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.
2018
Abstract
No abstract has been registered
Authors
Marleen Pallandt Bernhard Ahrend Sönke Zaehle Marion Schrumpf Holger Lange Markus ReichsteinAbstract
Soil organic carbon (SOC) is the largest terrestrial carbon pool. Changes in the hydrological cycle affect C-cycle turnover, with potential effects on the global C balance’s response to global change. However, large scale model representations of the sensitivity of soil carbon to soil moisture, through decomposition and interactions with nutrient cycles, are largely empirical to semi-empirical and uncertain. To better represent these dynamics, the aims of this PhD project* are to: • Investigate the role of soil moisture on SOC decomposition over a vertical profile; • Assess which moisture controls are (most) important in a multi-layered, mechanistic soil biogeochemistry model, the Jena Soil Model (JSM, Fig 2); • Update and improve the representations of soil moisture dynamics in JSM and evaluate this model for multiple sites along a moisture gradient and global scale
Abstract
Horizontal Visibility Graphs (HVGs) are a recently developed method to construct networks from time series. The values of the time series are considered as the nodes of the network and are linked to each other if there is no larger value between them, such as they can “see” each other. The network properties reflect the nonlinear dynamics of the time series. For some classes of stochastic processes and for periodic time series, analytical results can be obtained for network-derived quantities such as the degree distribution, the local clustering coefficient distribution, the mean path length, and others. HVGs have the potential to discern between deterministic-chaotic and correlated-stochastic time series. Here, we investigate the sensitivity of the HVG methodology to properties and pre-processing of real-world data, i.e., time series length, the presence of ties, and deseasonalization, using a set of around 150 runoff time series from managed rivers at daily resolution from Brazil with an average length of 65 years. We show that an application of HVGs on real-world time series requires a careful consideration of data pre-processing steps and analysis methodology before robust results and interpretations can be obtained. For example, one recent analysis of the degree distribution of runoff records reported pronounced sub-exponential “long-tailed” behavior of North American rivers, whereas another study of South American rivers showed hyper-exponential “short-tailed” behavior resembling correlated noise.We demonstrate, using the dataset of Brazilian rivers, that these apparently contradictory results can be reconciled by minor differences in data-preprocessing (here: small differences in subtracting the seasonal cycle). Hence, data-preprocessing that is conventional in hydrology (“deseasonalization”) changes long-term correlations and the overall runoff dynamics substantially, and we present empirical consequences and extensive simulations to investigate these issues from a HVG methodological perspective. After carefully accounting for these methodological aspects, the HVG analysis reveals that the river runoff dataset shows indeed complex behavior that appears to stem from a superposition of short-term correlated noise and “long-tailed behaviour,” i.e., highly connected nodes. Moreover, the construction of a dam along a river tends to increase short-term correlations in runoff series. In summary, the present study illustrates the (often substantial) effects of methodological and data-preprocessing choices for the interpretation of river runoff dynamics in the HVG framework and its general applicability for real-world time series.
Authors
Holger LangeAbstract
No abstract has been registered
Abstract
Many nonlinear methods of time series analysis require a minimal number of observations in the hundreds to thousands, which is not always easy to achieve for observations of environmental systems. As a result, finite size effects often hamper proper interpretation of the results; the estimation of the correlation dimension, Lyapunov exponents or KolmogorovSinai entropies, to name a few, is plagued by huge uncertainties. Eddy Covariance (EC) measurements of the carbon exchange between the atmosphere and vegetation provide a noticeable exception. The turbulent wind fields transporting carbon dioxide to the surface layer show variability over a large range of spatiotemporal scales, and their quantification demand a high temporal resolution, typically at 20 Hz. This generates very long time series even for short measurement periods; usually, the raw data are aggregated to carbon cycle observables, like Gross Primary Productivity (GPP) or Net Ecosystem Exchange (NEE) at half-hourly time steps. In this presentation, we investigate the high-resolution raw data of 3D wind speed and CO2 concentrations measured at a young forest plantation in Southeast Norway since July 2018. After introducing the EC technique and the Integrated Carbon Observation System (ICOS), we present results of complexity analysis, Tarnopolski diagrams, q-Entropy and Hurst analysis, and Empirical Mode Decomposition. This provides insights into not only whether the young forest stand is actually a source or sink of carbon, but also when, how and how strong carbon uptake and release are taking place at the site, and the nature of dynamics of carbon fluxes across this system boundary in general.
Authors
Svein SolbergAbstract
No abstract has been registered
Abstract
No abstract has been registered
Authors
Tore SkrøppaAbstract
No abstract has been registered
Authors
Tore SkrøppaAbstract
No abstract has been registered
Authors
Tore SkrøppaAbstract
No abstract has been registered