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.
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.
EAGER is a core group of international scientists trying to improve and harmonize national ammonia emission inventory calculations. Through the exchange they strive to continuously improve their respective N flux models. Six N-flow models used to calculate NH3 emissions from agriculture for inventories and policy implementation in UK, DK, NL, DE, CH were compared using standard activity data sets. For liquid manure the results were well comparable. For solid manure larger differences indicated greater uncertainties. In a third WP a survey on punlished and unpulished experimental data on emissions from solid manure was therfore conducted. Project goal: EAGER aims at achieving a detailed overview of the present best available inventory techniques, compiling and harmonizing the available knowledge on emission factors (EF) and initiating a new generation of NH3 emission inventories. Results: EAGER is a core group of international scientists trying to improve and harmonize national ammonia emission inventory calculations. Through the exchange they strive to continuously improve their respective N flux models
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...
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.
Accurate assessment of the aquatic impact of organic contaminants relies heavily on quantifying the principal degradation processes responsible for their removal. It is increasingly understood that oxidation processes play a significant and complicated role in the degradation of many contaminants in aquatic systems. The goals of the proposed project are threefold: 1. To assess three distinct computational approaches for their ability to accurate predict oxidation reaction barriers for a suite of organic compounds. These methods will be validated against existing databases of experimental measurements and high-level theoretical benchmarks. 2. To extend the existing database of measured rate constants describing organic contaminant oxidation by important oxidations in aquatic systems, for a limited number of test pollutants, using previously validated experimental protocols. 3. To further test the validity of the kinetic models for describing the rates of oxidations of organic contaminants in natural waters, using the combined data set of newly measured rate constants and both measured and newly computed oxidation barrier free energy data. In the proposed project, we will test both existing and newly developed quantum chemical models to predict the oxidation rates of several target contaminant families. The approaches developed here are expected to enable the future screening of other contaminants for their reactivity with oxidants in aquatic environments. This has important implications for the fate and impact of organic contaminants in natural surface waters, water and wastewater treatment facilities, and remediation efforts at highly contaminated sites.
Soil moisture - the water stored in soil within reach of the plants - is a crucial parameter for a large number of applications. Consequently, the field of microwave remote sensing of soil moisture has been an important research topic since the 1970s. But only in the last few years significant progress towards operational soil moisture services has been made. This progress became possible due to advances in sensor technology and new algorithmic approaches. With the improved algorithms it has been possible to derive soil moisture from existing operational microwave sensors. The first global soil moisture dataset derived from ERS-1/2 scatterometer measurements was released in 2002. The first near-real-time operational soil moisture service was started by EUMETSAT in May 2008 based on METOP ASCAT, which is the successor instrument of the ERS-1/2 scatterometer. Austria has made important contributions to these developments. The algorithms for retrieving soil moisture from the C-band scatterometers on board of ERS-1/2 and METOP have been developed by the Vienna University of Technology (TU Wien). Within EUMETSAT's Satellite Application Facility in Support to Operational Hydrology and Water Management (Hydrology SAF) the Austrian meteorological service (ZAMG) coordinates the soil moisture activities and is responsible for building up operational services for value-added METOP ASCAT soil moisture products. The overall goal of the proposed project is to advance the use of soil moisture services based on METOP ASCAT and complementary satellite systems, most importantly SMOS and ENVISAT ASAR, by extending the Hydrology SAF products to Africa and Australia, carrying out extensive calibration and validation (Cal/Val) activities and by developing novel water hazards applications. The considered applications are weather forecasting, drought and yield monitoring, hydrologic prediction, epidemiological modelling, climate change, desertification monitoring and societal risks assessment. A project of comparable thematic focus and breath has not been proposed before. It is expected that the interdisciplinary cooperation of specialists from different fields will lead to important scientific innovations that will promote a wide use of satellite technology in water hazards applications.
The goal of the proposal is to explore the structure and functioning of metacommunities in ecological compensation areas at a multi-trophic level. First, we will assess the effect of plant diversity and herbivore and/or predator exclusion on metacommunity functioning in sown wildflower strips. We will document the communities inhabiting these experimental plots, paying attention at interactions between species, and with consideration of larger consumers linking these habitats with the surrounding matrix. Second, we will explore the relationship between various measures of the environment (isolation, habitat size) and descriptors of the metacommunities (diversity, composition, abundances, and productivity of various taxonomic groups, food-web structure, temporal variability, local invasions and extinctions). Third, using a high-quality dataset on quantitative food webs and the present data, we will conduct meta-analyses to test various models of community organisation (neutral models of biodiversity, species-area relationship in trophic levels, regional similarity hypothesis, food-web structure). Fourth, we will develop various models describing food-web structure and metacommunities dynamics. We will synthesize our results to develop a theory of 'meta food-webs'. Fifth, we will apply the gained knowledge to improve current agri-environment schemes. The study of species interactions in spatially structured metacommunities is comprehensive and global. As such, this project has a strong potential to provide fundamental insight into conservation biology. This project is multidisciplinary, putting together practitioners, ecologists and mathematicians, and is expected to yield important results both of fundamental and conservation relevance. We will use various methodologies to reach our goals. For the first part, we will set up an experiment with replicated sown wildflower strips where plant species richness and the abundance of major predators (foxes and birds of prey) and/or of major herbivores (voles and slugs) will be controlled (balanced incomplete block design). The other parts will rely on classical meta-analyses, multivariate statistics, and mathematical modelling. For the latter part, we will develop stochastic models to explore the dynamics of communities.