TY - GEN A1 - Nord, Guillaume A1 - Boudevillain, Brice A1 - Berne, Alexis A1 - Branger, Flora A1 - Braud, Isabelle A1 - Dramais, Guillaume A1 - Gérard, Simon A1 - Le Coz, Jérôme A1 - Legoût, Cédric A1 - Molinié, Gilles A1 - Van Baelen, Joel A1 - Vandervaere, Jean-Pierre A1 - Andrieu, Julien A1 - Aubert, Coralie A1 - Calianno, Martin A1 - Delrieu, Guy A1 - Grazioli, Jacopo A1 - Hachani, Sahar A1 - Horner, Ivan A1 - Huza, Jessica A1 - Le Boursicaud, Raphaël A1 - Raupach, Timothy H. A1 - Teuling, Adriaan J. A1 - Uber, Magdalena A1 - Vincendon, Béatrice A1 - Wijbrans, Annette T1 - A high space–time resolution dataset linking meteorological forcing and hydro-sedimentary response in a mesoscale Mediterranean catchment (Auzon) of the Ardèche region, France T2 - Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe N2 - A comprehensive hydrometeorological dataset is presented spanning the period 1 January 201131 December 2014 to improve the understanding of the hydrological processes leading to flash floods and the relation between rainfall, runoff, erosion and sediment transport in a mesoscale catchment (Auzon, 116 km(2)) of the Mediterranean region. Badlands are present in the Auzon catchment and well connected to high-gradient channels of bedrock rivers which promotes the transfer of suspended solids downstream. The number of observed variables, the various sensors involved (both in situ and remote) and the space-time resolution (similar to km(2), similar to min) of this comprehensive dataset make it a unique contribution to research communities focused on hydrometeorology, surface hydrology and erosion. Given that rainfall is highly variable in space and time in this region, the observation system enables assessment of the hydrological response to rainfall fields. Indeed, (i) rainfall data are provided by rain gauges (both a research network of 21 rain gauges with a 5 min time step and an operational network of 10 rain gauges with a 5 min or 1 h time step), S-band Doppler dual-polarization radars (1 km(2), 5 min resolution), disdrometers (16 sensors working at 30 s or 1 min time step) and Micro Rain Radars (5 sensors, 100m height resolution). Additionally, during the special observation period (SOP-1) of the HyMeX (Hydrological Cycle in the Mediterranean Experiment) project, two X-band radars provided precipitation measurements at very fine spatial and temporal scales (1 ha, 5 min). (ii) Other meteorological data are taken from the operational surface weather observation stations of Meteo-France (including 2m air temperature, atmospheric pressure, 2 m relative humidity, 10m wind speed and direction, global radiation) at the hourly time resolution (six stations in the region of interest). (iii) The monitoring of surface hydrology and suspended sediment is multi-scale and based on nested catchments. Three hydrometric stations estimate water discharge at a 2-10 min time resolution. Two of these stations also measure additional physico-chemical variables (turbidity, temperature, conductivity) and water samples are collected automatically during floods, allowing further geochemical characterization of water and suspended solids. Two experimental plots monitor overland flow and erosion at 1 min time resolution on a hillslope with vineyard. A network of 11 sensors installed in the intermittent hydrographic network continuously measures water level and water temperature in headwater subcatchments (from 0.17 to 116 km(2)) at a time resolution of 2-5 min. A network of soil moisture sensors enables the continuous measurement of soil volumetric water content at 20 min time resolution at 9 sites. Additionally, concomitant observations (soil moisture measurements and stream gauging) were performed during floods between 2012 and 2014. Finally, this dataset is considered appropriate for understanding the rainfall variability in time and space at fine scales, improving areal rainfall estimations and progressing in distributed hydrological and erosion modelling. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 671 KW - hydraulic properties KW - soil moisture KW - flash floods KW - rainfall KW - radar KW - scale KW - variability KW - transport KW - erosion KW - model Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-419127 SN - 1866-8372 IS - 671 ER - TY - GEN A1 - Molnos, Sonja A1 - Mamdouh, Tarek A1 - Petri, Stefan A1 - Nocke, Thomas A1 - Weinkauf, Tino A1 - Coumou, Dim T1 - A network-based detection scheme for the jet stream core T2 - Postprints der Universität Potsdam : Mathematisch Naturwissenschaftliche Reihe N2 - The polar and subtropical jet streams are strong upper-level winds with a crucial influence on weather throughout the Northern Hemisphere midlatitudes. In particular, the polar jet is located between cold arctic air to the north and warmer subtropical air to the south. Strongly meandering states therefore often lead to extreme surface weather. Some algorithms exist which can detect the 2-D (latitude and longitude) jets' core around the hemisphere, but all of them use a minimal threshold to determine the subtropical and polar jet stream. This is particularly problematic for the polar jet stream, whose wind velocities can change rapidly from very weak to very high values and vice versa. We develop a network-based scheme using Dijkstra's shortest-path algorithm to detect the polar and subtropical jet stream core. This algorithm not only considers the commonly used wind strength for core detection but also takes wind direction and climatological latitudinal position into account. Furthermore, it distinguishes between polar and subtropical jet, and between separate and merged jet states. The parameter values of the detection scheme are optimized using simulated annealing and a skill function that accounts for the zonal-mean jet stream position (Rikus, 2015). After the successful optimization process, we apply our scheme to reanalysis data covering 1979-2015 and calculate seasonal-mean probabilistic maps and trends in wind strength and position of jet streams. We present longitudinally defined probability distributions of the positions for both jets for all on the Northern Hemisphere seasons. This shows that winter is characterized by two well-separated jets over Europe and Asia (ca. 20 degrees W to 140 degrees E). In contrast, summer normally has a single merged jet over the western hemisphere but can have both merged and separated jet states in the eastern hemisphere. With this algorithm it is possible to investigate the position of the jets' cores around the hemisphere and it is therefore very suitable to analyze jet stream patterns in observations and models, enabling more advanced model-validation. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 625 KW - Southern-Hemisphere KW - variability KW - weather KW - driven KW - amplification KW - circulation KW - reanalysis KW - extremes KW - climate KW - summer Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-419099 SN - 1866-8372 IS - 625 SP - 75 EP - 89 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 - GEN A1 - Acevedo, Walter A1 - Fallah, Bijan A1 - Reich, Sebastian A1 - Cubasch, Ulrich T1 - Assimilation of pseudo-tree-ring-width observations into an atmospheric general circulation model T2 - Postprints der Universität Potsdam : Mathematisch Naturwissenschaftliche Reihe N2 - Paleoclimate data assimilation (DA) is a promising technique to systematically combine the information from climate model simulations and proxy records. Here, we investigate the assimilation of tree-ring-width (TRW) chronologies into an atmospheric global climate model using ensemble Kalman filter (EnKF) techniques and a process-based tree-growth forward model as an observation operator. Our results, within a perfect-model experiment setting, indicate that the "online DA" approach did not outperform the "off-line" one, despite its considerable additional implementation complexity. On the other hand, it was observed that the nonlinear response of tree growth to surface temperature and soil moisture does deteriorate the operation of the time-averaged EnKF methodology. Moreover, for the first time we show that this skill loss appears significantly sensitive to the structure of the growth rate function, used to represent the principle of limiting factors (PLF) within the forward model. In general, our experiments showed that the error reduction achieved by assimilating pseudo-TRW chronologies is modulated by the magnitude of the yearly internal variability in themodel. This result might help the dendrochronology community to optimize their sampling efforts. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 627 KW - high resolution paleoclimatology KW - sparse proxy data KW - climate reconstructions KW - limiting factors KW - Kalman filter KW - co-limitation KW - ensemble KW - variability KW - reanalysis KW - framework Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-418743 SN - 1866-8372 IS - 627 SP - 545 EP - 557 ER - TY - GEN A1 - Seibert, Mathias A1 - Merz, Bruno A1 - Apel, Heiko T1 - Seasonal forecasting of hydrological drought in the Limpopo Basin BT - a comparison of statistical methods T2 - Postprints der Universität Potsdam : Mathematisch Naturwissenschaftliche Reihe N2 - The Limpopo Basin in southern Africa is prone to droughts which affect the livelihood of millions of people in South Africa, Botswana, Zimbabwe and Mozambique. Seasonal drought early warning is thus vital for the whole region. In this study, the predictability of hydrological droughts during the main runoff period from December to May is assessed using statistical approaches. Three methods (multiple linear models, artificial neural networks, random forest regression trees) are compared in terms of their ability to forecast streamflow with up to 12 months of lead time. The following four main findings result from the study. 1. There are stations in the basin at which standardised streamflow is predictable with lead times up to 12 months. The results show high inter-station differences of forecast skill but reach a coefficient of determination as high as 0.73 (cross validated). 2. A large range of potential predictors is considered in this study, comprising well-established climate indices, customised teleconnection indices derived from sea surface temperatures and antecedent streamflow as a proxy of catchment conditions. El Nino and customised indices, representing sea surface temperature in the Atlantic and Indian oceans, prove to be important teleconnection predictors for the region. Antecedent streamflow is a strong predictor in small catchments (with median 42% explained variance), whereas teleconnections exert a stronger influence in large catchments. 3. Multiple linear models show the best forecast skill in this study and the greatest robustness compared to artificial neural networks and random forest regression trees, despite their capabilities to represent nonlinear relationships. 4. Employed in early warning, the models can be used to forecast a specific drought level. Even if the coefficient of determination is low, the forecast models have a skill better than a climatological forecast, which is shown by analysis of receiver operating characteristics (ROCs). Seasonal statistical forecasts in the Limpopo show promising results, and thus it is recommended to employ them as complementary to existing forecasts in order to strengthen preparedness for droughts. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 626 KW - sea-surface temperature KW - southern Africa KW - neural-network KW - summer rainfall KW - Atlantic-Ocean KW - river-basin KW - predictability KW - variability KW - prediction KW - climate Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-418442 IS - 626 SP - 1611 EP - 1629 ER -