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  • 1
    Publication Date: 2023-01-14
    Description: Over the last decades, treatment of domestic wastewater promoted by environmental regulations have reduced human health risks and improved water quality. However, ecological risks caused by effluents of wastewater treatment plants (WWTPs) discharged into rivers still persist. Moreover, the evolution of these ecological risks in the future is intimately related to effects of changing climate, especially regarding streamflow in receiving rivers. Here, we present an analytical and transferable framework for assessing the ecological risks posed by WWTP‐effluents at the catchment scale. The framework combines the size‐class k of WWTPs, which is a load‐proxy, with their outflows' location in river networks, represented by stream‐order ω. We identify ecological risks by using three proxy indicators: the urban discharge fraction and the local‐scale concentrations of each total phosphorous and ammonium‐nitrogen discharged from WWTPs. About 3,200 WWTPs over three large catchments (Rhine, Elbe, and Weser) in Central Europe were analyzed by incorporating simulated streamflow for the most extreme projected climate change scenario. We found that WWTPs causing ecological risks in the future prevail in lower ω, across almost all k. Distinct patterns of ecological risks are identified in the k‐ω framework for different indicators and catchments. We show, as climate changes, intensified risks are especially expected in lower ω receiving effluents of intermediate‐k WWTPs. We discuss the implications of our findings for prioritizing WWTPs upgrading and urging updates on environmental regulations. Further discussions underline the feasibility of applying the framework to any geographical regions and highlight its potentials to help in achieving global long‐term commitments on freshwater security.
    Description: Key Points: An analytical, generic framework was developed to assess wastewater treatment plants causing ecological risks in rivers under climate change. Smaller streams will face higher ecological risks for almost all load classes of wastewater treatment plants in future climate. Of the legally regulated effluent parameters for treated wastewater, ammonium‐nitrogen concentration will pose the greatest ecological risk.
    Description: Helmholtz Association http://dx.doi.org/10.13039/501100009318
    Keywords: ddc:551.48 ; analytical assessment framework ; wastewater treatment plants emissions ; river stream‐order ; dilution ; local‐scale nutrients concentrations
    Language: English
    Type: doc-type:article
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  • 2
    Publication Date: 2021-07-03
    Description: Mixture of runoff generation processes poses a challenge for predicting upper flood quantiles. We examined transformations of generation processes from all identifiable runoff events to frequent and upper tail floods for a large set of mesoscale catchments and observed a substantial change of the dominant processes. Two trajectories of transformation were detected. In regions where floods occur almost exclusively in winter the dominance of processes related to snowmelt consistently increases from small events to frequent and upper tail floods. In catchments characterized by frequent winter‐spring floods and occasional summer‐autumn flood events triggered by rare meteorological phenomena (e.g., Vb cyclones), processes that dominate upper tails are not adequately represented in the sample of frequent floods. Predictions of extremes and projections of flood changes might remain highly uncertain in the latter cases.
    Description: Plain Language Summary: Prediction of floods remains a challenging task for the engineering practice. Floods triggered by different physical mechanisms have contrasting statistical attributes. Mixture of these processes hinders reliable prediction of the largest floods. In this study we classified a large number of streamflow events observed in a wide range of German river basins according to their generation processes. We analyzed changes in the frequency of occurrence of different generation processes, from the smallest identifiable runoff events to annual floods to rarer events corresponding to larger river flows. Interestingly, for some river basins certain processes tend to consistently increase their frequency from small streamflow events to common and larger floods. In other cases, we observed an opposite tendency. Certain processes become less important for the generation of annual floods compared to small streamflow events but then dominate generation of the largest floods. This has important implications for our ability to predict extreme floods and their possible changes.
    Description: Key Points: We analyzed transformation of processes from small runoff events to larger floods using a process‐based framework for event characterization. A substantial transformation of the frequency of processes from small runoff events to frequent and upper tail floods is observed. Differences in trajectories of process transformation among catchments suggest regionally variable predictability of extremes.
    Description: Deutsche Forschungsgemeinschaft (DFG) http://dx.doi.org/10.13039/501100001659
    Description: Helmholtz‐Zentrum für Umweltforschung (UFZ)
    Keywords: 551.489 ; flood origins ; event classification ; ordinary events ; annual floods ; upper tail floods ; process transformation
    Type: article
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  • 3
    Publication Date: 2021-10-14
    Description: This study proposes a new process-based framework to characterize and classify runoff events of various magnitudes occurring in a wide range of catchments. The framework uses dimensionless indicators that characterize space–time dynamics of precipitation events and their spatial interaction with antecedent catchment states, described as snow cover, distribution of frozen soils, and soil moisture content. A rigorous uncertainty analysis showed that the developed indicators are robust and regionally consistent. Relying on covariance- and ratio-based indicators leads to reduced classification uncertainty compared to commonly used (event-based) indicators based on absolute values of metrics such as duration, volume, and intensity of precipitation events. The event typology derived from the proposed framework is able to stratify events that exhibit distinct hydrograph dynamics even if streamflow is not directly used for classification. The derived typology is therefore able to capture first-order controls of event runoff response in a wide variety of catchments. Application of this typology to about 180,000 runoff events observed in 392 German catchments revealed six distinct regions with homogeneous event type frequency that match well regions with similar behavior in terms of runoff response identified in Germany. The detected seasonal pattern of event type occurrence is regionally consistent and agrees well with the seasonality of hydroclimatic conditions. The proposed framework can be a useful tool for comparative analyses of regional differences and similarities of runoff generation processes at catchment scale and their possible spatial and temporal evolution.
    Keywords: 551.48 ; event classification ; event type ; rainfall-runoff events ; event typology ; event characteristics ; runoff generation mechanisms
    Language: English
    Type: map
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  • 4
    Publication Date: 2011-08-16
    Keywords: GENERAL
    Type: Progr. of the X-15 Res. Airplane Program; p 75-84
    Format: text
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  • 5
    Publication Date: 2019-07-18
    Description: The Constellation-X mission, planned for launch in 2011, will feature an array of hard-x ray telescopes with a total collecting area goal of 1500 square centimeters at 40 keV. Various technologies are currently being investigated for the optics of these telescopes including multilayer-coated Eletroformed-Nickel-Replicated (ENR) shells. The attraction of the ENR process is that the resulting full-shell optics are inherently stable and offer the promise of good angular resolution and enhanced instrument sensitivity. The challenge for this process is to meet a relatively tight weight budget with a relatively dense material (rho nickel = 9 grams per cubic centimeters.) To demonstrate the viability of the ENR process we are fabricating a prototype HXT mirror module to be tested against a competing segmented-glass-shell optic. The ENR prototype will consist of 5 shells of diameters from 150 mm to 280 mm and of 426 mm total length. To meet the stringent weight budget for Con-X, the shells will be only 150 micron thick. The innermost of these will be coated with Iridium, while the remainder will be coated with graded-density multilayers. Mandrels for these shells are currently under fabrication (Jan 03), with the first shells scheduled for production in February 03. A tentative date of late Summer has been set for prototype testing. Issues currently being addressed are the control of stresses in the multiplayer coating and ways of mitigating their effects on the figure of the necessarily thin shells. Also, the fabrication, handling and mounting of these shells without inducing permanent figure distortions. A full status report on the prototype optic will be presented along with test results as available.
    Keywords: Astronomy
    Type: Optics for EUV, X-Ray and Gamma-Ray Astronomy; Aug 03, 2003 - Aug 08, 2003; San Diego, CA; United States
    Format: text
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  • 6
    Publication Date: 2019-07-13
    Description: The Agricultural Model Intercomparison and Improvement Project (AgMIP) is a major international effort linking the climate, crop, and economic modeling communities with cutting-edge information technology to produce improved crop and economic models and the next generation of climate impact projections for the agricultural sector. The goals of AgMIP are to improve substantially the characterization of world food security due to climate change and to enhance adaptation capacity in both developing and developed countries. Analyses of the agricultural impacts of climate variability and change require a transdisciplinary effort to consistently link state-of-the-art climate scenarios to crop and economic models. Crop model outputs are aggregated as inputs to regional and global economic models to determine regional vulnerabilities, changes in comparative advantage, price effects, and potential adaptation strategies in the agricultural sector. Climate, Crop Modeling, Economics, and Information Technology Team Protocols are presented to guide coordinated climate, crop modeling, economics, and information technology research activities around the world, along with AgMIP Cross-Cutting Themes that address uncertainty, aggregation and scaling, and the development of Representative Agricultural Pathways (RAPs) to enable testing of climate change adaptations in the context of other regional and global trends. The organization of research activities by geographic region and specific crops is described, along with project milestones. Pilot results demonstrate AgMIP's role in assessing climate impacts with explicit representation of uncertainties in climate scenarios and simulations using crop and economic models. An intercomparison of wheat model simulations near Obregn, Mexico reveals inter-model differences in yield sensitivity to [CO2] with model uncertainty holding approximately steady as concentrations rise, while uncertainty related to choice of crop model increases with rising temperatures. Wheat model simulations with midcentury climate scenarios project a slight decline in absolute yields that is more sensitive to selection of crop model than to global climate model, emissions scenario, or climate scenario downscaling method. A comparison of regional and national-scale economic simulations finds a large sensitivity of projected yield changes to the simulations' resolved scales. Finally, a global economic model intercomparison example demonstrates that improvements in the understanding of agriculture futures arise from integration of the range of uncertainty in crop, climate, and economic modeling results in multi-model assessments.
    Keywords: Meteorology and Climatology
    Type: GSFC-E-DAA-TN8896 , Agricultural and Forest Meteorology; 170; 166-182
    Format: application/pdf
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  • 7
    Publication Date: 2019-07-13
    Description: Potential consequences of climate change on crop production can be studied using mechanistic crop simulation models. While a broad variety of maize simulation models exist, it is not known whether different models diverge on grain yield responses to changes in climatic factors, or whether they agree in their general trends related to phenology, growth, and yield. With the goal of analyzing the sensitivity of simulated yields to changes in temperature and atmospheric carbon dioxide concentrations [CO2], we present the largest maize crop model intercomparison to date, including 23 different models. These models were evaluated for four locations representing a wide range of maize production conditions in the world: Lusignan (France), Ames (USA), Rio Verde (Brazil) and Morogoro (Tanzania). While individual models differed considerably in absolute yield simulation at the four sites, an ensemble of a minimum number of models was able to simulate absolute yields accurately at the four sites even with low data for calibration, thus suggesting that using an ensemble of models has merit. Temperature increase had strong negative influence on modeled yield response of roughly -0.5 Mg ha(sup 1) per degC. Doubling [CO2] from 360 to 720 lmol mol 1 increased grain yield by 7.5% on average across models and the sites. That would therefore make temperature the main factor altering maize yields at the end of this century. Furthermore, there was a large uncertainty in the yield response to [CO2] among models. Model responses to temperature and [CO2] did not differ whether models were simulated with low calibration information or, simulated with high level of calibration information.
    Keywords: Meteorology and Climatology
    Type: GSFC-E-DAA-TN14222 , Global Change Biology; 20; 7; 2301-2320
    Format: application/pdf
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  • 8
    Publication Date: 2019-07-13
    Description: Field-scale crop models are increasingly applied at spatio-temporal scales that range from regions to the globe and from decades up to 100 years. Sufficiently detailed data to capture the prevailing spatio-temporal heterogeneity in weather, soil, and management conditions as needed by crop models are rarely available. Effective sampling may overcome the problem of missing data but has rarely been investigated. In this study the effect of sampling weather data has been evaluated for simulating yields of winter wheat in a region in Germany over a 30-year period (1982-2011) using 12 process-based crop models. A stratified sampling was applied to compare the effect of different sizes of spatially sampled weather data (10, 30, 50, 100, 500, 1000 and full coverage of 34,078 sampling points) on simulated wheat yields. Stratified sampling was further compared with random sampling. Possible interactions between sample size and crop model were evaluated. The results showed differences in simulated yields among crop models but all models reproduced well the pattern of the stratification. Importantly, the regional mean of simulated yields based on full coverage could already be reproduced by a small sample of 10 points. This was also true for reproducing the temporal variability in simulated yields but more sampling points (about 100) were required to accurately reproduce spatial yield variability. The number of sampling points can be smaller when a stratified sampling is applied as compared to a random sampling. However, differences between crop models were observed including some interaction between the effect of sampling on simulated yields and the model used. We concluded that stratified sampling can considerably reduce the number of required simulations. But, differences between crop models must be considered as the choice for a specific model can have larger effects on simulated yields than the sampling strategy. Assessing the impact of sampling soil and crop management data for regional simulations of crop yields is still needed.
    Keywords: Meteorology and Climatology
    Type: GSFC-E-DAA-TN30834 , Agricultural and Forest Meteorology; 220; 101-115
    Format: text
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  • 9
    Publication Date: 2019-07-13
    Description: Crop models of crop growth are increasingly used to quantify the impact of global changes due to climate or crop management. Therefore, accuracy of simulation results is a major concern. Studies with ensembles of crop model scan give valuable information about model accuracy and uncertainty, but such studies are difficult to organize and have only recently begun. We report on the largest ensemble study to date, of 27 wheat models tested in four contrasting locations for their accuracy in simulating multiple crop growth and yield variables. The relative error averaged over models was 2438 for the different end-of-season variables including grain yield (GY) and grain protein concentration (GPC). There was little relation between error of a model for GY or GPC and error for in-season variables. Thus, most models did not arrive at accurate simulations of GY and GPC by accurately simulating preceding growth dynamics. Ensemble simulations, taking either the mean (e-mean) or median (e-median) of simulated values, gave better estimates than any individual model when all variables were considered. Compared to individual models, e-median ranked first in simulating measured GY and third in GPC. The error of e-mean and e-median declined with an increasing number of ensemble members, with little decrease beyond 10 models. We conclude that multimodel ensembles can be used to create new estimators with improved accuracy and consistency in simulating growth dynamics. We argue that these results are applicable to other crop species, and hypothesize that they apply more generally to ecological system models.
    Keywords: Meteorology and Climatology; Earth Resources and Remote Sensing
    Type: GSFC-E-DAA-TN28990 , Global Change Biology; 21; 2; 911-925
    Format: text
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  • 10
    Publication Date: 2019-07-13
    Description: AgMIP (www.agmip.org) is an international community of climate, crop, livestock, economics, and IT experts working to further the development and application of multi-model, multi-scale, multi-disciplinary agricultural models that can inform policy and decision makers around the world. This meeting will engage the AGU community by providing a brief overview of AgMIP, in particular its new plans for a Coordinated Global and Regional Assessment of climate change impacts on agriculture and food security for AR6. This Town Hall will help identify opportunities for participants to become involved in AgMIP and its 30+ activities.
    Keywords: Meteorology and Climatology; Earth Resources and Remote Sensing
    Type: GSFC-E-DAA-TN28978 , AGU Fall Meeting 2015; Dec 14, 2015 - Dec 18, 2015; San Francisco, CA; United States
    Format: application/pdf
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