effect on yield. Similarly, when increasing [CO2] from 360 to 540 ppm, the models simulated a yield increase that ranged from 6% to 31%. Model calibration did not reduce variability across models but had an unexpected effect on modifying yield responses to temperature for some of the models. The high uncertainty in model responses indicates the limited applicability of individual models for climate change food projections. However, the ensemble mean of simulations across models was an effective tool to reduce the high uncertainty in soybean yield simulations associated with individual models and their parametrization. Ensemble mean yield responses to temperature and [CO2] were similar to those reported from the literature. Our study is the first demonstration of the benefits achieved from using an ensemble of grain legume models for climate change food projections, and highlights that further soybean model development with experiments under elevated [CO2] and temperature is needed to reduce the uncertainty from the individual models.
Although cosmic-ray neutron sensing (CRNS) is probably the most promising noninvasive proximal soil moisture measurement technique at the field scale, its application for hydrological simulations remains underexplored in the literature so far. This study assessed the use of CRNS to inversely calibrate soil hydraulic parameters at the intermediate field scale to simulate the groundwater recharge rates at a daily timescale. The study was conducted for two contrasting hydrological years at the Guaraira experimental basin, Brazil, a 5.84-km(2), a tropical wet and rather flat landscape covered by secondary Atlantic forest. As a consequence of the low altitude and proximity to the equator low neutron count rates could be expected, reducing the precision of CRNS while constituting unexplored and challenging conditions for CRNS applications. Inverse calibration for groundwater recharge rates was used based on CRNS or point-scale soil moisture data. The CRNS-derived retention curve and saturated hydraulic conductivity were consistent with the literature and locally performed slug tests. Simulated groundwater recharge rates ranged from 60 to 470 mm yr(-1), corresponding to 5 and 29% of rainfall, and correlated well with estimates based on water table fluctuations. In contrast, the estimated results based on inversive point-scale datasets were not in alignment with measured water table fluctuations. The better performance of CRNS-based estimations of field-scale hydrological variables, especially groundwater recharge, demonstrated its clear advantages over traditional invasive point-scale techniques. Finally, the study proved the ability of CRNS as practicable in low altitude, tropical wet areas, thus encouraging its adoption for water resources monitoring and management.
This article analyses incremental institutional change and subsequent organizational and performance outcomes of the digital transformation from a comparative perspective. Through 31 expert interviews, the authors compare two digitalized public services in Germany. Two digitalization approaches are identified. The voluntary, decentralized bottom-up approach involves layering of new rules, limited organizational restructuring, and performance deficits. Conversely, the compulsory, top-down approach with centralized control facilitates displacement of existing rules and far-reaching organizational change; in this study, it is also associated with improved performance.
Mathematical modelling and statistical inference provide a framework to evaluate different non-pharmaceutical and pharmaceutical interventions for the control of epidemics that has been widely used during the COVID-19 pandemic. In this paper, lessons learned from this and previous epidemics are used to highlight the challenges for future pandemic control. We consider the availability and use of data, as well as the need for correct parameterisation and calibration for different model frameworks. We discuss challenges that arise in describing and distinguishing between different interventions, within different modelling structures, and allowing both within and between host dynamics. We also highlight challenges in modelling the health economic and political aspects of interventions. Given the diversity of these challenges, a broad variety of interdisciplinary expertise is needed to address them, combining mathematical knowledge with biological and social insights, and including health economics and communication skills. Addressing these challenges for the future requires strong cross disciplinary collaboration together with close communication between scientists and policy makers.
Geodetic studies of crustal deformation using Global Navigation Satellite System (GNSS, earlier commonly referred to as Global Positioning System, GPS) measurements at CSIR-NGRI started in 1995 with the installation of a permanent GNSS station at CSIR-NGRI Hyderabad which later became an International GNSS Service (IGS) site. The CSIR-NGRI started expanding its GNSS networks after 2003 with more focussed studies through installation in the NE India, Himalayan arc, Andaman subduction zone, stable and failed rift regions of India plate. In each instance, these measurements helped in unravelling the geodynamics of the region and seismic hazard assessment, e.g., the discovery of a plate boundary fault in the Indo-Burmese wedge, rate and mode of strain accumulation and its spatial variation in the Garhwal-Kumaun and Kashmir region of the Himalayan arc, the influence of non-tectonic deformation on tectonic deformation in the Himalayan arc, nature of crustal deformation through earthquake cycle in the Andaman Sumatra subduction zone, and localised deformation in the intraplate region and across the paleo rift regions. Besides these, GNSS measurements initiated in the Antarctica region have helped in understanding the plate motion and influence of seasonal variations on deformation. Another important by-product of the GNSS observations is the capabilities of these observations in understanding the ionospheric variations due to earthquake processes and also due to solar eclipse. We summarize these outcomes in this article.
Zuerst erschienen in:
Alexander von Humboldt-Stiftung. Mitteilungen, 5. Jg., Heft 38, Oktober 1980, S. 27–36.
Off-road adventures
(2024)
This article focuses on the visual qualities of Alexander von Humboldt’s statistical tables in his Political Essay on the Kingdom of New Spain (1808–1811, 2nd ed. 1825–1827) with special attention to how such composites of numbers, alphabetical script, and semiotic elements relate to narrative writing. I argue that Humboldt’s tables/tableaus open up spaces inside his narrative that fragment the reading process, inviting new conversations, connections, and ideas.
Die deutsch-kubanische Forschungs- und Digitalisierungsinitiative „Proyecto Humboldt Digital“ (ProHD) hat während ihrer Projektlaufzeit (2019–2023) wichtige Quellen zum Thema „Humboldt und Kuba“ erstmals digital erschlossen. Als Kooperation zwischen der Berlin-Brandenburgischen Akademie der Wissenschaften und der Oficina del Historiador de la Ciudad de La Habana hat ProHD damit wichtige Akzente für die Archivdigitalisierung, die digitale Editionsphilologie und die digitale Wissenschaftskommunikation in Kuba gesetzt. Das Korpus der erschlossenen Bestände wird hier in fünf Schlaglichtern vorgestellt: 1) Quellen zur Humboldt’schen Forschungsreise, 2) Juan Luis de la Cuesta, 3) Materialien zu Kuba aus dem Humboldt-Nachlass, 4) Zensur und Beschlagnahme des Essai politique sur l’île de Cuba, 5) Francisco de Arango y Parreño.
La traduction en langue chinoise du premier volume du monumental ouvrage scientifique d'Alexander von Humboldt, intitulé «Cosmos», a vu le jour en 2023 sous l՚égide de la prestigieuse maison d՚édition de l՚Université de Pékin. Dans sa postface éclairée, la traductrice émérite, Gao Hong, éclaire la lanterne des lecteurs chinois sur la fresque cosmique esquissée par Humboldt, révélant ainsi les intrications entre les phénomènes naturels et leur pertinence à l՚échelle de l'univers tout entier. Gao Hong narre son propre périple aux côtés de Humboldt, tout en distillant ses réflexions personnelles sur cette fresque cosmique. «Cosmos» d՚Humboldt, œuvre scientifique par excellence, transcende également les sphères esthétiques et artistiques, exprimant invariablement une profonde vénération pour l՚univers. Restituer en chinois la «beauté géométrique» de la langue allemande, empreinte d՚une rigueur structurelle, constitue un défi singulier, le chinois se caractérisant par sa fluidité, sa souplesse et sa poésie imagée, en totale antithèse avec l՚allemand. En qualité de traducteur, il importe de naviguer librement entre ces deux mondes linguistiques distincts.