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.
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.
Both lakes and oceans are important for the global carbon cycle and thus the regulation of climate processes. Due to climate change and human activities, aquatic systems are subject to increasing pressure with changes already observed at multiple levels affecting their functioning. It is therefore urgent to understand the dynamic of aquatic systems, if one wants to predict their response to changing conditions. Phytoplankton, act as engineers, initiating the incorporation of terrestrial and atmospheric compounds into the food chain and driving their biogeochemical cycling. They not only respond rapidly to their environment, they also profoundly alter aquatic chemistry, affecting the reactivity, recycling, remineralisation and therefore fate of many elements. As such, phytoplankton affect the dynamics of aquatic systems with effects at both local and global scales. Phytoplankton can thus be used as sentinel to assess the dynamics and changes in aquatic systems. One of the most prominent reported controls of phytoplankton biomass, biodiversity and productivity is nutrient limitation, reported in most of the ocean and numerous lakes. Iron (Fe), nitrogen (N) and phosphorous (P) are the main limiting nutrients in aquatic systems. Nutrient limitation affects the functioning of aquatic systems and their contribution to the global carbon cycle. Despite numerous studies, the parameters controlling nutrient limitation and their accessibility to phytoplankton (viz. bioavailability) remain largely unknown. The aim of this project is to identify nutrient (Fe, N, P) limitation in different aquatic systems, and to improve our understanding of aquatic biogeochemistry - from gene expression, chemistry and bioavailability through to the impact on biodiversity under current and future conditions. The study regions include the largest lake in Western Europe, Lake Geneva; the Southern Ocean, a pivotal region for the global carbon cycle; and the Tasman Sea, one of the most sensitive regions to predicted climate change. All these regions are associated with significant socio-economical value. Here, a rigorous multi-disciplinary laboratory and field approach will be used to provide complementary data sets to shed light on how nutrients affect the biodiversity, the biogeochemical cycles of key elements and the functioning of natural systems. The laboratory approach (1) explore the mechanisms controlling nutrient biological accessibility using relevant axenic phytoplankton cultures and (2) allows the calibration and validation of biological and chemical sensors to rapidly monitor nutrient limitation in aquatic systems. In addition, field work will (1) explore the link and the seasonality between important physical, biological and chemical parameters and (2) use perturbation experiments to investigate the complexity of the link between nutrients and natural planktonic assemblages. (...)
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 .
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.
The investigation of high-silica rhyolitic rocks collected in the recent ICDP drilling from the Snake River Plain (SRP) volcanic province (western United States) as well as rocks from the adjacent rhyolitic complexes offers a unique opportunity to track the evolution of magma storage conditions in time and space in the 'Yellowstone hotspot' intracontinental volcanic province. The application of various geothermometers which can be used to determine pre-eruptive temperatures show a general trend indicating a general decrease of temperature over the last 16 Ma. However, the depth (or pressure) of the magma chambers is difficult to constrain and remains mainly unknown because the mineral assemblage in the rhyolitic systems is not suitable for geobarometry. As an alternative to mineral compositions, the silica content of rhyolitic melts can be used to constrain pressure, provided that the silicate melts have cotectic compositions (melts coexisting with quartz and feldspar), which is the case for most SRP rhyolites. From studies in synthetic systems, it is well known that the silica content of cotectic melts decreases with increasing pressure and that it may be used as barometer in pressure ranges of ca 1000 - 50 MPa. However, the evolution of silica content with pressure is not calibrated for natural systems containing up to 2 wtProzent Cao and 4 wtProzent FeO. In this study, we plan to determine the role of pressure on the silica content of cotectic melts compositions relevant for SRP compositions. The experimental data are crucial to interpret the natural glass compositions (matrix glass and glass inclusions) analyzed in the ICDP core samples and will be used to extract quantitative information on the depth of magma storage prior to eruption. The dataset obtained from various eruptive events (samples from ICDP drillings and other SRP rhyolites) will be used to check if there is an evolution of the depth of magma storage over the lifetime of the 'Yellowstone hotspot' in the last 16 Ma and if there is a correlation between the pre-eruptive pressure, the volume of erupted material, the temperature (or differentiation level) and the water activity of magmas. This study will be conducted in close cooperation with other U.S. groups who are in charge of the analysis of ICDP rhyolitic samples. It is emphasized that the experimental database obtained in this project can also be applied to other case studies (high silica rhyolites, A-type granites).
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