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.
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.
Die südecuadorianischen Anden beherbergen eine außergewöhnlich hohe Artenvielfalt. Viele verschiedene Umweltfaktoren beeinflussen sich auf sehr limitiertem Raum und erschaffen so einzigartige und komplexe Ökosysteme. Dieses Gebiet ist jedoch auf Grund des zunehmenden menschlichen Einflusses durch die fortschreitende Intensivierung der Landnutzung und des globalen Wandels hochgefährdet. Wir wissen nur wenig über die paläoökologische Geschichte und Landschaftsdynamik dieses Gebiets. Die Information über das warum und wie einer Veränderung von Ökosystemen ist unerlässlich für die Entwicklung innovativer Strategien für Naturschutz und im Hinblick auf zukünftige Klimaveränderungen. In der vorliegenden Studie werden palynologische Analysen aus den südecuadorianischen Anden vorgestellt, die dazu beitragen, Muster und Prozesse heutiger und vergangener Ökosysteme zu beleuchten. Eine paläoökologische Studie des Quimsacocha-Vulkanbeckens auf der östlichen Erhebung der Westkordillere der südecuadorianischen Anden deckt Klima-, Vegetations- und Brandregimeveränderungen in dieser Region seit dem frühen Holozän auf. Das mittlere Holozän war eine Zeit starker Umweltveränderungen, verursacht durch ein trockenes und wohl wärmeres Klima. Während des späten Holozäns wechselten sich mehrere Kalt-und Warmphasen ab. Brände können seit dem frühen Holozän im Gebiet verzeichnet werden. Sie könnten ein erstes Zeichen menschlichen Einflusses darstellen. Mit anderen paläoökologischen Aufzeichnungen aus den südecuadorianischen Anden verglichene multivariate Analysen decken teilweise konstrastierende Entwicklungen an den verschieden Standorten auf, die vermutlich durch die Heterogenität der Umweltfaktoren zu erklären sind. Weiterhin wurden Studien zum Verhältnis von heutigem Pollenregen mit der Vegetation in der Podocarpus Nationalpark-Region durchgeführt, um die Pollenverbreitungsmuster innerhalb der verschiedenen Vegetationstypen, prämontaner Wald, unterer Bergwald, oberer Bergwald und Páramo, zu verstehen und damit eine bessere Grundlage zur Interpretation fossiler Pollendaten zu schaffen. Ein Vergleich von Abundanz und An-/Abwesenheitsdaten von Familien als taxonomischer Einheit für Pollen- und Vegetation zeigt, dass Diversität, Verbreitung und Häufigkeiten beider Datensätze gut miteinander in Verbindung gebracht werden können. Dennoch werden die Muster durch variierende Anteile von durch Ferntransport eingetragenen Pollenkörnern sowie durch unterschiedliche Pollenproduktivität verschiedener Taxa und heterogene Windsysteme beeinflusst. Analysen der Pollenakkumulationsraten, die über drei Jahre erfasst wurden, lassen auf eine geringe inter-annuelle aber hohe räumliche Variation in den Daten schließen. (Text gekürzt)
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.
The Earth's climate is changing as a response to anthropogenic emissions of fossil fuels, and is very likely to do so over the next decades to centuries. The projected changes from multiple numerical climate models differ significantly, because some feedbacks and processes are poorly understood or cannot be resolved in the models. Yet a quantitative picture of the uncertainty associated with the expected changes on regional and global scales is crucial, in order to quantify impacts, and decide on adaptation and mitigation measures. The objective of this project is to assess the uncertainty in large-scale regional to global climate change projections based on the CMIP3 dataset, a collection of simulations of about twenty global climate models coordinated for the recent IPCC report. While the amount of data produced by climate models and the desire for ever more detailed projections and probabilistic information is growing rapidly, there is a lack of understanding on how to define the skill of a climate model for a forecast that in practice cannot be verified. So far, models are often averaged regardless of their ability to satisfy even basic requirements, and the community is reluctant to define a set of metrics that would seem important for a model to be credible. Also, a consensus of uncertainty in climate change projections on regional to global scales is missing. We propose to develop a framework to evaluate climate models for their suitability in predicting changes by a set of metrics that are important to be simulated correctly from a physical point of view, rather than comparing present day simulations to observations. In addition, by treating one model M as observations, statistical methods can be used to find optimal predictors and to define model weight, such that a weighted mean or a probabilistic method using the rest of the models can best predict M. The issue of common model error and model dependence will be addressed. We also propose novel methods to aggregate results in regional averages using clustering techniques, and a more flexible approach to communicating results using climate change indices. As milestones of the project, we will attempt to answer the following questions: How large are the uncertainties of model projections on different spatial and temporal scales? On what spatial scale can the current models provide robust information? How can we quantify model performance for projections, and use that to define model weight? Is a small set of 'good' models more useful than a large number of 'good' and 'bad' models? How do model dependence, common biases, and overconfidence affect projection uncertainties? How can we attach confidence to climate change indices? The proposed work combines efforts in climate research and statistics. The results will be used in improving a Bayesian hierarchical model to generate probabilistic climate change information for the spatial scales on which .
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.
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.
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.
The HGF Alliance 'Remote Sensing and Earth System Dynamics' aims at the development and evaluation of novel bio/geo-physical information products derived from data acquired by a new generation of remote sensing satellites; and their integration in Earth system models for improving understanding and modelling ability of global environmental processes and ecosystem change. The Earth system comprises a multitude of processes that are intimately meshed through complex interactions. In times of accelerated global change, the understanding and quantification of these processes is of primary importance. Spaceborne remote sensing sensors are predestined to produce bio-geo-information products on a global scale. The upcoming generation of spaceborne remote sensing configurations will be able to provide global data sets and products with unprecedented spatial and temporal resolution in the context of a consistent and systematic observation strategy. The integration of these data sets in existing environmental and climate science components will allow a new global view of the Earth system and its dynamics, initiating a performance leap in ecosystem and climate change modelling.
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