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.
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.
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.
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.
Understanding the drivers behind the loss of biodiversity currently observed is of major importance in the context of the global change discussion. For studies of biodiversity at broad spatial scales, satellite remote sensing is the premier source of information, as it is uniquely capable of covering large areas of the Earth at high temporal resolution. The underlying assumption is that satellite-derived geophysical surface parameters, such as vegetation greenness, are related to biodiversity. For that purpose, concepts such as the Dynamic Habitat Index (DHI) were recently developed. The DHI combines information on the overall greenness, the base level of vegetation cover, and vegetation seasonality at a certain location. By comparing the annual DHI with a long term mean, areas undergoing disturbances or recovery events can be delineated, which are indicative of changes in species composition and diversity. The concept of the DHI was developed for data obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) post-2000. However, for climate change impact studies a longer time series is desirable. The only sensor system suitable for such applications is the Advanced Very High Resolution Radiometer (AVHRR), which has been in orbit on various platforms since the early 1980ies. The purpose of this project was to develop a methodology to derive long term information on habitat conditions at continental scales based on historical satellite data. In particular, the goal was to adopt the principle of the DHI as developed for the MODIS sensor to an AVHRR archive at 1 km spatial resolution over Canada and to analyze long term variations in the DHI. In a first project phase, an AVHRR data set of vegetation greenness, generated in the framework of the project, was validated against the reference MODIS product. The results demonstrated a very good agreement between both data sets for a wide range of vegetation types and on various spatial and temporal scales. A historical baseline of habitat conditions post-1987 based on the DHI was subsequently generated based on the long term AVHRR data. The analysis of the DHI showed that certain areas, particularly northern parts of the Province of Quebec as well as southwestern Canada, experienced significant changes over the past two decades, which may have had significant impacts on species diversity and abundance in these areas. In the future, the methods developed in the framework of this project may be used to obtain information on long term variations in habitat conditions in areas covered by other historical satellite archives, e.g., for Europe based on an AVHRR archive hosted at the University of Bern.
Das räumlich explizite klimasensitive 3D-Waldökosystemmodell PICUS wurde kürzlich durch ein biogeochemisches Bodenmodul (TRACE) zur Simulation von C- und N-Kreisläufen ergänzt und steht derzeit in einer anhand von Literaturdaten und Expertenwissen parametrisierten Version für Szenarioanalysen zur Verfügung. Ziel des gegenständlichen Projektes ist es, das ergänzte Modell PICUS v1.41 anhand von Daten von Dauerversuchsflächen des BFW (unbehandelte Parzellen von Düngungsversuchen mit Beobachtungszeiträumen von bis zu 35 Jahren) zu validieren um es in einer überprüften und zuverlässigen Version für die Analyse von Konzepten zur nachhaltigen Waldbewirtschaftung zur Verfügung zu stellen. Für die hier beschriebenen Experimente konnten die Versuchsflächen Grottenhof, Helfenberg und Karlstift verwendet werden. Insgesamt kann festgestellt werden, dass die letztendliche Bereitstellung der Vergleichswerte für C und N Pools für die Versuchsflächen zahlreiche Probleme aufwirft, deren Lösung meist mit zusätzlicher Unsicherheit in den Vergleichswerten verbunden ist. Die Ergebnisse der Vergleiche von simulierten und beobachteten Systemgrößen waren für die oberirdische Biomasseentwicklung (Bestandesparameter) i.A. sehr zufriedenstellend. Bei den Boden-Pools für C und N konnte in den meisten Fällen der allgemeine Entwicklungstrend reproduziert werden. Details (Form der Ab- bzw. Zunahme über die Beobachtungsperiode, absolute Größenordnung der Veränderungen in den Poolgrößen) aber von PICUS nicht immer zufriedenstellend simuliert werden konnte. Grund dafür ist vor allem, dass kurzfristige Trendumkehren in C und N Pools von Bodenmodellen aufgrund deren Konzeption i.A. nicht simuliert werden können, soferne keine exogenen Faktoren den dafür benötigten Impuls liefern. Dies kann zum Beispiel durch Streuinput oder durch Veränderungen in der N-Deposition bewerkstelligt werden. Berücksichtigt man die generische Initialisierung und Parameterisierung von PICUS v1.41, dann sind die Ergebnisse als vielversprechend zu bezeichnen. Bei standorts- und parzellenspezifischen Kalibrierungsschritten ist eine noch bessere Anpassung der simulierten and die beobachteten C und N Pools zu erwarten. Damit einhergehen würde allerdings die Möglichkeit, PICUS v1.41 für großflächige regionale und nationale Simulationsstudien einzusetzen. Als Folgerung aus diesen Erkenntnissen wird demnächst versucht werden, den Initialisierungansatz für Erhebungspunkte der Waldinventur weiter zu verbessern.
Subproject within the DFG research unit 816: Biodiversity and Sustainable Management Of a Megadiverse Mountain Ecosystem in South Ecuador The main aim of the project is to unveil the impacts of climate and land use change on the regional climate of the ecosystem platform, to examine effects of climate change on biodiversity for selected organismic groups by testing two different approaches, to investigate atmospheric nutrient deposition from remote sources in the framework of the NUMEX experiment as well as its future development under environmental change, and to support the research unit by providing data on vegetation activity based on remotely sensed data. Subject 1 encompasses an in-depth analysis of weather situations with an anomalous zonal overturning Walker circulation (El Niño/La Niña events) by means of a comprehensive data set gathered during previous studies. Additionally, a coupled model suite of a regional climate (WRF) and a SVAT model (CLM) will be used to conduct simulation runs for the joint scenarios of land use and global climate change. Subject 2 uses downscaled temperature data for the climate change scenarios to test effects on biodiversity with the species-area approach and the energetic-equivalence rule for moths, soil mites and trees. Subject 3 observes fog- and rain-water deposition including a back-trajectory modelling encompassing. Remotely sensed products of atmospheric chemistry and future climate/emission scenario runs are applied to disentangle present-day and future atmospheric fertilization of the mountain forest and its remote sources. Subject 4 makes vegetation products (NDVI, LAI, GPP) of different sensors available to the research unit.
Forest biomass information at regional to global scales are essential data for a variety of applications such as forest inventories or dynamic vegetation models. Space-borne synthetic aperture radar (SAR) imagery can support the estimation of forest parameters because of the direct sensitivity of the backscattered signal to forest structure and SAR independency from sun light and weather conditions. The availability of repeated SAR observations within a short time period furthermore allows refining estimates of a forest parameter with respect to a single image estimate. To exploit the potential offered by multi-temporal SAR data to retrieve forest parameters, the BIOMASAR algorithm was set up. The BIOMASAR algorithm retrieves forest growing stock volume (GSV, unit: m3/ha) with a fully automated approach. This website provides maps of GSV derived with the BIOMASAR algorithm from several spaceborne SAR datasets to interested users of the scientific community. The datasets are free of charge, with the aim to encourage further research in the field of forest parameters retrieval and investigations that require spatially explicit information on forest parameters.
The major aim of CARCINOGENOMICS is to develop in vitro methods for assessing the carcinogenic potential of compounds, as an alternative to current rodent bioassays for genotoxicity and carcinogenicity. The major goal is to develop a battery of mechanism-based in vitro tests accounting for various modes of carcinogenic action. These tests will be designed to cover major target organs for carcinogenic action e.g. the liver, the lung, and the kidney. The novel assays will be based on the application of 'omics' technologies (i.e. genome-wide transcriptomics as well as metabonomics) to robust in vitro systems (rat/human), thereby also exploring stem cell technology, to generate 'omic' responses from a well-defined set of model compounds causing genotoxicity and carcinogenicity. Phenotypic markers for genotoxic and carcinogenic events will be assessed for the purpose of anchoring gene expression modulations, metabolic profiles and mechanism pathways. Through extensive biostatistics, literature mining, and analysis of molecular-expression datasets, differential genetic pathways will be identified capable of predicting mechanisms of chemical carcinogenesis in vivo. Furthermore, generated transcriptomic and metabonomic data will be integrated into a holistic understanding of systems biology, and applied to build an iterative in silico model of chemical carcinogenesis. Subsequently, predictive gene expression profiles, typically consisting of some 150-250 genes, will be loaded onto high throughput dedicated DNA-chips, thus accelerating the analysis of transcriptomic responses by a factor of 100. It is expected that the outcome of this project will generate a platform enabling the investigation of large numbers of compounds for their genotoxic and carcinogenic potential, as envisaged under the REACH initiative. This will contribute to speeding the identification of potential harmful substances to man, while lowering costs and reducing animal tests. Prime Contractor: Maastricht, University, Health Risk Analysis and Toxicology (Grat); Maastricht, Nederland.
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