@phdthesis{Schwabedal2010,
  author    = {Schwabedal, Justus Tilmann Caspar},
  title     = {Phase dynamics of irregular oscillations},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-50115},
  school      = {Universit{\"a}t Potsdam},
  year      = {2010},
  abstract  = {In der vorliegenden Dissertation wird eine Beschreibung der Phasendynamik irregul{\"a}rer Oszillationen und deren Wechselwirkungen vorgestellt. Hierbei werden chaotische und stochastische Oszillationen autonomer dissipativer Systeme betrachtet. F{\"u}r eine Phasenbeschreibung stochastischer Oszillationen m{\"u}ssen zum einen unterschiedliche Werte der Phase zueinander in Beziehung gesetzt werden, um ihre Dynamik unabh{\"a}ngig von der gew{\"a}hlten Parametrisierung der Oszillation beschreiben zu k{\"o}nnen. Zum anderen m{\"u}ssen f{\"u}r stochastische und chaotische Oszillationen diejenigen Systemzust{\"a}nde identifiziert werden, die sich in der gleichen Phase befinden. Im Rahmen dieser Dissertation werden die Werte der Phase {\"u}ber eine gemittelte Phasengeschwindigkeitsfunktion miteinander in Beziehung gesetzt. F{\"u}r stochastische Oszillationen sind jedoch verschiedene Definitionen der mittleren Geschwindigkeit m{\"o}glich. 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 zweckm{\"a}ßige Phasenbeziehung her. Wie anhand einfacher Beispiele erkl{\"a}rt wird, kann so die Theorie der effektiven Phasendynamik auch kontinuierlich und pulsartig wechselwirkende stochastische Oszillationen beschreiben. Weiterhin wird ein Kriterium f{\"u}r die invariante Identifikation von Zust{\"a}nden gleicher Phase irregul{\"a}rer Oszillationen zu sogenannten generalisierten Isophasen beschrieben: Die Zust{\"a}nde einer solchen Isophase sollen in ihrer dynamischen Entwicklung ununterscheidbar werden. F{\"u}r 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 Sch{\"a}tzung der Isophasen aus Daten wird die Anwendbarkeit der Theorie anhand eines Signals regelm{\"a}ßiger Atmung gezeigt. Weiterhin zeigt sich, dass das Kriterium der Phasenidentifikation f{\"u}r chaotische Oszillationen nur approximativ erf{\"u}llt werden kann. Anhand des R{\"o}ssleroszillators wird der tiefgreifende Zusammenhang zwischen approximativen Isophasen, chaotischer Phasendiffusion und instabilen periodischen Orbits dargelegt. Gemeinsam erm{\"o}glichen die Theorien der effektiven Phasendynamik und der generalisierten Isophasen eine umfassende und einheitliche Phasenbeschreibung irregul{\"a}rer Oszillationen.},
  language  = {de}
}
@misc{Marwan2011,
  author    = {Marwan, Norbert},
  title     = {How to avoid potential pitfalls in recurrence plot based data analysis},
  series = {International journal of bifurcation and chaos : in applied sciences and engineering},
  volume    = {21},
  journal   = {International journal of bifurcation and chaos : in applied sciences and engineering},
  number    = {4},
  publisher = {World Scientific},
  address   = {Singapore},
  issn      = {0218-1274},
  doi       = {10.1142/S0218127411029008},
  pages     = {1003 -- 1017},
  year      = {2011},
  abstract  = {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.},
  language  = {en}
}
@article{CostaBronstertKneis2012,
  author    = {Costa, Alexandre Cunha and Bronstert, Axel and Kneis, David},
  title     = {Probabilistic flood forecasting for a mountainous headwater catchment using a nonparametric stochastic dynamic approach},
  series = {Hydrological sciences journal = Journal des sciences hydrologiques},
  volume    = {57},
  journal   = {Hydrological sciences journal = Journal des sciences hydrologiques},
  number    = {1},
  publisher = {Routledge, Taylor \& Francis Group},
  address   = {Abingdon},
  issn      = {0262-6667},
  doi       = {10.1080/02626667.2011.637043},
  pages     = {10 -- 25},
  year      = {2012},
  abstract  = {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.},
  language  = {en}
}
@phdthesis{Kuetter2015,
  author    = {K{\"u}tter, Sissy},
  title     = {Magnetotelluric measurements across the southern Barberton Greenstone Belt, South Africa},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-83198},
  school      = {Universit{\"a}t Potsdam},
  pages     = {xix, 156},
  year      = {2015},
  abstract  = {Der Barberton Gr{\"u}nsteing{\"u}rtel (BGB) in S{\"u}dafrika geh{\"o}rt 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. {\"U}ber die tiefere Struktur des BGB ist hingegen wenig bekannt. Zahlreiche Evolutionsmodelle, die auf Altersbestimmungsdaten und strukturellen Informationen beruhen wurden {\"u}ber die Jahre aufgestellt. Diese Theorien sind zumeist widerspr{\"u}chlich. Sie konzentrieren sich im Wesentlichen auf die Frage, ob plattentektonische Prozesse bereits bei der Entwicklung der fr{\"u}hen Erde eine Rolle spielten oder ob vertikale Tektonik, angetrieben durch die im Archaikum h{\"o}heren Temperaturen, die Bildung der heutigen Kontinente bestimmt hat. Um neue Erkenntnisse {\"u}ber die interne Struktur und Entwicklungsgeschichte des BGB zu erhalten, wurden im Rahmen der Deutsch-S{\"u}dafrikanischen Forschungsinitiative Inkaba yeAfrica magnetotellurische (MT) Messungen durchgef{\"u}hrt. Entlang von sechs Profilen, die den gesamten s{\"u}dlichen Teil des BGB's {\"u}berdecken, wurden nahezu 200 MT-Stationen installiert. Tektonische Strukturen wie z. B. (fossile) Verwerfungszonen k{\"o}nnen erh{\"o}hte Leitf{\"a}higigkeiten haben, wenn sich leitf{\"a}hige Mineralisationen innerhalb der Scherzonen gebildet haben. Durch die Abbildung der elektrischen Leitf{\"a}higkeitsverteilung des Untergrundes mit Hilfe von MT Messungen kann der Verlauf tektonischer Strukturen nachvollzogen werden, woraus Schl{\"u}sse {\"u}ber m{\"o}glicherweise abgelaufene tektonische Prozesse gezogen werden k{\"o}nnen. Der gesamte MT Datensatz weist starke St{\"o}reinfl{\"u}sse durch k{\"u}nstliche elektromagnetische Signale auf, die bspw. von Stromleitungen und elektrischen Z{\"a}unen stammen. Insbesondere langperiodische Daten (>1 s) sind davon betroffen, die f{\"u}r die Aufl{\"o}sung tieferer Strukturen notwendig sind. Die Anwendung etablierter Ans{\"a}tze wie Verschiebungsfiltern und der Remote Reference-Methode, f{\"u}hrte zu Verbesserungen vorrangig f{\"u}r Perioden < 1 s. Der langperiodische Bereich ist durch impulsartige St{\"o}rsignale in den magnetischen und dazugeh{\"o}rigen Stufen in den elektrischen Feldkomponenten gepr{\"a}gt. Im Rahmen dieser Arbeit wurde ein neuartiger Zeitbereichs-Filter entwickelt, welcher auf einer abgewandelten Form des Wiener Filters beruht und diese Art von St{\"o}rsignalen aus den Daten entfernt. Durch den Vergleich der Datenvarianz einer lokalen Station mit der einer Referenzstation k{\"o}nnen gest{\"o}rte Zeitsegmente identifiziert werden. Anschließend wird ein Wiener-Filter-Algorithmus angewendet, um f{\"u}r diese Segmente mithilfe der Referenzdaten physikalisch sinnvolle Zeitreihen zu berechnen, mit denen die Daten der lokalen Station ersetzt werden. W{\"a}hrend impulsartige St{\"o}rsignale in den magnetischen Datenkan{\"a}len relativ einfach erfasst werden k{\"o}nnen, ist die Detektion von Vers{\"a}tzen in den elektrischen Zeitreihen je nach Versatzh{\"o}he 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 St{\"o}rsignale durch den Vergleich des kurzzeitigen und des langzeitigen Datenmittelwerts ausfindig gemacht. Bei diesem Filter werden die St{\"o}rsignale aus den Zeitreihen entfernt und durch eine lineare Interpolation ersetzt. Durch die beiden Filtermethoden wurde eine deutliche Verbesserung der Datenqualit{\"a}t bis zu 10 und teilweise 100 s erreicht. Zur Interpretation des MT-Datensatzes wurden 2D und 3D Inversionen durchgef{\"u}hrt. Die so erhaltenen elektrischen Leitf{\"a}higkeitsmodelle zeigen eine gute {\"U}bereinstimmung mit den kartierten, geologischen Strukturen. Die Gesteine des BGB weisen in den Modellen hohe Widerst{\"a}nde auf und sind deutlich von leitf{\"a}higen benachbarten geologischen Strukturen abgegrenzt. Verwerfungszonen korrelieren mit leitf{\"a}higen Strukturen, die sich bis in eine Tiefe von 5 bis 10 km erstrecken. Eine Fortsetzung der Verwerfungszonen {\"u}ber die s{\"u}dliche 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.},
  language  = {en}
}
@phdthesis{Hohenbrink2016,
  author    = {Hohenbrink, Tobias Ludwig},
  title     = {Turning a problem into a solution: heterogeneities in soil hydrology},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-101485},
  school      = {Universit{\"a}t Potsdam},
  pages     = {x, 123},
  year      = {2016},
  abstract  = {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.},
  language  = {en}
}
@phdthesis{Schmidt2017,
  author    = {Schmidt, Silke Regina},
  title     = {Analyzing lakes in the time frequency domain},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-406955},
  school      = {Universit{\"a}t Potsdam},
  pages     = {VIII, 126},
  year      = {2017},
  abstract  = {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.},
  language  = {en}
}
@article{RosenbaumRaatzWeithoffetal.2019,
  author    = {Rosenbaum, Benjamin and Raatz, Michael and Weithoff, Guntram and Fussmann, Gregor F. and Gaedke, Ursula},
  title     = {Estimating parameters from multiple time series of population dynamics using bayesian inference},
  series = {Frontiers in ecology and evolution},
  volume    = {6},
  journal   = {Frontiers in ecology and evolution},
  publisher = {Frontiers Research Foundation},
  address   = {Lausanne},
  issn      = {2296-701X},
  doi       = {10.3389/fevo.2018.00234},
  pages     = {14},
  year      = {2019},
  abstract  = {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.},
  language  = {en}
}
@article{SelleKnorrLischeid2019,
  author    = {Selle, Benny and Knorr, Klaus-Holger and Lischeid, Gunnar},
  title     = {Mobilisation and transport of dissolved organic carbon and iron in peat catchments-Insights from the Lehstenbach stream in Germany using generalised additive models},
  series = {Hydrological processes},
  volume    = {33},
  journal   = {Hydrological processes},
  number    = {25},
  publisher = {Wiley},
  address   = {Hoboken},
  issn      = {0885-6087},
  doi       = {10.1002/hyp.13552},
  pages     = {3213 -- 3225},
  year      = {2019},
  abstract  = {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.},
  language  = {en}
}
@article{PennekampIlesGarlandetal.2019,
  author    = {Pennekamp, Frank and Iles, Alison C. and Garland, Joshua and Brennan, Georgina and Brose, Ulrich and Gaedke, Ursula and Jacob, Ute and Kratina, Pavel and Matthews, Blake and Munch, Stephan and Novak, Mark and Palamara, Gian Marco and Rall, Bjorn C. and Rosenbaum, Benjamin and Tabi, Andrea and Ward, Colette and Williams, Richard and Ye, Hao and Petchey, Owen L.},
  title     = {The intrinsic predictability of ecological time series and its potential to guide forecasting},
  series = {Ecological monographs : a publication of the Ecological Society of America.},
  volume    = {89},
  journal   = {Ecological monographs : a publication of the Ecological Society of America.},
  number    = {2},
  publisher = {Wiley},
  address   = {Hoboken},
  issn      = {0012-9615},
  doi       = {10.1002/ecm.1359},
  pages     = {17},
  year      = {2019},
  language  = {en}
}
@misc{ŚlęzakBurneckiMetzler2019,
  author    = {Ślęzak, Jakub and Burnecki, Krzysztof and Metzler, Ralf},
  title     = {Random coefficient autoregressive processes describe Brownian yet non-Gaussian diffusion in heterogeneous systems},
  series = {Postprints der Universit{\"a}t Potsdam Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Postprints der Universit{\"a}t Potsdam Mathematisch-Naturwissenschaftliche Reihe},
  number    = {765},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-43792},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-437923},
  pages     = {18},
  year      = {2019},
  abstract  = {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.},
  language  = {en}
}
@article{ŚlęzakBurneckiMetzler2019,
  author    = {Ślęzak, Jakub and Burnecki, Krzysztof and Metzler, Ralf},
  title     = {Random coefficient autoregressive processes describe Brownian yet non-Gaussian diffusion in heterogeneous systems},
  series = {New Journal of Physics},
  volume    = {21},
  journal   = {New Journal of Physics},
  publisher = {Deutsche Physikalische Gesellschaft ; IOP, Institute of Physics},
  address   = {Bad Honnef und London},
  issn      = {1367-2630},
  doi       = {10.1088/1367-2630/ab3366},
  pages     = {18},
  year      = {2019},
  abstract  = {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.},
  language  = {en}
}
@article{LoiblBookhagenValadeetal.2019,
  author    = {Loibl, David and Bookhagen, Bodo and Valade, Sebastien and Schneider, Christoph},
  title     = {OSARIS, the "Open Source SAR Investigation System" for Automatized Parallel InSAR Processing of Sentinel-1 Time Series Data With Special Emphasis on Cryosphere Applications},
  series = {Frontiers in Earth Science},
  volume    = {7},
  journal   = {Frontiers in Earth Science},
  publisher = {Frontiers Media},
  address   = {Lausanne},
  issn      = {2296-6463},
  doi       = {10.3389/feart.2019.00172},
  pages     = {20},
  year      = {2019},
  abstract  = {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.},
  language  = {en}
}
@article{RungeGrosse2020,
  author    = {Runge, Alexandra and Grosse, Guido},
  title     = {Mosaicking Landsat and Sentinel-2 Data to Enhance LandTrendr Time Series Analysis in Northern High Latitude Permafrost Regions},
  series = {Remote Sensing},
  volume    = {12},
  journal   = {Remote Sensing},
  number    = {15},
  publisher = {MDPI},
  address   = {Basel},
  issn      = {2072-4292},
  doi       = {10.3390/rs12152471},
  pages     = {23},
  year      = {2020},
  abstract  = {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},
  language  = {en}
}
@misc{RungeGrosse2020,
  author    = {Runge, Alexandra and Grosse, Guido},
  title     = {Mosaicking Landsat and Sentinel-2 Data to Enhance LandTrendr Time Series Analysis in Northern High Latitude Permafrost Regions},
  series = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  number    = {1009},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-48031},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-480317},
  pages     = {25},
  year      = {2020},
  abstract  = {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},
  language  = {en}
}
@misc{MorishitaLazeckyWrightetal.2020,
  author    = {Morishita, Yu and Lazecky, Milan and Wright, Tim J. and Weiss, Jonathan R. and Elliott, John R. and Hooper, Andy},
  title     = {LiCSBAS},
  series = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  number    = {1078},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-47243},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-472431},
  pages     = {31},
  year      = {2020},
  abstract  = {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.},
  language  = {en}
}
@article{MorishitaLazeckyWrightetal.2020,
  author    = {Morishita, Yu and Lazecky, Milan and Wright, Tim J. and Weiss, Jonathan R. and Elliott, John R. and Hooper, Andy},
  title     = {LiCSBAS},
  series = {Remote sensing},
  volume    = {12},
  journal   = {Remote sensing},
  number    = {3},
  publisher = {MDPI},
  address   = {Basel},
  issn      = {2072-4292},
  doi       = {10.3390/rs12030424},
  pages     = {29},
  year      = {2020},
  abstract  = {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.},
  language  = {en}
}
@article{BernerTrauthHolschneider2022,
  author    = {Berner, Nadine and Trauth, Martin H. and Holschneider, Matthias},
  title     = {Bayesian inference about Plio-Pleistocene climate transitions in Africa},
  series = {Quaternary science reviews : the international multidisciplinary research and review journal},
  volume    = {277},
  journal   = {Quaternary science reviews : the international multidisciplinary research and review journal},
  publisher = {Elsevier},
  address   = {Oxford},
  issn      = {0277-3791},
  doi       = {10.1016/j.quascirev.2021.107287},
  pages     = {12},
  year      = {2022},
  abstract  = {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. .},
  language  = {en}
}