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  • 1
    Series available for loan
    Series available for loan
    Hannover : Fachrichtung Geodäsie und Geoinformatik der Leibniz Unviersität Hannover
    Associated volumes
    Call number: S 99.0139(395)
    In: Wissenschaftliche Arbeiten der Fachrichtung Geodäsie und Geoinformatik der Leibniz Universität Hannover, Nr. 395
    Description / Table of Contents: Die hochgenaue, geometrische Erfassung von Objekten und deren Umfeld mit geodätischen Messsystemen wie Lasertrackern und 3D Laserscannern wird bereits seit einigen Jahren durchgeführt. Bei langgezogenen Profilen, z. B. Führungs-, Fahr-, und Leitschienen, mit Längen von bis zu mehreren hundert Metern, wie sie bei Kranbahnen oder Hochregallagern vorkommen, ist bisher eine punktuelle, linienhafte Erfassung üblich. Aus den Messdaten werden Zustandsgrößen abgeleitet, die in Richtlinien, wie z. B. der VDI 3576 beschrieben sind. Zur Reduzierung der Absturzgefahr beim Signalisieren hochliegender Schienenprofile und zur Beschleunigung des Messprozesses, können motorisierte Plattformen für den Transport von Reflektoren eingesetzt werden. Es wird ein Bewegungs- und Auswertemodell für ein mit hoher Abtastrate messendes kinematisches System erarbeitet, so dass die tatsächliche Lage von Führungs-, Fahr-, und Leitschienen mit einer Unsicherheit im Submillimeterbereich bestimmt werden kann. Damit die Messung für die Praxis relevant wird, können die Ergebnisse unmittelbar ausgewertet werden. Aus den Messdaten lassen sich für eine objektive Beurteilung des Zustands von Profilen und Befestigungen folgende Zustandsparameter ableiten: Lage, Z-Werte, Neigung und Zustand der Schiene und deren Befestigung. Die Qualität der Messungen und Zustandsparameter lässt sich qualitätsgesichert durch Auflösung und Standardabweichung nachweisen.
    Description / Table of Contents: The high-precision, geometric capture of objects and their surroundings with geodetic measurement systems such as laser trackers and 3D laser scanners has already been carried out for several years. In the case of elongated profiles, e.g. guide rails, carriage rails and guard rails, with lengths of up to several hundred meters, such as those found in crane runways or high-bay warehouses, a point-by-point, line-by-line recording has been common practice up to now. Condition variables are derived from the measurement data, which are described in guidelines such as VDI 3576. To reduce the risk of falling when signaling high-lying profiles and to speed up the measurement process, motorized platforms can be used to transport reflectors. A motion and evaluation model for a kinematic system measuring at a high sampling rate will be developed, so that the actual position of guide rails can be determined with an uncertainty in the submillimeter range. To make the measurement relevant for practical applications, the results can be evaluated immediately.
    Type of Medium: Series available for loan
    Pages: 158 Seiten , Illustrationen, Tabellen, Diagramme , 30 cm
    ISSN: 01741454
    Series Statement: Wissenschaftliche Arbeiten der Fachrichtung Geodäsie und Geoinformatik der Leibniz Universität Hannover Nr. 395
    Language: German
    Note: Dissertation, Gottfried Wilhelm Leibniz Universität Hannover, 2024 , Abkürzungsverzeichnis ix 1 Einleitung 1 1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Zielsetzung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.3 Aufbau der Arbeit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 2 Stand der Technik im Bereich der Vermessung von Schienenanlagen der Intralogistik 5 2.1 Elemente von Schienenanlagen der Intralogistik . . . . . . . . . . . . . . . . . . . . . 6 2.1.1 Schienen und Profilstähle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.1.2 Schienenlagerungssysteme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.1.3 Schienenstöße, Festpunkte, Endbegrenzer, An- und Einbauten . . . . . . . . . 11 2.1.4 Schienengebundene Krane . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.2 Rechtlicher Rahmen, Richtlinien und klassische Zustandsgrößen . . . . . . . . . . . . 16 2.2.1 Rechtlicher Rahmen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 2.2.2 Richtlinien . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 2.2.3 Klassische Zustandsgrößen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 2.2.4 Kritische Betrachtung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 2.3 Vermessung von Schienenanlagen der Intralogistik . . . . . . . . . . . . . . . . . . . 19 2.3.1 Koordinatensystem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 2.3.2 Vermessung mit Theodolit, Bandmaß und Nivellier . . . . . . . . . . . . . . . 21 2.3.3 Alignierverfahren mit Laser . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 2.3.4 Vermessung mit Tachymeter oder Lasertracker . . . . . . . . . . . . . . . . . 25 2.3.5 Automatisierte Systeme mit georeferenzierendem Sensor . . . . . . . . . . . . 25 3 Grundlagen zur Bestimmung der geometrischen Zustandsgrößen von Profilen 31 3.1 Rekursive Filterung im Zustandsraum . . . . . . . . . . . . . . . . . . . . . . . . . . 31 3.1.1 Wahrscheinlichkeiten, Satz von Bayes, Verteilungen . . . . . . . . . . . . . . . 31 3.1.2 Bayes Filter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 3.1.3 Kalman Filter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 3.1.4 Extended Kalman Filter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 3.1.5 Unscented Kalman Filter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 3.1.6 Unscented Rauch Tung Striebel Smoother . . . . . . . . . . . . . . . . . . . . 39 3.1.7 Fazit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 3.2 Geometrische Modellierung von Kurven . . . . . . . . . . . . . . . . . . . . . . . . . 41 3.2.1 Polynome . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 3.2.2 Splines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 3.2.3 B-Splines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 4 Profilvermessungssystem 49 4.1 Neue Zustandsgrößen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 4.2 Sensorik . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 4.2.1 Georeferenzierender Sensor . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 4.2.2 Profillaserscanner . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 4.2.3 Kameras . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 xii Inhaltsverzeichnis 4.2.4 Inklinometer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 4.2.5 Inertiale Messeinheit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 4.2.6 Encoder . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 4.2.7 Ultraschallsensoren . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 4.2.8 Sensorintegration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 4.3 Profilvermessungssystem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 4.3.1 Plattform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 4.3.2 Antriebseinheit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 4.3.3 Seitenführung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 4.3.4 Schwingen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 4.3.5 Halterung Sensorik . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 4.3.6 Drehvorrichtung für Reflektor . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 4.4 Erreichbare Messunsicherheiten . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 4.5 Datensynchronisierung und Datenhaltungskonzept . . . . . . . . . . . . . . . . . . . 65 4.5.1 Anforderung an die Synchronisierung . . . . . . . . . . . . . . . . . . . . . . . 66 4.5.2 Synchronisierung über die Zeit . . . . . . . . . . . . . . . . . . . . . . . . . . 68 4.5.3 Synchronisierung im Objektraum . . . . . . . . . . . . . . . . . . . . . . . . . 70 4.5.4 Datenhaltungskonzept . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 4.6 Kalibrierung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 4.6.1 Komponentenkalibrierung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 4.6.2 Systemkalibrierung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 5 Zustandsgrößen einer überarbeiteten VDI 3576 83 5.1 Messdatenerfassung und -aufbereitung . . . . . . . . . . . . . . . . . . . . . . . . . . 83 5.1.1 Messdatenerfassung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83 5.1.2 Orientierungsparameter aus Positionsdaten . . . . . . . . . . . . . . . . . . . 83 5.1.3 Aufbereitung der Lasertracker- oder Tachymeterdaten . . . . . . . . . . . . . 86 5.1.4 Korrektur der Beschleunigungswerte von der Erdschwere . . . . . . . . . . . . 88 5.1.5 Korrektur der Inklinometermesswerte von Beschleunigungseinflüssen . . . . . 89 5.1.6 Korrektur der Längs- und Querablage . . . . . . . . . . . . . . . . . . . . . . 89 5.2 Sensorfusion für die Georeferenzierung des Profilmesswagens . . . . . . . . . . . . . . 89 5.2.1 Quaternionen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91 5.2.2 Adaptive Filterung der Inertial Measurment Unit (IMU)-Messwerte . . . . . 92 5.2.3 Funktionales Modell . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94 5.2.4 Stochastisches Modell . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 5.2.5 Steigerung der Zuverlässigkeit der Filterung . . . . . . . . . . . . . . . . . . . 99 6 Testmessung und Validierung des kinematischen Multisensorsystems 101 6.1 Durchführung einer kinematischen Schienenmessung mit dem Profilvermessungssystem101 6.2 Qualitätssicherung des Messprozesses . . . . . . . . . . . . . . . . . . . . . . . . . . . 102 6.3 Messkampagne I: Messung unter Laborbedingungen . . . . . . . . . . . . . . . . . . 102 6.3.1 Auswertung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104 6.3.2 Einfacher Ansatz zum Finden weiterer Zustandsgrößen . . . . . . . . . . . . . 111 6.3.3 Zusammenfassung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112 6.4 Messkampagne II: Messung unter realen Bedingungen . . . . . . . . . . . . . . . . . 113 6.4.1 Messumgebung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113 6.4.2 Messkonzept und Netzplanung . . . . . . . . . . . . . . . . . . . . . . . . . . 113 6.4.3 Ergebnisse und Bewertung der Netzmessung . . . . . . . . . . . . . . . . . . . 115 6.5 Qualitätsaussagen zu dem Profilvermessungssystem . . . . . . . . . . . . . . . . . . . 116 6.5.1 Bewertung der Kalibrierparameter . . . . . . . . . . . . . . . . . . . . . . . . 116 6.5.
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  • 2
    Series available for loan
    Series available for loan
    Hannover : Fachrichtung Geodäsie und Geoinformatik der Leibniz-Universität Hannover
    Associated volumes
    Call number: S 99.0139(396)
    In: Wissenschaftliche Arbeiten der Fachrichtung Geodäsie und Geoinformatik der Leibniz Universität Hannover, Nr. 396
    Description / Table of Contents: With increasing urbanization, a well-functioning transport infrastructure that takes into account the needs of the society is becoming more and more important. In particular, a high proportion of motorized traffic can cause far-reaching problems that affect large parts of the urban population, such as traffic congestion or increased air pollution. To counteract this trend, an optimized distribution of traffic flows could improve the situation from a societal perspective. Since most routing decisions are made based on digital maps before the journey starts, clear and intuitive visualization is crucial for conveying the cartographic information to the traveler. While most existing services typically provide the most efficient routing options in terms of travel time, newer approaches attempt to guide drivers to societally favorable routes. These take into account societally relevant factors, which are referred to as scenarios in this thesis, and include environmental issues such as traffic congestion or air pollution. However, since such a societally favorable route is not necessarily efficient for the individual traveler, it is important to convince the traveler to choose a seemingly less efficient route. For this purpose, an automatic method for visualizing route maps is developed, which calculates societally favorable routes, and communicates them visually to the end user in such a way that the user would prefer to use them. For this communication, different visual variables of cartography are used, whose usage is adapted to the different scenarios and controlled by scenario-specific thresholds. Based on the goal of dynamic distribution of traffic flows, the proposed method recommends routes that are not necessarily the shortest or fastest, but rather those that seek to avoid unfavorable or hazardous paths or areas. The proposed design variants of route maps use a large variety of symbolization techniques; including classic visual variables of cartography such as color, size or pattern, but also more abstract methods that use cartographic generalization techniques.
    Description / Table of Contents: Mit zunehmender Verstädterung gewinnt eine gut funktionierende Verkehrsinfrastruktur, die den Bedürfnissen der Gesellschaft Rechnung trägt, immer mehr an Bedeutung. Insbesondere ein hoher Anteil an motorisiertem Verkehr kann weitreichende Probleme verursachen, die große Teile der Stadtbevölkerung betreffen, wie z.B. Verkehrsstaus oder erhöhte Luftverschmutzung. Um dieser Entwicklung entgegenzuwirken, könnte eine optimierte Verteilung der Verkehrsströme die Situation für die Gemeinschaft verbessern. Da die meisten Routing-Entscheidungen vor Reiseantritt auf der Grundlage digitaler Karten getroffen werden, ist eine klare und intuitive Visualisierung entscheidend für die Vermittlung kartografischer Informationen an den Reisenden. Während die meisten bestehenden Dienste in der Regel die effizientesten Routing-Optionen im Hinblick auf die Reisezeit bieten, versuchen neuere Ansätze, die Fahrer auf gesellschaftlich vorteilhafte Routen zu leiten. Diese berücksichtigen gesellschaftlich relevante Faktoren, die in dieser Arbeit als Szenarien bezeichnet werden. Darunter fallen Umweltprobleme wie Verkehrsstaus oder Luftverschmutzung. Da eine solche gesellschaftlich vorteilhafte Route für den einzelnen Reisenden jedoch nicht zwangsläufig effizient ist, ist es wichtig, den Reisenden davon zu überzeugen, eine scheinbar weniger effiziente Route zu wählen. Dazu wird im Rahmen der Arbeit ein automatisches Verfahren zur Visualisierung von Routenkarten entwickelt, welches gesellschaftlich vorteilhafte Routen berechnet und diese so visuell dem Endnutzer kommuniziert, dass dieser sie bevorzugt nutzen möchte. Für diese Kommunikation kommen verschiedene visuelle Variablen der Kartographie zum Einsatz, deren Verwendung auf die verschiedenen Szenarien angepasst sind und über Szenario-spezifische Schwellwerte gesteuert werden. Basierend auf dem Ziel einer dynamischen Verteilung der Verkehrsströme empfiehlt die vorgeschlagene Methode Routen, die nicht unbedingt die kürzesten oder schnellsten sind, sondern vielmehr solche Routen, die ungünstige oder gefährliche Wege oder Bereiche zu vermeiden versuchen. Die vorgeschlagenen Designvarianten von Routenkarten nutzen eine Vielzahl von Symbolisierungstechniken; darunter klassische, visuelle Variablen der Kartographie wie Farbe, Größe oder Muster, aber auch abstraktere Methoden, die kartographische Generalisierungstechniken verwenden.
    Type of Medium: Series available for loan
    Pages: 207 Seiten , Illustrationen, Diagramme , 30 cm
    ISSN: 01741454
    Series Statement: Wissenschaftliche Arbeiten der Fachrichtung Geodäsie und Geoinformatik der Leibniz Universität Hannover Nr. 396
    Language: English
    Note: Dissertation, Gottfried Wilhelm Leibniz Universität Hannover, 2024 , 1 Introduction 1.1 Motivation and problem statemen 1.2 Research objectives and key hypotheses 1.3 Structure of the thesis 2 Theoretical background 2.1 Visual communication with maps 2.2 Route choice factors 2.3 Cartographic symbolization 2.3.1 Visual variables 2.3.1.1 Levels of organization of visual variables 2.3.1.2 ‘Original visual variables’ as proposed by Bertin 2.3.1.3 Visual variable additions 2.3.1.4 Experimental visual variables 2.3.1.5 Conjunctions of visual variables 2.3.1.6 Dynamic visual variables 2.3.2 Cartographic design tools 2.3.3 Visual metaphor 2.3.4 Cartographic generalization and map abstraction 2.3.4.1 Insights from cognitive mapping research 2.3.4.2 Elementary processes of cartographic generalization 2.3.4.3 Cartographic generalization algorithms 2.4 Nudging 2.5 Maps and emotions 2.5.1 Classifying emotions 2.5.2 Instruments for measuring emotions 2.6 Map-related usability testing 2.6.1 Types of user study designs 2.6.2 Statistical analysis of user survey results 2.6.2.1 Descriptive statistics 2.6.2.2 Basic statistical tests and models 2.6.2.3 Sophisticated statistical models for non-parametric data 2.6.2.4 Statistical significance 2.6.2.5 Main effect and post-hoc tests 2.6.2.6 Effect sizes 2.6.2.7 Inter-rater reliability 2.6.2.8 Software for statistical analysis 3 Related work 3.1 Visual route communication using visual variables 3.2 Cartographic generalization for route map communication 3.3 Map-based visualization of environmental hazards 3.4 The role of emotions in map-based communication 3.5 Research gap addressed in this thesis 4 Framework and data preprocessing 4.1 Research framework 4.2 Scenarios 4.2.1 Traffic 4.2.2 Air quality 4.3 Routing 4.3.1 Data basis for route calculation 4.3.2 Calculation of favorable routes 4.3.3 Routing results 5 Visualization concepts for designing ‘social’ route maps 5.1 Map symbols 5.2 Data-based calculation of graphical differences in symbolization 5.3 Visually modified geometry 5.3.1 Line distortion and simplification 5.3.1.1 Line distortion 5.3.1.2 Line simplification 5.3.1.3 Combined approach 5.3.1.4 Topological issues and further adaptions 5.3.2 Length distortion using PUSH 5.3.3 Application to discrete areas: Geometric deformation of risk zones 5.4 Examples of route map design variants 5.4.1 Design variants for symbolizing route favorability 5.4.2 Application of the methodology to discrete objects 6 Usability evaluation of proposed route map design variants 6.1 User study 1: Subjective usability – Attractiveness, intuitiveness and suitability of design variants 6.1.1 Sub-hypotheses 6.1.2 Study design 6.1.3 Participants 6.1.4 Results – Intuitiveness and suitability 6.1.5 Results – Attractiveness 6.1.6 Discussion and conclusion – User study 1 6.2 User study 2: Objective usability – Effectiveness of line objects for influencing route choice in the traffic scenario 6.2.1 Common design specifications in user study 2 and user study 3 6.2.2 Sub-hypotheses 6.2.3 Route maps ............................................................................................................ 109 6.2.4 Design variants ...................................................................................................... 110 6.2.5 Calculation of graphical differences among design variants and modification intensities …………………………………………………………………………………… 112 6.2.6 Study design .......................................................................................................... 115 6.2.7 Participants ............................................................................................................ 117 6.2.8 Results – User study 2 ........................................................................................... 117 6.2.8.1 Influencing route choice ......................................................................... 117 6.2.8.2 Decision time .......................................................................................... 120 6.2.8.3 Route characteristics ............................................................................... 121 6.2.8.4 Map use habits ........................................................................................ 123 6.2.9 Discussion – User study 2 ..................................................................................... 124 6.2.9.1 Effectiveness for influencing route choice behavior .............................. 124 6.2.9.2 The role of time during decision making ................................................ 125 6.2.9.3 Relations between route choice and route characteristics ...................... 125 6.2.9.4 Transferability of the findings to real world applications ...................... 126 6.2.10 Conclusion – User study 2 .................................................................................... 126 6.2.11 Modification of line objects using dynamic visual variables ................................ 127 6.3 User study 3: Objective usability – The impact of visual communication and emotions on route choice decision making using modification of line and area objects .................................. 128 6.3.1 Sub-hypotheses ...................................................................................................... 129 6.3.2 Route maps ............................................................................................................ 130 6.3.3 Design variants ...................................................................................................... 133 6.3.3.1 Line modifications .................................................................................. 135 6.3.3.2 Area modifications ................................................................................. 136 6.3.3.3 Line + area modifications ....................................................................... 136 6.3.4 Study design .......................................................................................................... 137 6.3.5 Participants ............................................................................................................ 139 6.3.6 Results – User study 3 ........................................................................................... 139 6.3.6.1 H1: Shift towards choosing the societally favorable route ..................... 139 6.3.6.2 H2: Scenario-dependent willingness to adapt route choice behavior ..... 143 6.3.6.3 H3: Scenario-dependent effectiveness of symbolization dimensions ..... 144 6.3.6.4 H4: Influence of combining multiple visual variables in one representation …………………………………………………………………………. 144 6.3.6.5 H5: Emotional responses to map symbols .............................................. 146 6.3.6.6 H6: Effect of emotions on route choice decision making ....................... 150 6.3.6.7 Helpfulness of map visualizations .......................................................... 152 6.3.6.8 Route choice strategies ........................................................................... 153 6.3.6.9 Text-based sentiment analysis ................................................................ 154 6.3.6.10 Suitability of visualizations .................................................................. 156 6.3.6.11 Further factors influencing route choice ............................................... 156 6.3.7 Discussion – User study 3 ...................................................................................... 157 6.3.7.1 Influence of different design variants on route choice ............................ 157 6.3.7.2 The effect of emotions on route choice................................................... 158 6.3.7.3 Limitations of the study design ............................................................... 159 6.3.7.4 Outlook ................................................................................................... 160 6.3.8 Conclusion – User study 3 .........................................................................
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  • 3
    Call number: S 99.0139(393)
    In: Wissenschaftliche Arbeiten der Fachrichtung Geodäsie und Geoinformatik der Leibniz Universität Hannover, Nr. 393
    Description / Table of Contents: In dieser Arbeit wird eine ganzheitliche Prozesskette zur flächenhaften Modellierung von Bodenbewegungen entwickelt und am Beispiel der niedersächsischen Landesfläche erprobt. Unter Verwendung von GNSS, Nivellement und der satellitengestützten Radarinterferometrie werden zunächst Bewegungen von Objektpunkten an der Erdoberfläche bestimmt. Um die heterogenen Beobachtungen der unterschiedlichen Messverfahren verarbeiten zu können, erfolgt die kinematische Modellierung in separaten Datenanalysen. Die resultierenden Geschwindigkeiten der Objektpunkte bilden die Grundlage zur flächenhaften Approximation von Bodenbewegungen, wobei die Vorzüge der jeweiligen Beobachtungsverfahren miteinander kombiniert werden.
    Description / Table of Contents: In this work, a holistic processing chain for the modeling of ground motions is developed and tested using Lower Saxony as an example. Using GNSS, levelling and satellite-based radar interferometry, movements of measurement points on the earth’s surface are first determined. In order to process the heterogeneous observations of the different measurement methods, kinematic modeling is performed in separate data analyses. The resulting velocities of the measurement points form the basis for the areal approximation of ground motions, using the advantages of the respective observation methods.
    Type of Medium: Series available for loan
    Pages: 229 Seiten , Illustrationen, Diagramme , 30 cm
    ISSN: 01741454
    Series Statement: Wissenschaftliche Arbeiten der Fachrichtung Geodäsie und Geoinformatik der Leibniz Universität Hannover Nr. 393
    Language: German
    Note: Dissertation, Gottfried Wilhelm Leibniz Universität Hannover, 2024 , 1 Einleitung 13 1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 1.2 Wissenschaftlicher Beitrag der Arbeit . . . . . . . . . . . . . . . . . . . . . . . . . . 15 1.3 Aufbau der Arbeit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 2 Grundlagen 19 2.1 Geodätische Bezugssysteme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 2.1.1 Geometrische Bezugssysteme . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 2.1.2 Physikalische Höhenbezugssysteme . . . . . . . . . . . . . . . . . . . . . . . . 23 2.2 Bodenbewegungen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 2.2.1 Ursachen von Bewegungsvorgängen . . . . . . . . . . . . . . . . . . . . . . . . 26 2.2.2 Bisherige Untersuchungen in Niedersachsen . . . . . . . . . . . . . . . . . . . 27 2.3 Messverfahren zur Erfassung von Bodenbewegungen . . . . . . . . . . . . . . . . . . 29 2.3.1 Global Navigation Satellite System GNSS . . . . . . . . . . . . . . . . . . . . 30 2.3.2 Geometrisches Nivellement . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 2.3.3 Radarinterferometrie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 2.4 Prozesskette zur flächenhaften Modellierung von Bodenbewegungen . . . . . . . . . . 34 2.4.1 Anforderungen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 2.4.2 Konzeption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 2.4.3 Datenanalyse unterschiedlicher Messverfahren . . . . . . . . . . . . . . . . . . 37 2.4.4 Flächenhafte Modellierung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 2.5 Ausgewählte Bodenbewegungsdienste . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 2.6 Mathematische Grundlagen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 2.6.1 Stochastische Prozesse . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 2.6.2 Parameterschätzung im Gauß-Markov-Modell . . . . . . . . . . . . . . . . . . 44 3 Fortgeschrittene Modellansätze zur Beschreibung von Bodenbewegungen 47 3.1 Bewegungsmodellierung von Objektpunkten . . . . . . . . . . . . . . . . . . . . . . . 48 3.1.1 Modellkonfiguration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 3.1.2 Analyse periodischer Bewegungsanteile . . . . . . . . . . . . . . . . . . . . . . 52 3.2 Räumliche Ausreißeranalyse . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 3.3 Multilevel B-Splines zur flächenhaften Bewegungsmodellierung . . . . . . . . . . . . 56 3.3.1 B-Spline Approximation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 3.3.2 Multilevel B-Spline Approximation . . . . . . . . . . . . . . . . . . . . . . . . 59 3.4 Geostatistik zur flächenhaften Bewegungsmodellierung . . . . . . . . . . . . . . . . . 62 3.4.1 Experimentelles Variogramm . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 3.4.2 Theoretisches Variogramm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 3.4.3 Ordinary Kriging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 3.4.4 Regressions-Kriging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 3.5 Modellvalidierung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 3.5.1 Kreuzvalidierung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 3.5.2 Jackknife . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 3.5.3 Bootstrapping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 4 Kinematische Bewegungsanalyse von Objektpunkten 79 4.1 Analyse von GNSS-Daten . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 4.1.1 Prozesskette für das Koordinatenmonitoring des Referenzstationsnetzes . . . 80 4.1.2 Datengrundlage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83 4.1.3 Ausreißerfilterung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 4.1.4 Zeitreihenanalyse . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 4.1.5 Berechnung von 3D-Geschwindigkeiten . . . . . . . . . . . . . . . . . . . . . . 92 4.1.6 Interpretation und Wertung . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94 4.2 Analyse von Nivellementdaten . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 4.2.1 Modellansatz der kinematischen Höhenausgleichung . . . . . . . . . . . . . . 100 4.2.2 Datengrundlage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104 4.2.3 Datenaufbereitung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106 4.2.4 Berechnung von Vertikalgeschwindigkeiten . . . . . . . . . . . . . . . . . . . . 110 4.2.5 Interpretation und Wertung . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115 4.3 Analyse von PSI-Daten . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119 4.3.1 Datengrundlage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119 4.3.2 Zeitreihenanalyse . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121 4.3.3 Berechnung von LOS-Geschwindigkeiten . . . . . . . . . . . . . . . . . . . . . 124 4.3.4 Räumliche Ausreißerfilterung . . . . . . . . . . . . . . . . . . . . . . . . . . . 126 4.3.5 Interpretation und Wertung . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129 5 Flächenhafte Modellierung von PSI-Daten 131 5.1 Multilevel B-Spline Approximation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132 5.1.1 Modellkonfiguration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132 5.1.2 Flächenhaftes Bewegungsmodell . . . . . . . . . . . . . . . . . . . . . . . . . 135 5.2 Ordinary Kriging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138 5.2.1 Räumliche Strukturanalyse . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138 5.2.2 Flächenhaftes Bewegungsmodell . . . . . . . . . . . . . . . . . . . . . . . . . 139 5.3 Regressions-Kriging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143 5.3.1 Trendmodell . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143 5.3.2 Signalmodell . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146 5.3.3 Flächenhaftes Bewegungsmodell . . . . . . . . . . . . . . . . . . . . . . . . . 148 5.4 Vergleich der Modellansätze . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150 6 Berechnung eines niedersächsischen Bodenbewegungsmodells 155 6.1 Aufnahmegeometrie von Radarsatelliten . . . . . . . . . . . . . . . . . . . . . . . . . 156 6.2 Geodätische Modellkalibrierung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157 6.2.1 Bestimmung von Korrektionswerten . . . . . . . . . . . . . . . . . . . . . . . 157 6.2.2 Flächenhaftes Korrektionsmodell . . . . . . . . . . . . . . . . . . . . . . . . . 160 6.2.3 Kalibriertes Bewegungsmodell . . . . . . . . . . . . . . . . . . . . . . . . . . . 162 6.3 Trennung der Bodenbewegungskomponenten . . . . . . . . . . . . . . . . . . . . . . . 164 6.3.1 Methodik . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165 6.3.2 Flächenhafte Vertikalbewegungen . . . . . . . . . . . . . . . . . . . . . . . . . 167 6.3.3 Flächenhafte Horizontalbewegungen . . . . . . . . . . . . . . . . . . . . . . . 169 6.3.4 Interpretation und Wertung . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171 7 Zusammenfassung und Ausblick 177 7.1 Zusammenfassung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177 7.2 Ausblick . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179 Inhaltsverzeichnis 11 Anhang 180 A Kinematische Bewegungsanalyse von Objektpunkten 181 A.1 Analyse von GNSS-Daten . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181 A.2 Analyse von Nivellementdat
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  • 4
    Call number: AWI Bio-24-95742
    Description / Table of Contents: The arctic is warming 2 – 4 times faster than the global average, resulting in a strong feedback on northern ecosystems such as boreal forests, which cover a vast area of the high northern latitudes. With ongoing global warming, the treeline subsequently migrates northwards into tundra areas. The consequences of turning ecosystems are complex: on the one hand, boreal forests are storing large amounts of global terrestrial carbon and act as a carbon sink, dragging carbon dioxide out of the global carbon cycle, suggesting an enhanced carbon uptake with increased tree cover. On the other hand, with the establishment of trees, the albedo effect of tundra decreases, leading to enhanced soil warming. Meanwhile, permafrost thaws, releasing large amounts of previously stored carbon into the atmosphere. So far, mainly vegetation dynamics have been assessed when studying the impact of warming onto ecosystems. Most land plants are living in close symbiosis with bacterial and fungal communities, sustaining their growth in nutrient poor habitats. However, the impact of climate change on these subsoil communities alongside changing vegetation cover remains poorly understood. Therefore, a better understanding of soil community dynamics on multi millennial timescales is inevitable when addressing the development of entire ecosystems. Unravelling long-term cross-kingdom dependencies between plant, fungi, and bacteria is not only a milestone for the assessment of warming on boreal ecosystems. On top, it also is the basis for agriculture strategies to sustain society with sufficient food in a future warming world. The first objective of this thesis was to assess ancient DNA as a proxy for reconstructing the soil microbiome (Manuscripts I, II, III, IV). Research findings across these projects enable a comprehensive new insight into the relationships of soil microorganisms to the surrounding vegetation. First, this was achieved by establishing (Manuscript I) and applying (Manuscript II) a primer pair for the selective amplification of ancient fungal DNA from lake sediment samples with the metabarcoding approach. To assess fungal and plant co-variation, the selected primer combination (ITS67, 5.8S) amplifying the ITS1 region was applied on samples from five boreal and arctic lakes. The obtained data showed that the establishment of fungal communities is impacted by warming as the functional ecological groups are shifting. Yeast and saprotroph dominance during the Late Glacial declined with warming, while the abundance of mycorrhizae and parasites increased with warming. The overall species richness was also alternating. The results were compared to shotgun sequencing data reconstructing fungi and bacteria (Manuscripts III, IV), yielding overall comparable results to the metabarcoding approach. Nonetheless, the comparison also pointed out a bias in the metabarcoding, potentially due to varying ITS lengths or copy numbers per genome. The second objective was to trace fungus-plant interaction changes over time (Manuscripts II, III). To address this, metabarcoding targeting the ITS1 region for fungi and the chloroplast P6 loop for plants for the selective DNA amplification was applied (Manuscript II). Further, shotgun sequencing data was compared to the metabarcoding results (Manuscript III). Overall, the results between the metabarcoding and the shotgun approaches were comparable, though a bias in the metabarcoding was assumed. We demonstrated that fungal shifts were coinciding with changes in the vegetation. Yeast and lichen were mainly dominant during the Late Glacial with tundra vegetation, while warming in the Holocene lead to the expansion of boreal forests with increasing mycorrhizae and parasite abundance. Aside, we highlighted that Pinaceae establishment is dependent on mycorrhizal fungi such as Suillineae, Inocybaceae, or Hyaloscypha species also on long-term scales. The third objective of the thesis was to assess soil community development on a temporal gradient (Manuscripts III, IV). Shotgun sequencing was applied on sediment samples from the northern Siberian lake Lama and the soil microbial community dynamics compared to ecosystem turnover. Alongside, podzolization processes from basaltic bedrock were recovered (Manuscript III). Additionally, the recovered soil microbiome was compared to shotgun data from granite and sandstone catchments (Manuscript IV, Appendix). We assessed if the establishment of the soil microbiome is dependent on the plant taxon and as such comparable between multiple geographic locations or if the community establishment is driven by abiotic soil properties and as such the bedrock area. We showed that the development of soil communities is to a great extent driven by the vegetation changes and temperature variation, while time only plays a minor role. The analyses showed general ecological similarities especially between the granite and basalt locations, while the microbiome on species-level was rather site-specific. A greater number of correlated soil taxa was detected for deep-rooting boreal taxa in comparison to grasses with shallower roots. Additionally, differences between herbaceous taxa of the late Glacial compared to taxa of the Holocene were revealed. With this thesis, I demonstrate the necessity to investigate subsoil community dynamics on millennial time scales as it enables further understanding of long-term ecosystem as well as soil development processes and such plant establishment. Further, I trace long-term processes leading to podzolization which supports the development of applied carbon capture strategies under future global warming.
    Type of Medium: Dissertations
    Pages: xii, 198 Seiten , Illustrationen, Diagramme
    Language: English
    Note: Dissertation, Universität Potsdam, 2024 , Table of Contents Summary Deutsche Zusammenfassung 1 Introduction 1.1 Arctic ecosystems under global warming 1.2 The plant-associated microbiome 1.3 Drivers of soil development 1.4 Ancient DNA to unravel past ecosystems 1.4.1 Lake sediments as archives of past community changes 1.4.2 Metabarcoding for targeting specific communities 1.4.3 Shotgun sequencing for broader overview 1.5 Thesis objective 1.6 Thesis outline and author contributions 2 Manuscript I 2.1 Abstract 2.2 Introduction 2.3 Materials and Methods 2.3.1 Primer design and evaluation In silico analyses Evaluation of lake sediment core DNA for analyses of fungal paleoecology 2.4 Results Primer design and evaluation Evaluation of lake sediment core DNA for fungal paleoecology 2.4.1 Taxonomic resolution across the cores 2.4.2 Comprehensiveness: Rarefaction and accumulation curves 2.4.3 Amplicon length and GC content to assess bias through degradation 2.4.4 General taxonomic composition of fungi in Siberian lake sediment cores Diversity of fungal paleocommunities from lake CH12 2.5 Discussion 2.5.1 Preservation biases and potential contamination 2.5.2 Characteristics of the optimized sedaDNA ITS1 metabarcoding assay 2.5.3 Potential of lake sediment fungal DNA for paleoecology 2.6 Author contributions 2.7 Acknowledgements 2.8 Conflict of interest 2.9 References 3 Manuscript II 3.1 Abstract 3.2 Introduction 3.3 Geographic setting and study sites 3.4 Materials and Methods 3.4.1 Sampling 3.4.2 DNA extraction and amplification 3.4.3 Bioinformatic analysis 3.4.4 Assessment of negative controls and contamination 3.4.5 Statistical analysis and visualization 3.5 Results 3.5.1 Fungi: sedaDNA sequencing results and overall patterns of alpha diversity and taxonomic composition 3.5.2 Vegetation: sedaDNA sequencing results and overall patterns of alpha diversity and taxonomic composition 3.5.3 Site-specific plant-fungus covariation 3.5.3.1 Fungus and plant covariation in arctic Siberia from MIS3 to the Holocene 3.5.3.2 Quantitative relationships between fungi and plant richness and composition 3.6 Discussion 3.6.1 Fungus and plant diversity along a spatiotemporal gradient in Siberia 3.6.2 Changes in ecosystem functioning over a spatiotemporal gradient 3.6.3 Implications of our results for ecosystem functioning and future research avenues 3.7 Conclusions Funding Availability of data and material Author contribution Declaration of competing interest Acknowledgements 3.8 References 4 Manuscript III 4.1 Abstract 4.2 Introduction 4.3 Results and Discussion 4.3.1 Compositional changes of plants, fungi, and bacteria in ancient metagenomic datasets 4.3.2 Long-term soil development: a trajectory or environmentally driven processes? 4.3.3 Bioweathering supported by lichens and mycorrhiza 4.3.4 Turnover in carbon, nitrogen, and sulphur cycling 4.3.5 Tracing podzolization 4.4 Implications and conclusions 4.5 Material and methods 4.5.1 Geographical setting and study site 4.5.2 X-ray fluorescence scanning of the sediment core 4.5.3 Core sub-sampling 4.5.4 DNA extraction 4.5.5 Single stranded DNA library build 4.5.6 Bioinformatic pipeline for the analysis of the sequencing results 4.5.7 Data analysis 4.5.8 Analysis of the ancient patterns 4.5.9 Statistical analysis of the dataset Acknowledgements 4.6 References Declarations 5 Discussion and synthesis 5.1 Long-term rhizosphere establishment in tundra and taiga areas 5.1.1 SedaDNA as a proxy for soil microbiome 5.1.1.1 Fungal DNA metabarcoding 5.1.1.2 Targeting soil communities with shotgun sequencing 5.1.1.3 Comparison between metabarcoding and shotgun sequencing for the soil microbiome 5.1.2 Fungi-vegetation interaction changes over time 5.1.3 Soil development on a temporal gradient 5.2 Conclusion and future perspectives 6 References 7 Appendix 7.1 Appendix to manuscript I 7.2 Appendix to manuscript II 7.3 Appendix to manuscript III 7.4 Manuscript IV 7.4.1 Abstract 7.4.2 Introduction 7.4.3 Geographical setting and study sites 7.4.4 Material & Methods 7.4.4.1 Sub-sampling of the sediment cores 7.4.4.2 DNA extraction 7.4.4.3 Single stranded DNA library built 7.4.4.4 Bioinformatic pipeline for the analysis of the sequencing data 7.4.4.5 Data analysis 7.4.4.6 Statistical analysis of the datasets 7.4.5 Results 7.4.5.1 Compositional changes of representative plant taxa alongside dynamics in fungal ecologies and bacterial element cycling in ancient metagenomic datasets 7.4.5.2 Impact of abiotic and biotic drivers on soil establishment across geographical locations 7.4.5.3 Relative positive correlations of functional soil taxa with plants across the locations 7.4.5.4 Assessment of the plant taxon-specific microbiome across the locations 7.4.6 Discussion 7.4.6.1 Site-specific soil development 7.4.6.2 Differences in the bedrock 7.4.6.3 Correlation between the lake biota 7.4.6.3.1 General Trends in positively correlated rhizosphere taxa 7.4.6.3.2 Plant taxa specific microbiome 7.4.7 Implications and future directions 7.4.8 References 7.4.9 Supplement to manuscript IV Acknowledgements Eidesstattliche Erklärung Damage pattern analysis – Auflagen Doktorarbeit Summary Main References
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  • 5
    Call number: AWI A5-24-95744
    Description / Table of Contents: The Arctic is the hot spot of the ongoing, global climate change. Over the last decades, near-surface temperatures in the Arctic have been rising almost four times faster than on global average. This amplified warming of the Arctic and the associated rapid changes of its environment are largely influenced by interactions between individual components of the Arctic climate system. On daily to weekly time scales, storms can have major impacts on the Arctic sea-ice cover and are thus an important part of these interactions within the Arctic climate. The sea-ice impacts of storms are related to high wind speeds, which enhance the drift and deformation of sea ice, as well as to changes in the surface energy budget in association with air mass advection, which impact the seasonal sea-ice growth and melt. The occurrence of storms in the Arctic is typically associated with the passage of transient cyclones. Even though the above described mechanisms how storms/cyclones impact the Arctic sea ice are in principal known, there is a lack of statistical quantification of these effects. In accordance with that, the overarching objective of this thesis is to statistically quantify cyclone impacts on sea-ice concentration (SIC) in the Atlantic Arctic Ocean over the last four decades. In order to further advance the understanding of the related mechanisms, an additional objective is to separate dynamic and thermodynamic cyclone impacts on sea ice and assess their relative importance. Finally, this thesis aims to quantify recent changes in cyclone impacts on SIC. These research objectives are tackled utilizing various data sets, including atmospheric and oceanic reanalysis data as well as a coupled model simulation and a cyclone tracking algorithm. Results from this thesis demonstrate that cyclones are significantly impacting SIC in the Atlantic Arctic Ocean from autumn to spring, while there are mostly no significant impacts in summer. The strength and the sign (SIC decreasing or SIC increasing) of the cyclone impacts strongly depends on the considered daily time scale and the region of the Atlantic Arctic Ocean. Specifically, an initial decrease in SIC (day -3 to day 0 relative to the cyclone) is found in the Greenland, Barents and Kara Seas, while SIC increases following cyclones (day 0 to day 5 relative to the cyclone) are mostly limited to the Barents and Kara Seas. For the cold season, this results in a pronounced regional difference between overall (day -3 to day 5 relative to the cyclone) SIC-decreasing cyclone impacts in the Greenland Sea and overall SIC-increasing cyclone impacts in the Barents and Kara Seas. A cyclone case study based on a coupled model simulation indicates that both dynamic and thermodynamic mechanisms contribute to cyclone impacts on sea ice in winter. A typical pattern consisting of an initial dominance of dynamic sea-ice changes followed by enhanced thermodynamic ice growth after the cyclone passage was found. This enhanced ice growth after the cyclone passage most likely also explains the (statistical) overall SIC-increasing effects of cyclones in the Barents and Kara Seas in the cold season. Significant changes in cyclone impacts on SIC over the last four decades have emerged throughout the year. These recent changes are strongly varying from region to region and month to month. The strongest trends in cyclone impacts on SIC are found in autumn in the Barents and Kara Seas. Here, the magnitude of destructive cyclone impacts on SIC has approximately doubled over the last four decades. The SIC-increasing effects following the cyclone passage have particularly weakened in the Barents Sea in autumn. As a consequence, previously existing overall SIC-increasing cyclone impacts in this region in autumn have recently disappeared. Generally, results from this thesis show that changes in the state of the sea-ice cover (decrease in mean sea-ice concentration and thickness) and near-surface air temperature are most important for changed cyclone impacts on SIC, while changes in cyclone properties (i.e. intensity) do not play a significant role.
    Type of Medium: Dissertations
    Pages: VIII, 131 Seiten , Illustrationen, Diagramme
    Language: English
    Note: Dissertation, Universität Potsdam, 2024 , Contents 1 Introduction 1.1 The Arctic sea-ice cover 1.1.1 Sea ice in the coupled Arctic climate system 1.1.2 Recent changes of the Arctic sea ice 1.2 The atmosphere as driver of sea-ice variability 1.2.1 Large-scale circulation patterns 1.2.2 Role of cyclones 1.3 Thesis structure and research questions 2 Theory and methods 2.1 Synoptic cyclones 2.1.1 Related fundamentals of atmospheric dynamics 2.1.2 Cyclone activity in the Arctic 2.2 Cyclone tracking and cyclone occurrence mask 2.3 Dynamic and thermodynamic sea-ice variability related to cyclones 3 New insights into cyclone impacts on sea ice in the Atlantic sector of the Arctic Ocean in winter 3.1 Abstract 3.2 Introduction 3.3 Data and methods 3.3.1 Database and cyclone identification 3.3.2 Quantification of cyclone impacts on SIC 3.4 Cyclone impacts on SIC 3.4.1 Effects of different time scales and regions 3.4.2 Effects of SIC conditions and cyclone depth 3.4.3 Spatial variability of SIC response to cyclones 3.4.4 Relation to near-surface wind and surface energy budget 3.5 Signature of ’New Arctic’ conditions 3.6 Conclusions 3.7 Supplementary material 4 Impact of three intense winter cyclones on the sea ice cover in the Barents Sea: A case study with a coupled regional climate model 4.1 Abstract 4.2 Introduction 4.3 Data and methods 4.3.1 HIRHAM–NAOSIM simulation 4.3.2 Supplementary evaluation data 4.3.3 Dynamic and thermodynamic contributions to sea-ice changes 4.4 Results 4.4.1 Cyclone cases 4.4.2 Cyclone impacts on SEB 4.4.3 Cyclone impacts on sea-ice concentration (SIC) 4.4.4 Cyclone impacts on sea-ice thickness (SIT) 4.4.5 Context to other cyclone cases during the MOSAiC winter 4.5 Discussion and conclusions 4.6 Supplementary material 5 Cyclone impacts on sea ice concentration in the Atlantic Arctic Ocean: Annual cycle and recent changes 5.1 Abstract 5.2 Introduction 5.3 Data and methods 5.4 Changes in cyclones and traversed sea ice 5.5 Cyclone impacts on SIC 5.5.1 Annual cycle in the old Arctic 5.5.2 Changes in the new Arctic 5.5.3 Regional changes in autumn 5.6 Conclusions 5.7 Supplementary material 6 Conclusions and Outlook 6.1 What is the statistical impact of cyclone passages on sea-ice concentration (SIC) in the Atlantic Arctic Ocean? 6.2 What are the individual contributions of dynamic and thermodynamic processes to sea-ice changes related to cyclones? 6.3 Do the SIC impacts of cyclones change in a warming Arctic and what are the related mechanisms? 6.4 Ways forward Appendix: Cyclones modulate the control of the North Atlantic Oscillation on transports into the Barents Sea Bibliography
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  • 6
    Call number: M 24.95739
    Keywords: Geothermik ; Bohrung ; Geothermische Energie ; Energietechnische Anlage ; Anlagenplanung ; Anlagenbau ; Geothermometrie ; Geothermik
    Type of Medium: Monograph available for loan
    Pages: 288 Seiten , Illustrationen, Diagramme
    ISBN: 9783895542473 , 3895542474
    URL: Inhaltsverzeichnis  (lizenzpflichtig)
    Language: German
    Location: Upper compact magazine
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  • 7
    Monograph available for loan
    Monograph available for loan
    Berlin : De Gruyter
    Call number: M 24.95740
    Type of Medium: Monograph available for loan
    Pages: XXVI, 372 Seiten , Illustrationen, Diagramme , 25 cm x 18 cm
    ISBN: 9783110298048 , 311029804X
    Series Statement: De Gruyter studies in mathematical physics volume 31
    Language: English
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