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.
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.
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.
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.
The goal of this study was to enable a prognosis on the future rainfall conditions of the Nile Equatorial Lakes regions by delivering time-series of monthly rainfall sums for the time-period from 2021 to 2050 that can be used for all kinds of applications. One example might be the dimensioning of hydraulic structures. In these very long lasting investments, future climatic conditions have to be considered during present planning and construction.The principal sources of information on future climate conditions are General Circulation Models (GCMs). These are physically based atmospheric models that resemble a numerical weather prediction system but on a much coarser scale. This forecast cannot be perfect. Especially, it cannot predict single values, e. g. if January 2050 will be rather wet or dry, but only climatic references, i.e. state, if Januaries in general will become wetter or dryer in the future. Even if the predictions of a GCM were perfect, its output could not be used directly for hydrological purposes, due to its coarse resolution. The monthly precipitation values that are provided by the GCM present the spatially averaged precipitation over a grid cell of several thousand square kilometres. This 'block rainfall' can differ significantly from rainfall measured at the ground. Rain gauges are influenced by local effects like micro climatic conditions or orographic effects of mountain ranges that GCMs are not able to resolve.This study combined the information from different data sources. As global trend information, monthly precipitation values from two GCMs (ECHAM5 and HadCM3) were used. Three CO2-emission scenarios (A1b, A2 and B1) were considered in this data. As local ground reference observed monthly rainfall sums from several rain gauges in East Africa as well as from three reanalysis projects (Climate Research Unit, University of Delaware and GPCC) were used.At each rain gauge or observation point in the reanalysis a technique called 'Quantile-Quantile-Transformation' was applied to establish a relationship between the Cumulative Distribution Function (CDF) of the GCMs and that of the ground references during the calibration period from 1961-1990. The CDFs were fitted by non-parametric Kernel-Smoothing. To account for potential shifts in the annual cycles of GCMs and ground references, the transformations was done separately for each month.Assuming that the relation between Global Model and local response will be constant in the future, the global predictions of the GCM can be downscaled to local scale, leading to future rainfall scenarios that are coherent with observed past rainfall.Combining the data from three CO2-emission scenarios of two GCM with three reanalysis data sets, an ensemble of 18 different rainfall time-series was created for each observation point. The range of this ensemble helps to estimate the possible uncertainties in the prognosis of future monthly precipitation sums from 2021 to 2050.
This project had two main goals: i) to test and further develop the novel method of scanning reflectance spectroscopy in the visible spectrum (VIS-RS) and ii) to gain improved insight into Holocene climate, especially into phases with rapid climate change by applying this method to sediments from lakes in Northern Europe. In a first study, we could confirm the high potential of VIS-RS for inferring for example organic content of lake sediments. We could as well demonstrate the high potential of multivariate calibration techniques for this purpose. In a next step it should be tested to which extend more time consuming and costly sediment parameters can be inferred by means of VIS-RS. In a second study, we analysed a comprehensive data set from a pro-glacial lake in Western Norway and extracted a signal of Holocene glacier variations from this data set. Focusing on the 8.2 ka event a period characterised by rapid cooling and subsequent warming in the North Atlantic realm, we find that the decomposition of the glacier happened even faster than the glacier advance. In this study, we applied a multitude of statistical methods to i) compare sediment parameters among each other, ii) to extract signals common to all sediment parameters and iii) to transform uncertainty of age-depth models into uncertainties of glacial activity. We therefore employed methods that are widely applied in palaeoecology but that have not found their way into the field of geochemistry and sedimentology yet.
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.
| Organisation | Count |
|---|---|
| Bund | 62 |
| Europa | 13 |
| Land | 3 |
| Wissenschaft | 23 |
| Type | Count |
|---|---|
| Förderprogramm | 57 |
| unbekannt | 6 |
| License | Count |
|---|---|
| Offen | 62 |
| Unbekannt | 1 |
| Language | Count |
|---|---|
| Deutsch | 9 |
| Englisch | 56 |
| Resource type | Count |
|---|---|
| Keine | 48 |
| Webdienst | 5 |
| Webseite | 10 |
| Topic | Count |
|---|---|
| Boden | 55 |
| Lebewesen und Lebensräume | 62 |
| Luft | 57 |
| Mensch und Umwelt | 63 |
| Wasser | 53 |
| Weitere | 63 |