TY - THES A1 - Schwabedal, Justus Tilmann Caspar T1 - Phase dynamics of irregular oscillations N2 - In der vorliegenden Dissertation wird eine Beschreibung der Phasendynamik irregulärer Oszillationen und deren Wechselwirkungen vorgestellt. Hierbei werden chaotische und stochastische Oszillationen autonomer dissipativer Systeme betrachtet. Für eine Phasenbeschreibung stochastischer Oszillationen müssen zum einen unterschiedliche Werte der Phase zueinander in Beziehung gesetzt werden, um ihre Dynamik unabhängig von der gewählten Parametrisierung der Oszillation beschreiben zu können. Zum anderen müssen für stochastische und chaotische Oszillationen diejenigen Systemzustände identifiziert werden, die sich in der gleichen Phase befinden. Im Rahmen dieser Dissertation werden die Werte der Phase über eine gemittelte Phasengeschwindigkeitsfunktion miteinander in Beziehung gesetzt. Für stochastische Oszillationen sind jedoch verschiedene Definitionen der mittleren Geschwindigkeit möglich. Um die Unterschiede der Geschwindigkeitsdefinitionen besser zu verstehen, werden auf ihrer Basis effektive deterministische Modelle der Oszillationen konstruiert. Hierbei zeigt sich, dass die Modelle unterschiedliche Oszillationseigenschaften, wie z. B. die mittlere Frequenz oder die invariante Wahrscheinlichkeitsverteilung, nachahmen. Je nach Anwendung stellt die effektive Phasengeschwindigkeitsfunktion eines speziellen Modells eine zweckmäßige Phasenbeziehung her. Wie anhand einfacher Beispiele erklärt wird, kann so die Theorie der effektiven Phasendynamik auch kontinuierlich und pulsartig wechselwirkende stochastische Oszillationen beschreiben. Weiterhin wird ein Kriterium für die invariante Identifikation von Zuständen gleicher Phase irregulärer Oszillationen zu sogenannten generalisierten Isophasen beschrieben: Die Zustände einer solchen Isophase sollen in ihrer dynamischen Entwicklung ununterscheidbar werden. Für stochastische Oszillationen wird dieses Kriterium in einem mittleren Sinne interpretiert. Wie anhand von Beispielen demonstriert wird, lassen sich so verschiedene Typen stochastischer Oszillationen in einheitlicher Weise auf eine stochastische Phasendynamik reduzieren. Mit Hilfe eines numerischen Algorithmus zur Schätzung der Isophasen aus Daten wird die Anwendbarkeit der Theorie anhand eines Signals regelmäßiger Atmung gezeigt. Weiterhin zeigt sich, dass das Kriterium der Phasenidentifikation für chaotische Oszillationen nur approximativ erfüllt werden kann. Anhand des Rössleroszillators wird der tiefgreifende Zusammenhang zwischen approximativen Isophasen, chaotischer Phasendiffusion und instabilen periodischen Orbits dargelegt. Gemeinsam ermöglichen die Theorien der effektiven Phasendynamik und der generalisierten Isophasen eine umfassende und einheitliche Phasenbeschreibung irregulärer Oszillationen. N2 - Many natural systems embedded in a complex surrounding show irregular oscillatory dynamics. The oscillations can be parameterized by a phase variable in order to obtain a simplified theoretical description of the dynamics. Importantly, a phase description can be easily extended to describe the interactions of the system with its surrounding. It is desirable to define an invariant phase that is independent of the observable or the arbitrary parameterization, in order to make, for example, the phase characteristics obtained from different experiments comparable. In this thesis, we present an invariant phase description of irregular oscillations and their interactions with the surrounding. The description is applicable to stochastic and chaotic irregular oscillations of autonomous dissipative systems. For this it is necessary to interrelate different phase values in order to allow for a parameterization-independent phase definition. On the other hand, a criterion is needed, that invariantly identifies the system states that are in the same phase. To allow for a parameterization-independent definition of phase, we interrelate different phase values by the phase velocity. However, the treatment of stochastic oscillations is complicated by the fact that different definitions of average velocity are possible. For a better understanding of their differences, we analyse effective deterministic phase models of the oscillations based upon the different velocity definitions. Dependent on the application, a certain effective velocity is suitable for a parameterization-independent phase description. In this way, continuous as well pulse-like interactions of stochastic oscillations can be described, as it is demonstrated with simple examples. On the other hand, an invariant criterion of identification is proposed that generalizes the concept of standard (Winfree) isophases. System states of the same phase are identified to belong to the same generalized isophase using the following invariant criterion: All states of an isophase shall become indistinguishable in the course of time. The criterion is interpreted in an average sense for stochastic oscillations. It allows for a unified treatment of different types of stochastic oscillations. Using a numerical estimation algorithm of isophases, the applicability of the theory is demonstrated by a signal of regular human respiration. For chaotic oscillations, generalized isophases can only be obtained up to a certain approximation. The intimate relationship between these approximate isophase, chaotic phase diffusion, and unstable periodic orbits is explained with the example of the chaotic \roes oscillator. Together, the concept of generalized isophases and the effective phase theory allow for a unified, and invariant phase description of stochastic and chaotic irregular oscillations. T2 - Phasendynamik irregulärer Oszillationen KW - Phasendynamik KW - Stochastische Oszillationen KW - Chaotische Oszillationen KW - Phasenkopplung KW - Zeitreihenanalyse KW - phase dynamics KW - stochastic oscillations KW - chaotic oscillations KW - phase coupling KW - time series analysis Y1 - 2010 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus-50115 ER - TY - JOUR A1 - Marwan, Norbert T1 - How to avoid potential pitfalls in recurrence plot based data analysis JF - International journal of bifurcation and chaos : in applied sciences and engineering N2 - Recurrence plots and recurrence quantification analysis have become popular in the last two decades. Recurrence based methods have on the one hand a deep foundation in the theory of dynamical systems and are on the other hand powerful tools for the investigation of a variety of problems. The increasing interest encompasses the growing risk of misuse and uncritical application of these methods. Therefore, we point out potential problems and pitfalls related to different aspects of the application of recurrence plots and recurrence quantification analysis. KW - Recurrence plot KW - recurrence quantification analysis KW - time series analysis KW - pitfalls Y1 - 2011 U6 - https://doi.org/10.1142/S0218127411029008 SN - 0218-1274 VL - 21 IS - 4 SP - 1003 EP - 1017 PB - World Scientific CY - Singapore ER - TY - JOUR A1 - Costa, Alexandre Cunha A1 - Bronstert, Axel A1 - Kneis, David T1 - Probabilistic flood forecasting for a mountainous headwater catchment using a nonparametric stochastic dynamic approach JF - Hydrological sciences journal = Journal des sciences hydrologiques N2 - Hydrological models are commonly used to perform real-time runoff forecasting for flood warning. Their application requires catchment characteristics and precipitation series that are not always available. An alternative approach is nonparametric modelling based only on runoff series. However, the following questions arise: Can nonparametric models show reliable forecasting? Can they perform as reliably as hydrological models? We performed probabilistic forecasting one, two and three hours ahead for a runoff series, with the aim of ascribing a probability density function to predicted discharge using time series analysis based on stochastic dynamics theory. The derived dynamic terms were compared to a hydrological model, LARSIM. Our procedure was able to forecast within 95% confidence interval 1-, 2- and 3-h ahead discharge probability functions with about 1.40 m(3)/s of range and relative errors (%) in the range [-30; 30]. The LARSIM model and the best nonparametric approaches gave similar results, but the range of relative errors was larger for the nonparametric approaches. KW - streamflow probabilistic forecasting KW - time series analysis KW - stochastic dynamical systems KW - parametric and nonparametric comparison Y1 - 2012 U6 - https://doi.org/10.1080/02626667.2011.637043 SN - 0262-6667 VL - 57 IS - 1 SP - 10 EP - 25 PB - Routledge, Taylor & Francis Group CY - Abingdon ER - TY - THES A1 - Kütter, Sissy T1 - Magnetotelluric measurements across the southern Barberton Greenstone Belt, South Africa T1 - Magnetotellurische Messungen am südlichen Barberton Grünsteingürtel, Südafrika N2 - Der Barberton Grünsteingürtel (BGB) in Südafrika gehört zu den wenigen Regionen mit noch gut erhaltener Archaischer Kruste. Seit Jahrhunderten wurde der BGB eingehend untersucht und seine geologischen und tektonischen Strukturen detailliert kartiert. Über die tiefere Struktur des BGB ist hingegen wenig bekannt. Zahlreiche Evolutionsmodelle, die auf Altersbestimmungsdaten und strukturellen Informationen beruhen wurden über die Jahre aufgestellt. Diese Theorien sind zumeist widersprüchlich. Sie konzentrieren sich im Wesentlichen auf die Frage, ob plattentektonische Prozesse bereits bei der Entwicklung der frühen Erde eine Rolle spielten oder ob vertikale Tektonik, angetrieben durch die im Archaikum höheren Temperaturen, die Bildung der heutigen Kontinente bestimmt hat. Um neue Erkenntnisse über die interne Struktur und Entwicklungsgeschichte des BGB zu erhalten, wurden im Rahmen der Deutsch-Südafrikanischen Forschungsinitiative Inkaba yeAfrica magnetotellurische (MT) Messungen durchgeführt. Entlang von sechs Profilen, die den gesamten südlichen Teil des BGB’s überdecken, wurden nahezu 200 MT-Stationen installiert. Tektonische Strukturen wie z. B. (fossile) Verwerfungszonen können erhöhte Leitfähigigkeiten haben, wenn sich leitfähige Mineralisationen innerhalb der Scherzonen gebildet haben. Durch die Abbildung der elektrischen Leitfähigkeitsverteilung des Untergrundes mit Hilfe von MT Messungen kann der Verlauf tektonischer Strukturen nachvollzogen werden, woraus Schlüsse über möglicherweise abgelaufene tektonische Prozesse gezogen werden können. Der gesamte MT Datensatz weist starke Störeinflüsse durch künstliche elektromagnetische Signale auf, die bspw. von Stromleitungen und elektrischen Zäunen stammen. Insbesondere langperiodische Daten (>1 s) sind davon betroffen, die für die Auflösung tieferer Strukturen notwendig sind. Die Anwendung etablierter Ansätze wie Verschiebungsfiltern und der Remote Reference-Methode, führte zu Verbesserungen vorrangig für Perioden < 1 s. Der langperiodische Bereich ist durch impulsartige Störsignale in den magnetischen und dazugehörigen Stufen in den elektrischen Feldkomponenten geprägt. Im Rahmen dieser Arbeit wurde ein neuartiger Zeitbereichs-Filter entwickelt, welcher auf einer abgewandelten Form des Wiener Filters beruht und diese Art von Störsignalen aus den Daten entfernt. Durch den Vergleich der Datenvarianz einer lokalen Station mit der einer Referenzstation können gestörte Zeitsegmente identifiziert werden. Anschließend wird ein Wiener-Filter-Algorithmus angewendet, um für diese Segmente mithilfe der Referenzdaten physikalisch sinnvolle Zeitreihen zu berechnen, mit denen die Daten der lokalen Station ersetzt werden. Während impulsartige Störsignale in den magnetischen Datenkanälen relativ einfach erfasst werden können, ist die Detektion von Versätzen in den elektrischen Zeitreihen je nach Versatzhöhe problematischer. Um dieses Problem zu umgehen, habe ich einen Algorithmus entwickelt, bei dem die Zeitreihen differenziert, gefiltert und im letzten Schritt integriert werden. In einer zweiten von mir entwickelten Filtermethode werden die Störsignale durch den Vergleich des kurzzeitigen und des langzeitigen Datenmittelwerts ausfindig gemacht. Bei diesem Filter werden die Störsignale aus den Zeitreihen entfernt und durch eine lineare Interpolation ersetzt. Durch die beiden Filtermethoden wurde eine deutliche Verbesserung der Datenqualität bis zu 10 und teilweise 100 s erreicht. Zur Interpretation des MT-Datensatzes wurden 2D und 3D Inversionen durchgeführt. Die so erhaltenen elektrischen Leitfähigkeitsmodelle zeigen eine gute Übereinstimmung mit den kartierten, geologischen Strukturen. Die Gesteine des BGB weisen in den Modellen hohe Widerstände auf und sind deutlich von leitfähigen benachbarten geologischen Strukturen abgegrenzt. Verwerfungszonen korrelieren mit leitfähigen Strukturen, die sich bis in eine Tiefe von 5 bis 10 km erstrecken. Eine Fortsetzung der Verwerfungszonen über die südliche Grenze des BGB wird in den 2D-Ergebnissen angedeutet. Insgesamt zeigen die Inversionsmodelle, dass vermutlich sowohl plattentektonische als auch vertikaltektonische Prozesse bei der Entstehung des BGB eine wichtige Rolle spielten. N2 - The Barberton Greenstone Belt (BGB) in the northwestern part of South Africa belongs to the few well-preserved remnants of Archean crust. Over the last centuries, the BGB has been intensively studied at surface with detailed mapping of its surfacial geological units and tectonic features. Nevertheless, the deeper structure of the BGB remains poorly understood. Various tectonic evolution models have been developed based on geo-chronological and structural data. These theories are highly controversial and centre on the question whether plate tectonics - as geoscientists understand them today - was already evolving on the Early Earth or whether vertical mass movements driven by the higher temperature of the Earth in Archean times governed continent development. To get a step closer to answering the questions regarding the internal structure and formation of the BGB, magnetotelluric (MT) field experiments were conducted as part of the German-South African research initiative Inkaba yeAfrica. Five-component MT data (three magnetic and two electric channels) were collected at ~200 sites aligned along six profiles crossing the southern part of the BGB. Tectonic features like (fossil) faults and shear zones are often mineralized and therefore can have high electrical conductivities. Hence, by obtaining an image of the conductivity distribution of the subsurface from MT measurements can provide useful information on tectonic processes. Unfortunately, the BGB MT data set is heavily affected by man-made electromagnetic noise caused, e.g. by powerlines and electric fences. Aperiodic spikes in the magnetic and corresponding offsets in the electric field components impair the data quality particularly at periods >1 s which are required to image deep electrical structures. Application of common methods for noise reduction like delay filtering and remote reference processing, only worked well for periods <1 s. Within the framework of this thesis two new filtering approaches were developed to handle the severe noise in long period data and obtain reliable processing results. The first algorithm is based on the Wiener filter in combination with a spike detection algorithm. Comparison of data variances of a local site with those of a reference site allows the identification of disturbed time series windows for each recorded channel at the local site. Using the data of the reference site, a Wiener filter algorithm is applied to predict physically meaningful data to replace the disturbed windows. While spikes in the magnetic channels are easily recognized and replaced, steps in the electric channels are more difficult to detect depending on their offset. Therefore, I have implemented a novel approach based on time series differentiation, noise removal and subsequent integration to overcome this obstacle. A second filtering approach where spikes and steps in the time series are identified using a comparison of the short and long time average of the data was also implemented as part of my thesis. For this filtering approach the noise in the form of spikes and offsets in the data is treated by an interpolation of the affected data samples. The new developments resulted in a substantial data improvement and allowed to gain one to two decades of data (up to 10 or 100 s). The re-processed MT data were used to image the electrical conductivity distribution of the BGB by 2D and 3D inversion. Inversion models are in good agreement with the surface geology delineating the highly resistive rocks of the BGB from surrounding more conductive geological units. Fault zones appear as conductive structures and can be traced to depths of 5 to 10 km. 2D models suggest a continuation of the faults further south across the boundary of the BGB. Based on the shallow tectonic structures (fault system) within the BGB compared to deeply rooted resistive batholiths in the area, tectonic models including both vertical mass transport and in parts present-day style plate tectonics seem to be most likely for the evolution of the BGB. KW - Magnetotellurik KW - magnetotellurics KW - Zeitreihenanalyse KW - time series analysis KW - Datenfilter KW - data filtering KW - Paläotektonik KW - early earth tectonics Y1 - 2015 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-83198 ER - TY - THES A1 - Hohenbrink, Tobias Ludwig T1 - Turning a problem into a solution: heterogeneities in soil hydrology T1 - Ein Problem zur Lösung machen: Heterogenitäten in der Bodenhydrologie N2 - It is commonly recognized that soil moisture exhibits spatial heterogeneities occurring in a wide range of scales. These heterogeneities are caused by different factors ranging from soil structure at the plot scale to land use at the landscape scale. There is an urgent need for effi-cient approaches to deal with soil moisture heterogeneity at large scales, where manage-ment decisions are usually made. The aim of this dissertation was to test innovative ap-proaches for making efficient use of standard soil hydrological data in order to assess seep-age rates and main controls on observed hydrological behavior, including the role of soil het-erogeneities. As a first step, the applicability of a simplified Buckingham-Darcy method to estimate deep seepage fluxes from point information of soil moisture dynamics was assessed. This was done in a numerical experiment considering a broad range of soil textures and textural het-erogeneities. The method performed well for most soil texture classes. However, in pure sand where seepage fluxes were dominated by heterogeneous flow fields it turned out to be not applicable, because it simply neglects the effect of water flow heterogeneity. In this study a need for new efficient approaches to handle heterogeneities in one-dimensional water flux models was identified. As a further step, an approach to turn the problem of soil moisture heterogeneity into a solu-tion was presented: Principal component analysis was applied to make use of the variability among soil moisture time series for analyzing apparently complex soil hydrological systems. It can be used for identifying the main controls on the hydrological behavior, quantifying their relevance, and describing their particular effects by functional averaged time series. The ap-proach was firstly tested with soil moisture time series simulated for different texture classes in homogeneous and heterogeneous model domains. Afterwards, it was applied to 57 mois-ture time series measured in a multifactorial long term field experiment in Northeast Germa-ny. The dimensionality of both data sets was rather low, because more than 85 % of the total moisture variance could already be explained by the hydrological input signal and by signal transformation with soil depth. The perspective of signal transformation, i.e. analyzing how hydrological input signals (e.g., rainfall, snow melt) propagate through the vadose zone, turned out to be a valuable supplement to the common mass flux considerations. Neither different textures nor spatial heterogeneities affected the general kind of signal transfor-mation showing that complex spatial structures do not necessarily evoke a complex hydro-logical behavior. In case of the field measured data another 3.6% of the total variance was unambiguously explained by different cropping systems. Additionally, it was shown that dif-ferent soil tillage practices did not affect the soil moisture dynamics at all. The presented approach does not require a priori assumptions about the nature of physical processes, and it is not restricted to specific scales. Thus, it opens various possibilities to in-corporate the key information from monitoring data sets into the modeling exercise and thereby reduce model uncertainties. N2 - Es ist allgemein anerkannt, dass Bodenfeuchte auf verschiedenen Raumskalen räumliche Heterogenitäten aufweist. Diese Heterogenitäten werden durch verschiedene Faktoren verursacht, die auf den unterschiedlichen Skalen wirken. Dies können z.B. die Bodenstruktur auf Plotskala oder die Landnutzung auf Landschaftsskala sein. Es werden dringend effiziente Ansätze benötigt, um mit den Heterogenitäten der Bodenfeuchte umzugehen. Dies gilt be-sonders für große Skalen, auf denen in der Regel weitreichende Managemententscheidun-gen getroffen werden. Das Ziel dieser Dissertation war es, effiziente Methoden zu testen, die es ermöglichen auf Basis bodenhydrologischer Daten sowohl Sickerwasserraten als auch die Haupteinflussfaktoren der Bodenfeuchtedynamik zu bestimmen. Dies bezieht Effekte von Bodenheterogenitäten mit ein. In einem ersten Schritt wurde die Eignung einer vereinfachten Buckingham-Darcy Methode zur Abschätzung von Sickerwasserflüssen auf Grundlage punktuell gemessener Zeitreihen der Bodenfeuchte untersucht. Hierzu wurde eine Simulationsstudie durchgeführt, in der ein breites Spektrum an Bodentexturen und Texturheterogenitäten berücksichtigt wurde. Die Methode lieferte gute Ergebnisse für die meisten Texturklassen. In reinem Sand jedoch stell-te sie sich als nicht anwendbar heraus, da hier Sickerwasserflüsse von heterogenen Fließfel-dern dominiert wurden. In dieser Studie wurde ein Bedarf an neuen effizienten Ansätzen für den Umgang mit Heterogenitäten in eindimensionalen Wasserflussmodellen identifiziert. In einem weiteren Schritt wurde ein Ansatz vorgestellt, um aus dem Problem der Boden-feuchteheterogenität eine Lösung zu machen: In einer Hauptkomponentenanalyse wurde die Variabilität zwischen Bodenfeuchtezeitreihen genutzt, um die wahre Komplexität bo-denhydrologischer Systeme zu analysieren. Auf diesem Weg ist es möglich die Haupteinfluss-faktoren des hydrologischen Verhaltens zu identifizieren, ihre Relevanz zu quantifizieren und ihre jeweiligen Effekte als funktional gemittelte Zeitreihen zu beschreiben. Der Ansatz wurde zunächst mit simulierten Bodenfeuchtezeitreihen für unterschiedliche Texturklassen im ho-mogenen und heterogenen Fall getestet. Anschließend wurde die Methode auf 57 Boden-feuchtezeitreihen angewendet, die in einem Langzeitfeldexperiment in Nordostdeutschland gemessen wurden. Die Dimensionalität beider Datensätze war gering, da mehr als 85 % der gesamten Boden-feuchtevarianz bereits durch das hydrologische Eingangssignal und die Transformation dieses Signals mit zunehmender Bodentiefe erklärt werden konnten. Analysen der Signaltransfor-mation haben sich als wertvolle Ergänzung zu den weit verbreiteten Massenflussbetrachtun-gen herausgestellt. Hierbei wird untersucht, wie sich hydrologische Eingangssignale (z.B. Nie-derschlag oder Schneeschmelze) in der vadosen Zone fortpflanzen. Die generellen Muster der Signaltransformation wurden weder durch verschiedene Bodentexturen noch durch räumliche Heterogeneitäten beeinfluss. Dies zeigt, dass komplexe räumliche Strukturen nicht zwangsläufig ein komplexes hydrologisches Verhalten hervorrufen. Im Fall der Felddaten wurden weitere 3,6 % der Gesamtvarianz durch verschiedene Fruchtfolgen erklärt. Darüber hinaus konnte gezeigt werden, dass die Bodenbearbeitung keinen Einfluss auf die Boden-feuchtedynamik hatte. Der vorgestellte Ansatz erfordert keine Vorannahmen über physikalische Prozesse und ist nicht auf eine bestimmte Skala begrenzt. Dadurch ergeben sich viele Möglichkeiten, wichtige Informationen aus Monitoringdatensätzen in die Modellbildung einzubeziehen und damit Modell-unsicherheiten zu verringern. KW - soil hydrology KW - soil heterogeneity KW - soil moisture KW - deep seepage KW - time series analysis KW - transformation of hydrological signals KW - Bodenhydrologie KW - Bodenheterogenität KW - Bodenfeuchte KW - Tiefenversickerung KW - Zeitreihenanalyse KW - Transformation hydrologischer Signale Y1 - 2017 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-101485 ER - TY - THES A1 - Schmidt, Silke Regina T1 - Analyzing lakes in the time frequency domain T1 - Analyse von Seen in der Zeit-Frequenz-Domäne N2 - The central aim of this thesis is to demonstrate the benefits of innovative frequency-based methods to better explain the variability observed in lake ecosystems. Freshwater ecosystems may be the most threatened part of the hydrosphere. Lake ecosystems are particularly sensitive to changes in climate and land use because they integrate disturbances across their entire catchment. This makes understanding the dynamics of lake ecosystems an intriguing and important research priority. This thesis adds new findings to the baseline knowledge regarding variability in lake ecosystems. It provides a literature-based, data-driven and methodological framework for the investigation of variability and patterns in environmental parameters in the time frequency domain. Observational data often show considerable variability in the environmental parameters of lake ecosystems. This variability is mostly driven by a plethora of periodic and stochastic processes inside and outside the ecosystems. These run in parallel and may operate at vastly different time scales, ranging from seconds to decades. In measured data, all of these signals are superimposed, and dominant processes may obscure the signals of other processes, particularly when analyzing mean values over long time scales. Dominant signals are often caused by phenomena at long time scales like seasonal cycles, and most of these are well understood in the limnological literature. The variability injected by biological, chemical and physical processes operating at smaller time scales is less well understood. However, variability affects the state and health of lake ecosystems at all time scales. Besides measuring time series at sufficiently high temporal resolution, the investigation of the full spectrum of variability requires innovative methods of analysis. Analyzing observational data in the time frequency domain allows to identify variability at different time scales and facilitates their attribution to specific processes. The merit of this approach is subsequently demonstrated in three case studies. The first study uses a conceptual analysis to demonstrate the importance of time scales for the detection of ecosystem responses to climate change. These responses often occur during critical time windows in the year, may exhibit a time lag and can be driven by the exceedance of thresholds in their drivers. This can only be detected if the temporal resolution of the data is high enough. The second study applies Fast Fourier Transform spectral analysis to two decades of daily water temperature measurements to show how temporal and spatial scales of water temperature variability can serve as an indicator for mixing in a shallow, polymictic lake. The final study uses wavelet coherence as a diagnostic tool for limnology on a multivariate high-frequency data set recorded between the onset of ice cover and a cyanobacteria summer bloom in the year 2009 in a polymictic lake. Synchronicities among limnological and meteorological time series in narrow frequency bands were used to identify and disentangle prevailing limnological processes. Beyond the novel empirical findings reported in the three case studies, this thesis aims to more generally be of interest to researchers dealing with now increasingly available time series data at high temporal resolution. A set of innovative methods to attribute patterns to processes, their drivers and constraints is provided to help make more efficient use of this kind of data. N2 - See-Ökosysteme sind eine der bedrohtesten Ressourcen der Hydrosphäre. Sie reagieren besonders sensibel auf Veränderungen des Klimas und auf Einflüsse durch Landnutzung, da verschiedene Prozesse im gesamten Einzugsgebiet auf sie einwirken. Daher ist es von besonderer Dringlichkeit, die verschiedenen Prozess-Dynamiken in See-Ökosystemen besser zu verstehen. Die hier vorliegende Doktorarbeit hat zum Ziel, das bestehende Wissen bezüglich der verschiedenen einwirkenden Prozesse in See-Ökosystemen zu erweitern. Die Arbeit stellt ein Forschungsdesign zur Diskussion, das eine Literatur-basierte und auf empirischen Erhebungen beruhende Analyse von Variabilität und Mustern in großen Datensätzen verschiedener Umweltparameter im Zeit-Frequenz-Raum ermöglicht. Umweltparameter sind häufig charakterisiert durch eine hohe zeitliche Dynamik. Diese Variabilität steht im Zentrum dieser Arbeit. Sie wird durch eine Fülle an periodischen und stochastischen Prozessen innerhalb und außerhalb des Ökosystems getrieben. Diese Prozesse können gleichzeitig und auf sehr unterschiedlichen Zeitskalen, von Sekunden bis hin zu Dekaden, ablaufen. In Messdaten überlagern sich alle diese Signale, und dominante Prozesse können die Signale anderer Prozesse verschleiern, insbesondere wenn Mittelwerte über längere Zeiträume analysiert werden. Dominante Signale werden oft durch Prozesse auf längeren Zeitskalen verursacht, wie z. B. saisonale Zyklen. Diese sind im Allgemeinen in der limnologischen Literatur gut dokumentiert. See-Ökosysteme werden allerdings von Prozessen auf allen Zeitskalen beeinflusst. Insbesondere biologische, chemische und physikalische Prozesse operieren in kürzeren Zeitrahmen. Die Variabilität, die über solche Prozesse in See-Ökosysteme eingebracht wird, ist bisher weit weniger gut erforscht. Neben der Notwendigkeit, Umweltparameter in hoher zeitlicher Auflösung zu messen, erfordert die Untersuchung der kompletten Bandbreite an Variabilität innovative Analysemethoden. Die Berücksichtigung der Zeit-Frequenz-Domäne kann dabei helfen, Dynamiken auf verschiedenen Zeitskalen zu identifizieren und daraus bestimmte Prozesse abzuleiten. Diese Arbeit zeigt die Vorzüge dieser Herangehensweise anhand von drei Fallstudien auf. Die erste Studie zeigt die Bedeutung von Zeitskalen für die Erfassung von Ökosystem-Reaktionen auf klimatische Veränderungen. Diese ereignen sich oft während kritischer Zeitfenster im Jahresverlauf und können durch die Überschreitung von Schwellenwerten in den treibenden Variablen, unter Umständen zeitlich verzögert, verursacht sein. Solche Zusammenhänge können nur erfasst werden, wenn die zeitliche Auflösung der Daten hoch genug ist. In der zweiten Studie wird die Spektralanalyse, basierend auf der Fast Fourier Transformation, auf einen Datensatz täglicher Messungen der Wassertemperatur über zwanzig Jahre hinweg angewendet. Es wird gezeigt, wie zeitliche und räumliche Skalen der Variabilität der Wassertemperatur als Indikator für Mischprozesse in einem polymiktischen See dienen können. In der dritten Studie wird die Wavelet Coherence als Diagnose-Werkzeug für einen multivariaten, hochfrequenten Datensatz genutzt. Dieser wurde zwischen dem Einsetzen einer Eisbedeckung und einer Sommerblüte von Cyanobakteriern in einem polymiktischen See im Jahr 2009 erhoben. Synchronizitäten zwischen limnologischen und meteorologischen Zeitreihen in schmalen Frequenz-Bändern wurden genutzt, um vorherrschende limnologische Prozesse zu identifizieren und analytisch zu trennen. Neben den neuen empirischen Erkenntnissen, die in den drei Fallstudien präsentiert werden, zielt diese Doktorarbeit darauf ab, Forscher*innen, Behörden und politischen Entscheidungsträger*innen eine Grundlage zu liefern, die hohe zeitliche Auflösung der heute vielfach verfügbaren Monitoring-Datensätze effizienter zu nutzen. Innovative Methoden sollen dabei helfen, Muster in den Daten Prozessen zuzuordnen und die entsprechenden Treiber und Limitationen zu identifizieren. KW - variability KW - time scale KW - wavelet KW - coherence KW - spectral analysis KW - time series analysis KW - polymictic lakes KW - process identification KW - Variabilität KW - Zeitskala KW - Spektralanalyse KW - Zeitreihenanalyse KW - polymiktische Seen KW - Prozessidentifikation Y1 - 2017 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-406955 ER - TY - JOUR A1 - Rosenbaum, Benjamin A1 - Raatz, Michael A1 - Weithoff, Guntram A1 - Fussmann, Gregor F. A1 - Gaedke, Ursula T1 - Estimating parameters from multiple time series of population dynamics using bayesian inference JF - Frontiers in ecology and evolution N2 - Empirical time series of interacting entities, e.g., species abundances, are highly useful to study ecological mechanisms. Mathematical models are valuable tools to further elucidate those mechanisms and underlying processes. However, obtaining an agreement between model predictions and experimental observations remains a demanding task. As models always abstract from reality one parameter often summarizes several properties. Parameter measurements are performed in additional experiments independent of the ones delivering the time series. Transferring these parameter values to different settings may result in incorrect parametrizations. On top of that, the properties of organisms and thus the respective parameter values may vary considerably. These issues limit the use of a priori model parametrizations. In this study, we present a method suited for a direct estimation of model parameters and their variability from experimental time series data. We combine numerical simulations of a continuous-time dynamical population model with Bayesian inference, using a hierarchical framework that allows for variability of individual parameters. The method is applied to a comprehensive set of time series from a laboratory predator-prey system that features both steady states and cyclic population dynamics. Our model predictions are able to reproduce both steady states and cyclic dynamics of the data. Additionally to the direct estimates of the parameter values, the Bayesian approach also provides their uncertainties. We found that fitting cyclic population dynamics, which contain more information on the process rates than steady states, yields more precise parameter estimates. We detected significant variability among parameters of different time series and identified the variation in the maximum growth rate of the prey as a source for the transition from steady states to cyclic dynamics. By lending more flexibility to the model, our approach facilitates parametrizations and shows more easily which patterns in time series can be explained also by simple models. Applying Bayesian inference and dynamical population models in conjunction may help to quantify the profound variability in organismal properties in nature. KW - Bayesian inference KW - chemostat experiments KW - ordinary differential equation KW - parameter estimation KW - population dynamics KW - predator prey KW - time series analysis KW - trait variability Y1 - 2019 U6 - https://doi.org/10.3389/fevo.2018.00234 SN - 2296-701X VL - 6 PB - Frontiers Research Foundation CY - Lausanne ER - TY - JOUR A1 - Selle, Benny A1 - Knorr, Klaus-Holger A1 - Lischeid, Gunnar T1 - Mobilisation and transport of dissolved organic carbon and iron in peat catchments-Insights from the Lehstenbach stream in Germany using generalised additive models JF - Hydrological processes N2 - During the last decades, increasing exports of both dissolved organic carbon (DOC) and iron were observed from peat catchments in North America and Europe with potential consequences for water quality of streamwater and carbon storages of soils. As mobilisation and transport processes of DOC and iron in peat catchments are only partly understood, the purpose of this study was to elucidate these processes in an intensively monitored and studied system. Specifically, it was hypothesised that dissimilatory iron reduction in riparian peatland soils mobilises DOC initially adsorbed to iron minerals. During stormflow conditions, both DOC and iron will be transported into the stream network. Ferrous iron may be reoxidised at redox interfaces on its way to the stream, and subsequently, ferric iron could be transported together with DOC as complexes. To test these hypotheses, generalised additive models (GAMs) were applied to 14 years of weekly time series of discharge and concentrations of selected solutes measured in a German headwater stream called Lehstenbach. This stream drains a 4.19-km(2) forested mountain catchment; one third of which is covered by riparian peatland soils. We interpreted results of different types of GAM in the way that (a) iron reduction drove the mobilisation of DOC from peatland soils and that (b) both iron and DOC were transported as complexes after their joint mobilisation to and within the steam. It was speculated that low nitrate availability in the uppermost wetland soil layer, particularly during the growing season, promoted iron reduction and thus the mobilisation of DOC. However, the influence of nitrate on the DOC mobilisation remains relatively uncertain. This influence could be further investigated using methods similar to the GAM analysis conducted here for other catchments with long-term data as well as detailed measurements of the relevant species in riparian wetland soils and the adjacent stream network. KW - dissolved organic carbon (DOC) KW - generalised additive models (GAMs) KW - headwater catchments KW - iron KW - peatlands KW - time series analysis Y1 - 2019 U6 - https://doi.org/10.1002/hyp.13552 SN - 0885-6087 SN - 1099-1085 VL - 33 IS - 25 SP - 3213 EP - 3225 PB - Wiley CY - Hoboken ER - TY - JOUR A1 - Pennekamp, Frank A1 - Iles, Alison C. A1 - Garland, Joshua A1 - Brennan, Georgina A1 - Brose, Ulrich A1 - Gaedke, Ursula A1 - Jacob, Ute A1 - Kratina, Pavel A1 - Matthews, Blake A1 - Munch, Stephan A1 - Novak, Mark A1 - Palamara, Gian Marco A1 - Rall, Bjorn C. A1 - Rosenbaum, Benjamin A1 - Tabi, Andrea A1 - Ward, Colette A1 - Williams, Richard A1 - Ye, Hao A1 - Petchey, Owen L. T1 - The intrinsic predictability of ecological time series and its potential to guide forecasting JF - Ecological monographs : a publication of the Ecological Society of America. KW - empirical dynamic modelling KW - forecasting KW - information theory KW - permutation entropy KW - population dynamics KW - time series analysis Y1 - 2019 U6 - https://doi.org/10.1002/ecm.1359 SN - 0012-9615 SN - 1557-7015 VL - 89 IS - 2 PB - Wiley CY - Hoboken ER - TY - GEN A1 - Ślęzak, Jakub A1 - Burnecki, Krzysztof A1 - Metzler, Ralf T1 - Random coefficient autoregressive processes describe Brownian yet non-Gaussian diffusion in heterogeneous systems T2 - Postprints der Universität Potsdam Mathematisch-Naturwissenschaftliche Reihe N2 - Many studies on biological and soft matter systems report the joint presence of a linear mean-squared displacement and a non-Gaussian probability density exhibiting, for instance, exponential or stretched-Gaussian tails. This phenomenon is ascribed to the heterogeneity of the medium and is captured by random parameter models such as ‘superstatistics’ or ‘diffusing diffusivity’. Independently, scientists working in the area of time series analysis and statistics have studied a class of discrete-time processes with similar properties, namely, random coefficient autoregressive models. In this work we try to reconcile these two approaches and thus provide a bridge between physical stochastic processes and autoregressive models.Westart from the basic Langevin equation of motion with time-varying damping or diffusion coefficients and establish the link to random coefficient autoregressive processes. By exploring that link we gain access to efficient statistical methods which can help to identify data exhibiting Brownian yet non-Gaussian diffusion. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 765 KW - diffusion KW - Langevin equation KW - Brownian yet non-Gaussian diffusion KW - diffusing diffusivity KW - superstatistics KW - autoregressive models KW - time series analysis KW - codifference Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-437923 SN - 1866-8372 IS - 765 ER - TY - JOUR A1 - Ślęzak, Jakub A1 - Burnecki, Krzysztof A1 - Metzler, Ralf T1 - Random coefficient autoregressive processes describe Brownian yet non-Gaussian diffusion in heterogeneous systems JF - New Journal of Physics N2 - Many studies on biological and soft matter systems report the joint presence of a linear mean-squared displacement and a non-Gaussian probability density exhibiting, for instance, exponential or stretched-Gaussian tails. This phenomenon is ascribed to the heterogeneity of the medium and is captured by random parameter models such as ‘superstatistics’ or ‘diffusing diffusivity’. Independently, scientists working in the area of time series analysis and statistics have studied a class of discrete-time processes with similar properties, namely, random coefficient autoregressive models. In this work we try to reconcile these two approaches and thus provide a bridge between physical stochastic processes and autoregressive models.Westart from the basic Langevin equation of motion with time-varying damping or diffusion coefficients and establish the link to random coefficient autoregressive processes. By exploring that link we gain access to efficient statistical methods which can help to identify data exhibiting Brownian yet non-Gaussian diffusion. KW - diffusion KW - Langevin equation KW - Brownian yet non-Gaussian diffusion KW - diffusing diffusivity KW - superstatistics KW - autoregressive models KW - time series analysis KW - codifference Y1 - 2019 U6 - https://doi.org/10.1088/1367-2630/ab3366 SN - 1367-2630 VL - 21 PB - Deutsche Physikalische Gesellschaft ; IOP, Institute of Physics CY - Bad Honnef und London ER - TY - JOUR A1 - Loibl, David A1 - Bookhagen, Bodo A1 - Valade, Sebastien A1 - Schneider, Christoph T1 - OSARIS, the "Open Source SAR Investigation System" for Automatized Parallel InSAR Processing of Sentinel-1 Time Series Data With Special Emphasis on Cryosphere Applications JF - Frontiers in Earth Science N2 - With the advent of the two Sentinel-1 (S1) satellites, Synthetic Aperture Radar (SAR) data with high temporal and spatial resolution are freely available. This provides a promising framework to facilitate detailed investigations of surface instabilities and movements on large scales with high temporal resolution, but also poses substantial processing challenges because of storage and computation requirements. Methods are needed to efficiently detect short term changes in dynamic environments. Approaches considering pair-wise processing of a series of consecutive scenes to retain maximum temporal resolution in conjunction with time series analyses are required. Here we present OSARIS, the “Open Source SAR Investigation System,” as a framework to process large stacks of S1 data on high-performance computing clusters. Based on Generic Mapping Tools SAR, shell scripts, and the workload manager Slurm, OSARIS provides an open and modular framework combining parallelization of high-performance C programs, flexible processing schemes, convenient configuration, and generation of geocoded stacks of analysis-ready base data, including amplitude, phase, coherence, and unwrapped interferograms. Time series analyses can be conducted by applying automated modules to the data stacks. The capabilities of OSARIS are demonstrated in a case study from the northwestern Tien Shan, Central Asia. After merging of slices, a total of 80 scene pairs were processed from 174 total input scenes. The coherence time series exhibits pronounced seasonal variability, with relatively high coherence values prevailing during the summer months in the nival zone. As an example of a time series analysis module, we present OSARIS' “Unstable Coherence Metric” which identifies pixels affected by significant drops from high to low coherence values. Measurements of motion provided by LOSD measurements require careful evaluation because interferometric phase unwrapping is prone to errors. Here, OSARIS provides a series of modules to detect and mask unwrapping errors, correct for atmospheric disturbances, and remove large-scale trends. Wall clock processing time for the case study (area ~9,000 km2) was ~12 h 4 min on a machine with 400 cores and 2 TB RAM. In total, ~12 d 10 h 44 min (~96%) were saved through parallelization. A comparison of selected OSARIS datasets to results from two state-of-the-art SAR processing suites, ISCE and SNAP, shows that OSARIS provides products of competitive quality despite its high level of automatization. OSARIS thus facilitates efficient S1-based region-wide investigations of surface movement events over multiple years. KW - remote sensing KW - InSAR KW - high mountain environments KW - rock glacier KW - sentinel-1 KW - time series analysis Y1 - 2019 U6 - https://doi.org/10.3389/feart.2019.00172 SN - 2296-6463 VL - 7 PB - Frontiers Media CY - Lausanne ER - TY - JOUR A1 - Runge, Alexandra A1 - Grosse, Guido T1 - Mosaicking Landsat and Sentinel-2 Data to Enhance LandTrendr Time Series Analysis in Northern High Latitude Permafrost Regions JF - Remote Sensing N2 - Permafrost is warming in the northern high latitudes, inducing highly dynamic thaw-related permafrost disturbances across the terrestrial Arctic. Monitoring and tracking of permafrost disturbances is important as they impact surrounding landscapes, ecosystems and infrastructure. Remote sensing provides the means to detect, map, and quantify these changes homogeneously across large regions and time scales. Existing Landsat-based algorithms assess different types of disturbances with similar spatiotemporal requirements. However, Landsat-based analyses are restricted in northern high latitudes due to the long repeat interval and frequent clouds, in particular at Arctic coastal sites. We therefore propose to combine Landsat and Sentinel-2 data for enhanced data coverage and present a combined annual mosaic workflow, expanding currently available algorithms, such as LandTrendr, to achieve more reliable time series analysis. We exemplary test the workflow for twelve sites across the northern high latitudes in Siberia. We assessed the number of images and cloud-free pixels, the spatial mosaic coverage and the mosaic quality with spectral comparisons. The number of available images increased steadily from 1999 to 2019 but especially from 2016 onward with the addition of Sentinel-2 images. Consequently, we have an increased number of cloud-free pixels even under challenging environmental conditions, which then serve as the input to the mosaicking process. In a comparison of annual mosaics, the Landsat+Sentinel-2 mosaics always fully covered the study areas (99.9–100 %), while Landsat-only mosaics contained data-gaps in the same years, only reaching coverage percentages of 27.2 %, 58.1 %, and 69.7 % for Sobo Sise, East Taymyr, and Kurungnakh in 2017, respectively. The spectral comparison of Landsat image, Sentinel-2 image, and Landsat+Sentinel-2 mosaic showed high correlation between the input images and mosaic bands (e.g., for Kurungnakh 0.91–0.97 between Landsat and Landsat+Sentinel-2 mosaic and 0.92–0.98 between Sentinel-2 and Landsat+Sentinel-2 mosaic) across all twelve study sites, testifying good quality mosaic results. Our results show that especially the results for northern, coastal areas was substantially improved with the Landsat+Sentinel-2 mosaics. By combining Landsat and Sentinel-2 data we accomplished to create reliably high spatial resolution input mosaics for time series analyses. Our approach allows to apply a high temporal continuous time series analysis to northern high latitude permafrost regions for the first time, overcoming substantial data gaps, and assess permafrost disturbance dynamics on an annual scale across large regions with algorithms such as LandTrendr by deriving the location, timing and progression of permafrost thaw disturbances KW - time series analysis KW - data fusion KW - disturbance tracking KW - permafrost KW - permafrost thaw Y1 - 2020 U6 - https://doi.org/10.3390/rs12152471 SN - 2072-4292 VL - 12 IS - 15 PB - MDPI CY - Basel ER - TY - GEN A1 - Runge, Alexandra A1 - Grosse, Guido T1 - Mosaicking Landsat and Sentinel-2 Data to Enhance LandTrendr Time Series Analysis in Northern High Latitude Permafrost Regions T2 - Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe N2 - Permafrost is warming in the northern high latitudes, inducing highly dynamic thaw-related permafrost disturbances across the terrestrial Arctic. Monitoring and tracking of permafrost disturbances is important as they impact surrounding landscapes, ecosystems and infrastructure. Remote sensing provides the means to detect, map, and quantify these changes homogeneously across large regions and time scales. Existing Landsat-based algorithms assess different types of disturbances with similar spatiotemporal requirements. However, Landsat-based analyses are restricted in northern high latitudes due to the long repeat interval and frequent clouds, in particular at Arctic coastal sites. We therefore propose to combine Landsat and Sentinel-2 data for enhanced data coverage and present a combined annual mosaic workflow, expanding currently available algorithms, such as LandTrendr, to achieve more reliable time series analysis. We exemplary test the workflow for twelve sites across the northern high latitudes in Siberia. We assessed the number of images and cloud-free pixels, the spatial mosaic coverage and the mosaic quality with spectral comparisons. The number of available images increased steadily from 1999 to 2019 but especially from 2016 onward with the addition of Sentinel-2 images. Consequently, we have an increased number of cloud-free pixels even under challenging environmental conditions, which then serve as the input to the mosaicking process. In a comparison of annual mosaics, the Landsat+Sentinel-2 mosaics always fully covered the study areas (99.9–100 %), while Landsat-only mosaics contained data-gaps in the same years, only reaching coverage percentages of 27.2 %, 58.1 %, and 69.7 % for Sobo Sise, East Taymyr, and Kurungnakh in 2017, respectively. The spectral comparison of Landsat image, Sentinel-2 image, and Landsat+Sentinel-2 mosaic showed high correlation between the input images and mosaic bands (e.g., for Kurungnakh 0.91–0.97 between Landsat and Landsat+Sentinel-2 mosaic and 0.92–0.98 between Sentinel-2 and Landsat+Sentinel-2 mosaic) across all twelve study sites, testifying good quality mosaic results. Our results show that especially the results for northern, coastal areas was substantially improved with the Landsat+Sentinel-2 mosaics. By combining Landsat and Sentinel-2 data we accomplished to create reliably high spatial resolution input mosaics for time series analyses. Our approach allows to apply a high temporal continuous time series analysis to northern high latitude permafrost regions for the first time, overcoming substantial data gaps, and assess permafrost disturbance dynamics on an annual scale across large regions with algorithms such as LandTrendr by deriving the location, timing and progression of permafrost thaw disturbances T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 1009 KW - time series analysis KW - data fusion KW - isturbance tracking KW - permafrost KW - permafrost thaw Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-480317 SN - 1866-8372 IS - 1009 ER - TY - GEN A1 - Morishita, Yu A1 - Lazecky, Milan A1 - Wright, Tim J. A1 - Weiss, Jonathan R. A1 - Elliott, John R. A1 - Hooper, Andy T1 - LiCSBAS BT - An Open-Source InSAR Time Series Analysis Package Integrated with the LiCSAR Automated Sentinel-1 InSAR Processor T2 - Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe N2 - For the past five years, the 2-satellite Sentinel-1 constellation has provided abundant and useful Synthetic Aperture Radar (SAR) data, which have the potential to reveal global ground surface deformation at high spatial and temporal resolutions. However, for most users, fully exploiting the large amount of associated data is challenging, especially over wide areas. To help address this challenge, we have developed LiCSBAS, an open-source SAR interferometry (InSAR) time series analysis package that integrates with the automated Sentinel-1 InSAR processor (LiCSAR). LiCSBAS utilizes freely available LiCSAR products, and users can save processing time and disk space while obtaining the results of InSAR time series analysis. In the LiCSBAS processing scheme, interferograms with many unwrapping errors are automatically identified by loop closure and removed. Reliable time series and velocities are derived with the aid of masking using several noise indices. The easy implementation of atmospheric corrections to reduce noise is achieved with the Generic Atmospheric Correction Online Service for InSAR (GACOS). Using case studies in southern Tohoku and the Echigo Plain, Japan, we demonstrate that LiCSBAS applied to LiCSAR products can detect both large-scale (>100 km) and localized (~km) relative displacements with an accuracy of <1 cm/epoch and ~2 mm/yr. We detect displacements with different temporal characteristics, including linear, periodic, and episodic, in Niigata, Ojiya, and Sanjo City, respectively. LiCSBAS and LiCSAR products facilitate greater exploitation of globally available and abundant SAR datasets and enhance their applications for scientific research and societal benefit. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 1078 KW - InSAR KW - Sentinel-1 KW - time series analysis KW - deformation monitoring KW - tectonics KW - subsidence KW - automatic processing KW - global Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-472431 SN - 1866-8372 IS - 1078 ER - TY - JOUR A1 - Morishita, Yu A1 - Lazecky, Milan A1 - Wright, Tim J. A1 - Weiss, Jonathan R. A1 - Elliott, John R. A1 - Hooper, Andy T1 - LiCSBAS BT - an open-source InSAR time series analysis package integrated with the LiCSAR automated Sentinel-1 InSAR processor JF - Remote sensing N2 - For the past five years, the 2-satellite Sentinel-1 constellation has provided abundant and useful Synthetic Aperture Radar (SAR) data, which have the potential to reveal global ground surface deformation at high spatial and temporal resolutions. However, for most users, fully exploiting the large amount of associated data is challenging, especially over wide areas. To help address this challenge, we have developed LiCSBAS, an open-source SAR interferometry (InSAR) time series analysis package that integrates with the automated Sentinel-1 InSAR processor (LiCSAR). LiCSBAS utilizes freely available LiCSAR products, and users can save processing time and disk space while obtaining the results of InSAR time series analysis. In the LiCSBAS processing scheme, interferograms with many unwrapping errors are automatically identified by loop closure and removed. Reliable time series and velocities are derived with the aid of masking using several noise indices. The easy implementation of atmospheric corrections to reduce noise is achieved with the Generic Atmospheric Correction Online Service for InSAR (GACOS). Using case studies in southern Tohoku and the Echigo Plain, Japan, we demonstrate that LiCSBAS applied to LiCSAR products can detect both large-scale (>100 km) and localized (similar to km) relative displacements with an accuracy of <1 cm/epoch and similar to 2 mm/yr. We detect displacements with different temporal characteristics, including linear, periodic, and episodic, in Niigata, Ojiya, and Sanjo City, respectively. LiCSBAS and LiCSAR products facilitate greater exploitation of globally available and abundant SAR datasets and enhance their applications for scientific research and societal benefit. KW - InSAR KW - Sentinel-1 KW - time series analysis KW - deformation monitoring KW - tectonics KW - subsidence KW - automatic processing KW - global Y1 - 2020 U6 - https://doi.org/10.3390/rs12030424 SN - 2072-4292 VL - 12 IS - 3 PB - MDPI CY - Basel ER - TY - JOUR A1 - Berner, Nadine A1 - Trauth, Martin H. A1 - Holschneider, Matthias T1 - Bayesian inference about Plio-Pleistocene climate transitions in Africa JF - Quaternary science reviews : the international multidisciplinary research and review journal N2 - During the last 5 Ma the Earth's ocean-atmosphere system passed through several major transitions, many of which are discussed as possible triggers for human evolution. A classic in this context is the possible influence of the closure of the Panama Strait, the intensification of Northern Hemisphere Glaciation, a stepwise increase in aridity in Africa, and the first appearance of the genus Homo about 2.5 - 2.7 Ma ago. Apart from the fact that the correlation between these events does not necessarily imply causality, many attempts to establish a relationship between climate and evolution fail due to the challenge of precisely localizing an a priori unknown number of changes potentially underlying complex climate records. The kernel-based Bayesian inference approach applied here allows inferring the location, generic shape, and temporal scale of multiple transitions in established records of Plio-Pleistocene African climate. By defining a transparent probabilistic analysis strategy, we are able to identify conjoint changes occurring across the investigated terrigenous dust records from Ocean Drilling Programme (ODP) sites in the Atlantic Ocean (ODP 659), Arabian (ODP 721/722) and Mediterranean Sea (ODP 967). The study indicates a two-step transition in the African climate proxy records at (2.35-2.10) Ma and (1.70 - 1.50) Ma, that may be associated with the reorganization of the Hadley-Walker Circulation. . KW - Plio-Pleistocene KW - Hadley-Walker Circulation KW - climate transition KW - Bayesian inference KW - time series analysis KW - ODP 659 KW - ODP 721/722 KW - ODP 967 Y1 - 2022 U6 - https://doi.org/10.1016/j.quascirev.2021.107287 SN - 0277-3791 SN - 1873-457X VL - 277 PB - Elsevier CY - Oxford ER -