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Geister versammeln : Vorwort
(2011)
Bereits zu Beginn seiner Abhandlung "Das Unheimliche" (1919) weist Sigmund Freud darauf hin, dass "dies Wort nicht immer in einem scharf zu bestimmenden Sinne gebraucht wird". Entsprechend charakterisiert sich für Freud das Unheimliche durch eine Vielzahl an schwer zu fassenden Eigenschaften: Es bezeichnet eine seltsame Nähe zwischen Wissen und Nichtwissen, erscheint als etwas Vertrautes in fremder Gestalt oder als etwas Fremdes mit vertrauten Eigenschaften. Diese Unfassbarkeit und Definitionsresistenz führt Freud implizit darauf zurück, dass das Unheimliche als "abseits liegendes" Thema vom ästhetischen Fachdiskurs weitgehend vernachlässigt wurde. Dieser Vorwurf hat mittlerweile deutlich an Aktualität eingebüßt: Längst fehlt es nicht mehr an einschlägigen Abhandlungen, deren Faszination für das Thema sich allerdings nicht daraus speist, dass das Unheimliche inzwischen eine eindeutige Bestimmung erfahren hätte, sondern umgekehrt daraus, dass es so vielgestaltig und schwer zu fassen ist.
Global-scale assessments of freshwater fluxes and storages by hydrological models under historic climate conditions are subject to a variety of uncertainties. Using the global hydrological model WaterGAP 2.2, we investigated the sensitivity of simulated freshwater fluxes and water storage variations to five major sources of uncertainty: climate forcing, land cover input, model structure, consideration of human water use and calibration (or no calibration). In a modelling experiment, five variants of the standard version of WaterGAP 2.2 were generated that differed from the standard version only regarding the investigated source of uncertainty. Sensitivity was analyzed by comparing water fluxes and water storage variations computed by the variants to those of the standard version, considering both global averages and grid cell values for the time period 1971–2000. The basin-specific calibration approach for WaterGAP, which forces simulated mean annual river discharge to be equal to observed values at 1319 gauging stations (representing 54% of global land area except Antarctica and Greenland), has the highest effect on modelled water fluxes and leads to the best fit of modelled to observed monthly and seasonal river discharge. Alternative state-of-the-art climate forcings rank second regarding the impact on grid cell specific fluxes and water storage variations, and their impact is ubiquitous and stronger than that of alternative land cover inputs. The diverse model refinements during the last decade lead to an improved fit to observed discharge, and affect globally averaged fluxes and storage values (the latter mainly due to modelling of groundwater depletion) but only affect a relatively small number of grid cells. Considering human water use is important for the global water storage trend (in particular in the groundwater compartment) but impacts on water fluxes are rather local and only important where water use is high. The best fit to observed time series of monthly river discharge (Nash–Sutcliffe criterion) or discharge seasonality is obtained with the standard WaterGAP 2.2 model version which is calibrated and driven by a sequence of two time series of daily observation-based climate forcings, WFD/WFDEI. Discharge computed by a calibrated model version using monthly CRU 3.2 and GPCC v6 climate input reduced the fit to observed discharge for most stations. Taking into account the investigated uncertainties of climate and land cover data, we estimate that the global 1971–2000 discharge into oceans and inland sinks is between 40 000 and 42 000 km3 yr−1. The range is mainly due differences in precipitation data that affect discharge in uncalibrated river basins. Actual evapotranspiration, with approximately 70 000 km3 yr−1, is rather unaffected by climate and land cover in global sum but differs spatially. Human water use is calculated to reduce river discharge by approximately 1000 km3 yr−1. Thus, global renewable water resources are estimated to range between 41 000 and 43 000 km3 yr−1. The climate data sets WFD (available until 2001) and WFDEI (starting in 1979) were found to be inconsistent with respect to short wave radiation data, resulting in strongly different potential evapotranspiration. Global assessments of freshwater fluxes and storages would therefore benefit from the development of a global data set of consistent daily climate forcing from 1900 to current.
This paper investigates the value of observed river discharge data for global-scale hydrological modeling of a number of flow characteristics that are e.g. required for assessing water resources, flood risk and habitat alteration of aquatic ecosystems. An improved version of the WaterGAP Global Hydrology Model (WGHM) was tuned against measured discharge using either the 724-station dataset (V1) against which former model versions were tuned or an extended dataset (V2) of 1235 stations. WGHM is tuned by adjusting one model parameter (γ) that affects runoff generation from land areas in order to fit simulated and observed long-term average discharge at tuning stations. In basins where γ does not suffice to tune the model, two correction factors are applied successively: the areal correction factor corrects local runoff in a basin and the station correction factor adjusts discharge directly the gauge. Using station correction is unfavorable, as it makes discharge discontinuous at the gauge and inconsistent with runoff in the upstream basin. The study results are as follows. (1) Comparing V2 to V1, the global land area covered by tuning basins increases by 5% and the area where the model can be tuned by only adjusting γ increases by 8%. However, the area where a station correction factor (and not only an areal correction factor) has to be applied more than doubles. (2) The value of additional discharge information for representing the spatial distribution of long-term average discharge (and thus renewable water resources) with WGHM is high, particularly for river basins outside of the V1 tuning area and in regions where the refined dataset provides a significant subdivision of formerly extended tuning basins (average V2 basin size less than half the V1 basin size). If the additional discharge information were not used for tuning, simulated long-term average discharge would differ from the observed one by a factor of, on average, 1.8 in the formerly untuned basins and 1.3 in the subdivided basins. The benefits tend to be higher in semi-arid and snow-dominated regions where the model is less reliable than in humid areas and refined tuning compensates for uncertainties with regard to climate input data and for specific processes of the water cycle that cannot be represented yet by WGHM. Regarding other flow characteristics like low flow, inter-annual variability and seasonality, the deviation between simulated and observed values also decreases significantly, which, however, is mainly due to the better representation of average discharge but not of variability. (3) The choice of the optimal sub-basin size for tuning depends on the modeling purpose. While basins over 60 000 km2 are performing best, improvements in V2 model performance are strongest in small basins between 9000 and 20 000 km2, which is primarily related to a low level of V1 performance. Increasing the density of tuning stations provides a better spatial representation of discharge, but it also decreases model consistency, as almost half of the basins below 20 000 km2 require station correction.
Objectives In this early retrospective cohort study, a total of 26 patients with SARS-CoV-2 were treated with bamlanivimab or casirivimab/imdevimab, and the reduction of the viral load associated with the developed clinical symptoms was analyzed.
Methods: Patients in the intervention groups received bamlanivimab or casirivimab/imdevimab. Patients without treatment served as control. Outcomes were assessed by clinical symptoms and change in log viral load from baseline based on the cycle threshold over a period of 18 days.
Results: Median log viral load decline was higher in both intervention groups after 3 and 6 days compared to control. However, at later time points, the decline of the viral load was more distinct in the control group. Mild symptoms of COVID-19 were observed in 6.3% of the intervention groups and in no patient of the control. No patients treated with bamlanivimab, 18.8% treated with casirivimab/imdevimab, and 14.2% in the control group developed moderate symptoms. Severe symptoms were recorded only in the control group (14.2%), including one related death.
Conclusion: Treatment with monoclonal SARS-CoV-2 antibodies seems to accelerate decline of virus loads, especially in the first 6 days after administration, compared to control. This may be associated with a reduced likeliness of a severe course of COVID-19.
Writing a positive account of utopias has always been a difficult and risky task. Utopias have always already been out of fashion and outside of time. Since 1989 at the latest, visions of utopia appear to have come to an end. Twenty years after Fukayama's 'end of history', this article re-assesses the potentially fruitful roles for utopia’s out-of-timeness. Focusing on the critical potential of utopias through the concept of tension, it argues that utopian thought must be conceptualized through its tensile connections both to the status quo of a given society and to its possible futures.
Flow velocity in rivers has a major impact on residence time of water and thus on high and low water as well as on water quality. For global scale hydrological modeling only very limited information is available for simulating flow velocity. Based on the Manning-Strickler equation, a simple algorithm to model temporally and spatially variable flow velocity was developed with the objective of improving flow routing in the global hydrological model of Water- GAP. An extensive data set of flow velocity measurements in US rivers was used to test and to validate the algorithm before integrating it into WaterGAP. In this test, flow velocity was calculated based on measured discharge and compared to measured velocity. Results show that flow velocity can be modeled satisfactorily at selected river cross sections. It turned out that it is quite sensitive to river roughness, and the results can be optimized by tuning this parameter. After the validation of the approach, the tested flow velocity algorithm has been implemented into the WaterGAP model. A final validation of its effects on the model results is currently performed.
his paper investigates the value of observed river discharge data for global-scale hydrological modeling of a number of flow characteristics that are required for assessing water resources, flood risk and habitat alteration of aqueous ecosystems. An improved version of WGHM (WaterGAP Global Hydrology Model) was tuned in a way that simulated and observed long-term average river discharges at each station become equal, using either the 724-station dataset (V1) against which former model versions were tuned or a new dataset (V2) of 1235 stations and often longer time series. WGHM is tuned by adjusting one model parameter (γ) that affects runoff generation from land areas, and, where necessary, by applying one or two correction factors, which correct the total runoff in a sub-basin (areal correction factor) or the discharge at the station (station correction factor). The study results are as follows. (1) Comparing V2 to V1, the global land area covered by tuning basins increases by 5%, while the area where the model can be tuned by only adjusting γ increases by 8% (546 vs. 384 stations). However, the area where a station correction factor (and not only an areal correction factor) has to be applied more than doubles (389 vs. 93 basins), which is a strong drawback as use of a station correction factor makes discharge discontinuous at the gauge and inconsistent with runoff in the basin. (2) The value of additional discharge information for representing the spatial distribution of long-term average discharge (and thus renewable water resources) with WGHM is high, particularly for river basins outside of the V1 tuning area and for basins where the average sub-basin area has decreased by at least 50% in V2 as compared to V1. For these basins, simulated long-term average discharge would differ from the observed one by a factor of, on average, 1.8 and 1.3, respectively, if the additional discharge information were not used for tuning. The value tends to be higher in semi-arid and snow-dominated regions where hydrological models are less reliable than in humid areas. The deviation of the other simulated flow characteristics (e.g. low flow, inter-annual variability and seasonality) from the observed values also decreases significantly, but this is mainly due to the better representation of average discharge but not of variability. (3) The optimal sub-basin size for tuning depends on the modeling purpose. On the one hand, small basins between 9000 and 20 000 km2 show a much stronger improvement in model performance due to tuning than the larger basins, which is related to the lower model performance (with and without tuning), with basins over 60 000 km2 performing best. On the other hand, tuning of small basins decreases model consistency, as almost half of them require a station correction factor.