Publications
NIBIOs employees contribute to several hundred scientific articles and research reports every year. You can browse or search in our collection which contains references and links to these publications as well as other research and dissemination activities. The collection is continously updated with new and historical material.
2016
Authors
Holger LangeAbstract
No abstract has been registered
Abstract
Convergent Cross Mapping (CCM) has recently been introduced by Sugihara et al. for the identification and quantification of causal relationships among ecosystem variables. In particular, the method allows to decide on the direction of causality; in some cases, the causality might be bidirectional, indicating a network structure. We extend this approach by introducing a method of surrogate data to obtain confidence intervals for CCM results. We then apply this method to time series from stream water chemistry. Specifically, we analyze a set of eight dissolved major ions from three different catchments belonging to the hydrological monitoring system at the Bramke valley in the Harz Mountains, Germany. Our results demonstrate the potentials and limits of CCM as a monitoring instrument in forestry and hydrology or as a tool to identify processes in ecosystem research. While some networks of causally linked ions can be associated with simple physical and chemical processes, other results illustrate peculiarities of the three studied catchments, which are explained in the context of their special history.
Authors
Sebastian Sippel Holger Lange Miguel D. Mahecha Michael Hauhs Paul Bodesheim Thomas W. Kaminski Fabian Gans Osvaldo A. RossoAbstract
Data analysis and model-data comparisons in the environmental sciences require diagnostic measures that quantify time series dynamics and structure, and are robust to noise in observational data. This paper investigates the temporal dynamics of environmental time series using measures quantifying their information content and complexity. The measures are used to classify natural processes on one hand, and to compare models with observations on the other. The present analysis focuses on the global carbon cycle as an area of research in which model-data integration and comparisons are key to improving our understanding of natural phenomena. We investigate the dynamics of observed and simulated time series of Gross Primary Productivity (GPP), a key variable in terrestrial ecosystems that quantifies ecosystem carbon uptake. However, the dynamics, patterns and magnitudes of GPP time series, both observed and simulated, vary substantially on different temporal and spatial scales. We demonstrate here that information content and complexity, or Information Theory Quantifiers (ITQ) for short, serve as robust and efficient data-analytical and model benchmarking tools for evaluating the temporal structure and dynamical properties of simulated or observed time series at various spatial scales. At continental scale, we compare GPP time series simulated with two models and an observations-based product. This analysis reveals qualitative differences between model evaluation based on ITQ compared to traditional model performance metrics, indicating that good model performance in terms of absolute or relative error does not imply that the dynamics of the observations is captured well. Furthermore, we show, using an ensemble of site-scale measurements obtained from the FLUXNET archive in the Mediterranean, that model-data or model-model mismatches as indicated by ITQ can be attributed to and interpreted as differences in the temporal structure of the respective ecological time series. At global scale, our understanding of C fluxes relies on the use of consistently applied land models. Here, we use ITQ to evaluate model structure: The measures are largely insensitive to climatic scenarios, land use and atmospheric gas concentrations used to drive them, but clearly separate the structure of 13 different land models taken from the CMIP5 archive and an observations-based product. In conclusion, diagnostic measures of this kind provide dataanalytical tools that distinguish different types of natural processes based solely on their dynamics, and are thus highly suitable for environmental science applications such as model structural diagnostics.
Authors
Holger LangeAbstract
No abstract has been registered
Authors
Sebastian Sippel Jakob Zscheischler Martin Heimann Holger Lange Miguel D. Mahecha Geert Jan van Oldenborgh Friederike E.L. Otto Markus ReichsteinAbstract
No abstract has been registered
Abstract
No abstract has been registered
Abstract
We investigate a set of long-term (several decades) time series for the runoff at river gauges at daily resolution. They are monitored by the Agencia Nacional de Aguas, and time series provided by the Operador Nacional do Sistema Elétrico, Brazil. A total of 150 time series was obtained, with an average length of 73 years. Both long-term trends as well as the influence of extreme events on the dynamical behaviour are analyzed. We use Horizontal Visibility Graphs (HVGs) to determine the individual temporal networks for the time series, and extract their degree distributions. Statistical and information-theoretic properties of these distributions are calculated: robust estimators of skewness and kurtosis, the maximum degree occurring in the time series, the Shannon entropy, permutation complexity and Fisher Information. For the latter, we also compare the information measures obtained from the degree distributions to those using the original time series directly, to investigate the impact of graph construction on the dynamical properties as reflected in these measures. Focus is on one hand on universal properties of the HVG, common to all runoff series, and on site-specific aspects on the other. We show that a specific pretreatment of the time series conventional in hydrology, the elimination of seasonality by a separate z-transformation for each calendar day, is highly detrimental to the nonlinear behaviour. It changes long-term correlations and the overall dynamic towards more random behaviour. Analysis based on the transformed data easily leads to spurious results, and bear a high risk of misinterpretation.
Abstract
Measuring energy and matter fluxes between the atmosphere and vegetation using the Eddy Covariance (EC) technique is the state-of-the-art method to quantify carbon exchange between terrestrial ecosystems and their surrounding. The EC equipment is usually mounted onto a flux tower reaching higher than the local canopy. Today, more than 600 flux towers are in operation worldwide. The methodological requirements lead to high sampling frequency (20 Hz) and thus to the production of very long time series. These are related to temperature, wind components, water vapour, heat and gas exchange, and others. In this chapter, the potential of Recurrence Analysis (RA) to investigate the dynamics of this atmosphere-vegetation boundary system is elucidated. In particular, the effect of temporal resolution, the identification of periods particular suitable for reliable EC flux calculations, and the detection of transitions between dynamical regimes will be highlighted.
Authors
Svein SolbergAbstract
Interferometric RADAR imagery can play an important role in REDD (Reduced Emissions from Deforestation and Forest Degradation). Interferometric RADAR acquires stereo imagery from which we derive height data. The RADAR heights are located high up in the tree crowns. Height above ground is correlated to forest biomass. Height decreases represent logging, i.e. reduced carbon stock. Height increases represent tree growth, i.e. increased carbon stock.
Abstract
No abstract has been registered