Although global pesticide use increases steadily, our field-data based knowledge regarding exposure of non-target ecosystems is very restricted. Consequently, this meta-analysis will for the first time evaluate the worldwide available peer-reviewed information on agricultural insecticide concentrations in surface water or sediment and test the following two hypotheses: I) Insecticide concentrations in the field largely exceed regulatory threshold levels and II) Additional factors important for threshold level exceedances can be quantified using retrospective meta-analysis. A feasibility study using a restricted dataset (n = 377) suggested the significance of the expected results, i.e. an threshold level exceedance rate of more than 50Prozent of the detected concentrations. Subsequent to a comprehensive database search in the peer-reviewed literature of the past 60 years, analysis of covariance with the relevant threshold level exceedance as the continuous dependent variable (about 10,000 cases) will be performed and the impact of significant predictor variables will be quantified. Parameters not yet considered in pesticide exposure assessment will be included as independent variables, such as compound class, environmental regulatory quality, and sampling design. The simultaneous presence of several insecticide compounds as a well as their metabolites will also be considered in the evaluation. The present approach may provide an innovative and integrated view on the potential environmental side effects of global high-intensity agriculture and in particular of pesticides use.
In hydrology, the relationship between water storage and flow is still fundamental in characterizing and modeling hydrological systems. However, this simplification neglects important aspects of the variability of the hydrological system, such as stable or instable states, tipping points, connectivity, etc. and influences the predictability of hydrological systems, both for extreme events as well as long-term changes. We still lack appropriate data to develop theory linking internal pattern dynamics and integral responses and therefore to identify functionally similar hydrological areas and link this to structural features. We plan to investigate the similarities and differences of the dynamic patterns of state variables and the integral response in replicas of distinct landscape units. A strategic and systematic monitoring network is planned in this project, which contributes the essential dynamic datasets to the research group to characterize EFUs and DFUs and thus significantly improving the usual approach of subdividing the landscape into static entities such as the traditional HRUs. The planned monitoring network is unique and highly innovative in its linkage of surface and subsurface observations and its spatial and temporal resolution and the centerpiece of CAOS.
The formation of biogeochemical interfaces in soils is controlled, among other factors, by the type of particle surfaces present and the assemblage of organic matter and mineral particles. Therefore, the formation and maturation of interfaces is studied with artificial soils which are produced in long-term biogeochemical laboratory incubation experiments (3, 6, 12, 18 months. Clay minerals, iron oxides and charcoal are used as major model components controlling the formation of interfaces because they exhibit high surface area and microporosity. Soil interface characteristics have been analyzed by several groups involved in the priority program for formation of organo-mineral interfaces, sorptive and thermal interface properties, microbial community structure and function. Already after 6 months of incubation, the artificial soils exhibited different properties in relation to their composition. A unique dataset evolves on the development and the dynamics of interfaces in soil in the different projects contributing to this experiment. An integrated analysis based on a conceptual model and multivariate statistics will help to understand overall processes leading to the biogeochemical properties of interfaces in soil, that are the basis for their functions in ecosystems. Therefore, we propose to establish an integrative project for the evaluation of data obtained and for publication of synergistic work, which will bring the results to a higher level of understanding.
Die Geosuche ist ein Webservice, welcher über die EGovernment-Basiskomponente Geodaten (GeoBAK) bereitgestellt wird. Die Geosuche ermöglicht eine multikriterielle Recherche nach ausgewählten Geobasisdaten und Geofachdaten, Geoinformationen (Metadaten) sowie Portalinhalten (Webseiten, Dokumente). Sie ist zentraler Bestandteil des Geoportals Sachsenatlas und als Freie Suche bzw. Volltextsuche ausgelegt. Die Umsetzung der Suche im Geoportal als singuläres Suchfeld (Omnibox, Einfeldsuche) analog zu bekannten Internetsuchmaschinen, ermöglicht einen schnellen Einstieg der Nutzer. Die Geosuche ermöglicht im Gegensatz zu standardisierten OGC-Geodatendiensten wie z.B. OGC-WFS-Gazetteer eine performanceoptimierte Recherche, welche nicht nur auf Geodaten beschränkt ist. Die Geosuche ermöglicht aufgrund der Filter- und Sortiermöglichkeiten die Umsetzung von über die Einfeldsuche hinausgehenden Recherchemöglichkeiten. Im Geoportal ist dies über die erweiterte Suche mit z.B. räumlicher und zeitlicher Auswahlmöglichkeit umgesetzt. Weiterhin sind einzelne Objekte untereinander verknüpft. Damit ist beispielsweise die Recherche nach allen Hausnummern einer Straße möglich (Drilldown). Die Umsetzung von Formularen mit Auswahllisten für eine Recherche, die die Geosuche aufrufen, ist möglich.
Objective: Integrated assessment and energy-economy models have become central tools for informing long-term global and regional climate mitigation strategies. There is a large demand for improved representations of complex system interactions and thorough validation of model behaviour in order to increase user confidence in climate policy assessments. ADVANCE aims to respond to this demand by facilitating the development of a new generation of integrated assessment models. This will be achieved by substantial progress in key areas where model improvements are greatly needed: end use and energy service demand; representation of heterogeneity, behaviour, innovation and consumer choices; technical change and uncertainty; system integration, path dependencies and resource constraints; and economic impacts of mitigation policies. In the past, methodological innovations and improvements were hindered by the unavailability of suitable input data. The ADVANCE project will make a large and coordinated effort to generate relevant datasets. These datasets, along with newly developed methodologies, will be made available to the broader scientific community as open-access resources. ADVANCE will also put a focus on improved model transparency, model validation, and data handling. A central objective of ADVANCE is to evaluate and to improve the suitability of models for climate policy impact assessments. The improved models will be applied to an assessment of long-term EU climate policy in a global context, and disseminated to the wider community. The ADVANCE consortium brings together long-standing expertise in integrated assessment and energy-economy modelling with a strong expertise in material flows, energy system integration, and energy service demand.
Farm households, whose living standard largely depend on the successful management of natural resources, have a low per capita income and are in danger of further impoverishment due to unsustainable resource management. Investigations in the first phase confirmed the hypothesis. A great number of farms were analyzed and clustered in representative types in both countries. Sustainability was measured using a sustainability index, which indicates tremendous environmental effects and variation between individual farms and ethnic groups.Sub-project G1.1 will follow three major tasks. The first is to evaluate sustainability strategies on the farm and farming system level, as it was done in the previous phase, but on the basis of a significantly extended data base. The second is to aggregate farm household data to the regional level. For this, a comparative-static approach is chosen. The third is to develop a multi-agent-based simulation model. Multi-agent simulation models (MAS) as well as GIS-tools are gaining increasing importance as tools for simulating future agriculture resource use, since they allow the integration of a wide range of different stakeholder's perceptions. It becomes possible to simulate the dynamic effects of changing land use patterns, environmental policy options, and technical innovation together with environmental constraints and structural change issues. The MAS approach is used to model heterogeneous farm-household and political decision makers perspectives by capturing their socio-economic, environmental, and spatial interactions explicitly. The integration of economic and spatial processes facilitates the consideration of feedback effects and the efficient use of scarce land resources. The simulation runs of the model will be carried out with a socio-economic and GIS data set, which is provided by the previous project phase in the attempt to generate effective ways of land use resource management. Land use efficiency is strongly influenced by the overall land allocation policy analyzed in project F1. Therefore, this is an important area further integrated research using MAS in combination with GIS as modeling tools.To achieve a continuous integration of results in the best possible way, a computer-based discussion/communication platform is developed. This serves as the conceptual basis for the development of the final multi-agent simulation model. Results of the discussion/communication platform and the agent-based simulation model will continuously be passed on to downstream sub-projects to be integrated into the ongoing research activities.
Current and future global warming will cause the degradation of mountain permafrost, which may strongly influence the stability of permafrost slopes or rock walls with potentially hazardous consequences. Due to the strong heterogeneity of both the thermal regime and the ground composition of mountain permafrost, its response to atmospheric forcing can however be highly variable for different landforms and within short distances. The spatial distribution of ice and liquid water is important for determining the sensitivity of a specific permafrost occurrence to climate change because of their large influence on the pace of temperature changes (by effects of latent heat) and their importance for geotechnical properties of the ground. Detailed knowledge of the material properties and internal structures of frozen ground is therefore an important prerequisite to determine the sensitivity of permafrost to climate change. Except for the active layer ice and water contents and their temporal and spatial variability usually cannot be measured directly. Geophysical methods are sensitive for the ice and liquid water content in the ground. With the proposed collaboration, two similar but complementary approaches to quantify the composition of the ground based on 2D sections of geophysical data will be combined for an improved determination of ice and water contents in permafrost regions. The so-called 4-phase model (4PM) is based on two simple petrophysical relationships for electrical resistivity and seismic velocity and estimates volumetric fractions of ice, water, and air within the pore volume of a rock matrix by jointly using complementary data sets from electric and seismic measurements. Due to inherent ambiguities in the model it is still restricted to specific cases and often allows only a rough estimation of the phase fractions. Major drawbacks of the current 4PM comprise the unsatisfactory discrimination between rock and ice and its under-determinedness, requiring the prescription of the porosity and further parameters. The so-called RSANN model (developed and used by the host institution) uses the technique of simulated annealing (a Monte-Carlo-type stochastic simulation approach) as an optimization tool for the integration of electrical resistivity and P-wave velocity to derive 2D sections of porosity, water saturation and volumetric water content. The simulated annealing technique allows - due to its iterative procedure - more parameters to be predicted instead of being prescribed as in the 4PM. The objective of the proposed collaboration is to combine the advantages of the two algorithms (4PM and RSANN) to overcome the shortcomings of the 4PM in order to improve the reliability of the determined ice and liquid water contents. (...)
An estimate of sediment transport rates in alluvial rivers is important in the context of erosion, sedimentation, flood control, long-term morphological assessment, etc. Extensive research during the last decades has produced a plethora of sediment transport models. Sediment transport is complex and often subject to semi-empirical or empirical treatment. Most of the sediment transport functions are based on simplified assumptions that the rate of sediment transport could be determined by one or two dominant factors, such as water discharge, average flow velocity, energy slope, and shear stress (Yang, 1996). In many practical situations prediction errors of these models are observed to be high.An alternative approach is to use data driven modelling, which is especially attractive for modelling processes about which adequate knowledge of the physics is limited, like in the case of sediment transport. Over the last decade fuzzy rule-based models have been introduced in engineering as a powerful alternative modelling tool. The fuzzy rule-based approach introduced by Zadeh (1965) is being widely utilized in various fields of engineering. It is a qualitative modelling scheme in which the system behaviour is described using a natural language (Sugeno & Yasukawa, 1993). This research focuses on the applicability of a data-driven fuzzy rule-based modelling approach in estimating sediment transport rates. It also aims at the comparison of the results of the fuzzy rule-based model with the results of other commonly utilized sediment transport functions.A number of variables play important roles in determining sediment transport capacity. These variables are: flow depth, particle fall velocity, particle diameter, flow velocity, energy or water surface slope, shear velocity, shear stress, fluid density, sediment density, stream power, unit stream power, and discharge. Additionally; size, shape, and unit weight of bed composition; morphology of bed forms and availability of sediment from source area affect sediment transport capacity. The most significant factors affecting sediment transport capacity will be identified and used for constructing a fuzzy model. The fuzzy model identification is usually carried out in two steps: (1) determining the number of fuzzy rules and their associated membership functions and (2) optimizing the fuzzy model. The fuzzy logic toolbox in MATLAB will be used for performing the fuzzy modelling.A general fuzzy system has the components of fuzzification, fuzzy rule base, fuzzy output engine, and defuzzification. Fuzzification converts each piece of input data to degrees of membership by a look-up in one or more several membership functions. Intuition, fuzzy clustering, neural networks, genetic algorithms, and inductive reasoning can be among many ways to assign membership values or functions to fuzzy variables...
Globalization raised the importance of food safety and quality concerns. Developed countries implement precautionary food regulation policies to protect their affluent consumers from unsafe food imported from developing and transition countries. However, the alarming number of trade disputes at WTO evidences cases of abuse of such policies. While claims on protectionist nature of food regulations are valid in principle, yet there is little empirical evidence about their economic effects. The questions of 1) quantification of trade impact of food standards and 2) investigation of national food regulation systems are absolutely essential for the new trade agenda. These problems for developing countries are on the focus of trade policy debate, whereas for transition countries are not considered seriously. Such a research for these recently liberalized markets gains a special significance. - The proposed research will employ Gravity Model for quantitative estimation of impact of EU aflatoxin standards on transition countries- exports.- Russian food regulations for cereal value chain, their enforcement and monitoring mechanisms will be investigated through value chain and cost-benefit analysis.- Compliance of Russian norms with EU standards will be estimated applying comparative advantage analysis.The study area is Stavropol region of the Russian Federation. Local experts will contribute to the construction of the research data set and analysis. The results of the research will assist 1) international policy makers in designing new global trade agenda and 2) Russian producers, exporters and decision makers in improving cereal value chain.
Satellite measurements strongly contribute to the understanding of the processes related to stratospheric ozone loss, e.g. by global and long term monitoring of ozone and its depleting substances. For instance, measurements performed in limb geometry by SCIAMACHY on ENVISAT largely improved the knowledge about the vertical distribution of species like BrO and OClO only recently. However, there are still important open questions, like e.g. the chlorine activation processes on different kinds of aerosols and polar stratospheric clouds. Also, the role of very short lived species in the stratospheric bromine budget or the effects of a possible enhancement of the Brewer-Dobson circulation are not fully understood.Globally, the vertical distribution of ozone depleting species varies significantly in space and time due to solar illumination, atmospheric chemistry and transport. Especially strong gradients occur near the twilight zone or across stratospheric transport barriers (polar vortex boundary, subtropical transport barriers). These regions are of particular importance for chemistry and transport of the lower stratosphere and upper troposphere, since they separate air masses on large scales but also enable exchange between them.Standard 1-D profile retrievals, which assume horizontal homogeneity, result in large systematic biases due to neglecting the effect of horizontal gradients on the measurement. We propose to develop, improve and apply a tomographic profile retrieval algorithm, which optimally combines the information provided by the SCIAMACHY limb and nadir measurements. An improved global dataset of 3D stratospheric profiles for NO2, BrO and OClO for the 10 years of the SCIAMACHY mission (2002-2012) will be developed, compared to atmospheric chemistry simulations and applied to selected questions of atmospheric science. The dataset developed in this project will be very useful for investigating the complex interplay of stratospheric chemistry and transport processes, and will help to reduce the uncertainties in the distribution of ozone depleting species, in particular for regions with large horizontal inhomogeneity.