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
Authors
Holger Lange Sebastian Sippel Britta Aufgebauer Michael Hauhs Christina Bogner Henning MeesenburgAbstract
No abstract has been registered
Abstract
Remote sensing observations provide important information about vegetation and carbon dynamics on large scales, flux towers in situ measurements at the plot scale. Events important for ecological processes, such as hydrometeorological extremes, often happen at spatiotemporal scales between those covered by these two data sources. We discuss the event detection rates of ecological in situ networks as a function of their size and design. Using extreme reductions of the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), available from satellite missions, as a proxy for substantial losses in Gross Primary Productivity (GPP), we rank historical events according to their severity, and show how many would have been detected with a given number of randomly placed sites, discuss the problem of clustering of sites, and compare the theoretical results with the existing networks FLUXNET and NEON. The further spatio-temporal expansion of the ICOS network should carefully consider the size distribution of extreme events in order to be able to monitor their impacts on the terrestrial biosphere.
Authors
Sebastian Sippel Markus Reichstein Xuanlong Ma Miguel D. Mahecha Holger Lange Milan Flach Dorothea FrankAbstract
Purpose of the Review Weather and climate extremes substantially affect global- and regional-scale carbon (C) cycling, and thus spatially or temporally extended climatic extreme events jeopardize terrestrial ecosystem carbon sequestration. We illustrate the relevance of drought and/or heat events (“DHE”) for the carbon cycle and highlight underlying concepts and complex impact mechanisms. We review recent results, discuss current research needs and emerging research topics. Recent Findings Our review covers topics critical to understanding, attributing and predicting the effects of DHE on the terrestrial carbon cycle: (1) ecophysiological impact mechanisms and mediating factors, (2) the role of timing, duration and dynamical effects through which DHE impacts on regional-scale carbon cycling are either attenuated or enhanced, and (3) large-scale atmospheric conditions under which DHE are likely to unfold and to affect the terrestrial carbon cycle. Recent research thus shows the need to view these events in a broader spatial and temporal perspective that extends assessments beyond local and concurrent C cycle impacts of DHE. Summary Novel data streams, model (ensemble) simulations, and analyses allow to better understand carbon cycle impacts not only in response to their proximate drivers (drought, heat, etc.) but also attributing them to underlying changes in drivers and large-scale atmospheric conditions. These attribution-type analyses increasingly address and disentangle various sequences or dynamical interactions of events and their impacts, including compensating or amplifying effects on terrestrial carbon cycling.
Lecture – Ecosystem observatory networks for detecting extreme events
Holger Lange, Sebastian Sippel
Abstract
No abstract has been registered
Abstract
Horizontal Visibility Graphs (HVGs) are a recently developed method to construct networks based on time series. Values (the nodes of the network) of the time series are linked to each other if there is no value higher between them. The network properties reflect the nonlinear dynamics of the time series. For some classes of stochastic processes and for periodic time series, analytic results can be obtained for 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. We investigate a set of around 150 river runoff time series at daily resolution from Brazil with an average length of 65 years. Most of the rivers are exploited for power generation and thus represent heavily managed basins. We investigate both long-term trends and human influence (e.g. the effect of dam construction) in the runoff regimes (disregarding direct upstream operations). HVGs are used to determine the degree and distance 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. We also 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, changes long-term correlations and the overall dynamics substantially and towards more random behaviour. Moreover, hydrological time series are typically limited in length and may contain ties, and we present empirical consequences and extensive simulations to investigate these issues from a HVG methodological perspective. Focus is on one hand on universal properties of the HVG, common to all runoff series, and on site-specific aspects on the other.
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.