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We calculate the additional carbon emissions as a result of the conversion of natural land in a process of urbanisation; and the change of carbon flows by “urbanised” ecosystems, when the atmospheric carbon is exported to the neighboring territories, from 1980 till 2050 for the eight regions of the world. As a scenario we use combined UN and demographic model′s prognoses for regional total and urban population growth. The calculations of urban areas dynamics are based on two models: the regression model and the Gamma-model. The urbanised area is sub-divided on built-up, „green“ (parks, etc.) and informal settlements (favelas) areas. The next step is to calculate the regional and world dynamics of carbon emission and export, and the annual total carbon balance. Both models give similar results with some quantitative differences. In the first model, the world annual emissions attain a maximum of 205 MtC/year between 2020-2030. Emissions will then slowly decrease. The maximum contributions are given by China and the Asia and Pacific regions. In the second model, world annual emissions increase to 1.25 GtC in 2005, beginning to decrease afterwards. If we compare the emission maximum with the annual emission caused by deforestation, 1.36GtC per year, then we can say that the role of urbanised territories (UT) is of a comparable magnitude. Regarding the world annual export of carbon by UT, we observe its monotonous growth by three times, from 24 MtC to 66 MtC in the first model, and from 249 MtC to 505 MtC in the second one. The latter, is therefore comparable to the amount of carbon transported by rivers into the ocean (196-537 MtC). By estimating the total balance we find that urbanisation shifts the total balance towards a “sink” state. The urbanisation is inhibited in the interval 2020-2030, and by 2050 the growth of urbanised areas would almost stop. Hence, the total emission of natural carbon at that stage will stabilise at the level of the 1980s (80 MtC per year). As estimated by the second model, the total balance, being almost constant until 2000, then starts to decrease at an almost constant rate. We can say that by the end of the XXI century, the total carbon balance will be equal to zero, when the exchange flows are fully balanced, and may even be negative, when the system begins to take up carbon from the atmosphere, i.e., becomes a “sink”.
The intracontinental endorheic Aral Sea, remote from oceanic influences, represents an excellent sedimentary archive in Central Asia that can be used for high-resolution palaeoclimate studies. We performed palynological, microfacies and geochemical analyses on sediment cores retrieved from Chernyshov Bay, in the NW part of the modern Large Aral Sea. The most complete sedimentary sequence, whose total length is 11 m, covers approximately the past 2000 years of the late Holocene. High-resolution palynological analyses, conducted on both dinoflagellate cysts assemblages and pollen grains, evidenced prominent environmental change in the Aral Sea and in the catchment area. The diversity and the distribution of dinoflagellate cysts within the assemblages characterized the sequence of salinity and lake-level changes during the past 2000 years. Due to the strong dependence of the Aral Sea hydrology to inputs from its tributaries, the lake levels are ultimately linked to fluctuations in meltwater discharges during spring. As the amplitude of glacial meltwater inputs is largely controlled by temperature variations in the Tien Shan and Pamir Mountains during the melting season, salinity and lake-level changes of the Aral Sea reflect temperature fluctuations in the high catchment area during the past 2000 years. Dinoflagellate cyst assemblages document lake lowstands and hypersaline conditions during ca. 0–425 AD, 920–1230 AD, 1500 AD, 1600–1650 AD, 1800 AD and since the 1960s, whereas oligosaline conditions and higher lake levels prevailed during the intervening periods. Besides, reworked dinoflagellate cysts from Palaeogene and Neogene deposits happened to be a valuable proxy for extreme sheet-wash events, when precipitation is enhanced over the Aral Sea Basin as during 1230–1450 AD. We propose that the recorded environmental changes are related primarily to climate, but may have been possibly amplified during extreme conditions by human-controlled irrigation activities or military conflicts. Additionally, salinity levels and variations in solar activity show striking similarities over the past millennium, as during 1000–1300 AD, 1450–1550 and 1600–1700 AD when low lake levels match well with an increase in solar activity thus suggesting that an increase in the net radiative forcing reinforced past Aral Sea’s regressions. On the other hand, we used pollen analyses to quantify changes in moisture conditions in the Aral Sea Basin. High-resolution reconstruction of precipitation (mean annual) and temperature (mean annual, coldest versus warmest month) parameters are performed using the “probability mutual climatic spheres” method, providing the sequence of climate change for the past 2000 years in western Central Asia. Cold and arid conditions prevailed during ca. 0–400 AD, 900–1150 AD and 1500–1650 AD with the extension of xeric vegetation dominated by steppe elements. Conversely, warmer and less arid conditions occurred during ca. 400–900 AD and 1150–1450 AD, where steppe vegetation was enriched in plants requiring moister conditions. Change in the precipitation pattern over the Aral Sea Basin is shown to be predominantly controlled by the Eastern Mediterranean (EM) cyclonic system, which provides humidity to the Middle East and western Central Asia during winter and early spring. As the EM is significantly regulated by pressure modulations of the North Atlantic Oscillation (NAO) when the system is in a negative phase, a relationship between humidity over western Central Asia and the NAO is proposed. Besides, laminated sediments record shifts in sedimentary processes during the late Holocene that reflect pronounced changes in taphonomic dynamics. In Central Asia, the frequency of dust storms occurring during spring when the continent is heating up is mostly controlled by the intensity and the position of the Siberian High (SH) Pressure System. Using titanium (Ti) content in laminated sediments as a proxy for aeolian detrital inputs, changes in wind dynamics over Central Asia is documented for the past 1500 years, offering the longest reconstruction of SH variability to date. Based on high Ti content, stronger wind dynamics are reported from 450–700 AD, 1210–1265 AD, 1350–1750 AD and 1800–1975 AD, reporting a stronger SH during spring. In contrast, lower Ti content from 1750–1800 AD and 1980–1985 AD reflect a diminished influence of the SH and a reduced atmospheric circulation. During 1180–1210 AD and 1265–1310 AD, considerably weakened atmospheric circulation is evidenced. As a whole, though climate dynamics controlled environmental changes and ultimately modulated changes in the western Central Asia’s climate system, it is likely that changes in solar activity also had an impact by influencing to some extent the Aral Sea’s hydrology balance and also regional temperature patterns in the past. <hr> The appendix of the thesis is provided via the HTML document as ZIP download.
Together with the gradual change of mean values, ongoing climate change is projected to increase frequency and amplitude of temperature and precipitation extremes in many regions of Europe. The impacts of such in most cases short term extraordinary climate situations on terrestrial ecosystems are a matter of central interest of recent climate change research, because it can not per se be assumed that known dependencies between climate variables and ecosystems are linearly scalable. So far, yet, there is a high demand for a method to quantify such impacts in terms of simultaneities of event time series.
In the course of this manuscript the new statistical approach of Event Coincidence Analysis (ECA) as well as it's R implementation is introduced, a methodology that allows assessing whether or not two types of event time series exhibit similar sequences of occurrences. Applications of the method are presented, analyzing climate impacts on different temporal and spacial scales: the impact of extraordinary expressions of various climatic variables on tree stem variations (subdaily and local scale), the impact of extreme temperature and precipitation events on the owering time of European shrub species (weekly and country scale), the impact of extreme temperature events on ecosystem health in terms of NDVI (weekly and continental scale) and the impact of El Niño and La Niña events on precipitation anomalies (seasonal and global scale).
The applications presented in this thesis refine already known relationships based on classical methods and also deliver substantial new findings to the scientific community: the widely known positive correlation between flowering time and temperature for example is confirmed to be valid for the tails of the distributions while the widely assumed positive dependency between stem diameter variation and temperature is shown to be not valid for very warm and very cold days. The larger scale investigations underline the sensitivity of anthrogenically shaped landscapes towards temperature extremes in Europe and provide a comprehensive global ENSO impact map for strong precipitation events.
Finally, by publishing the R implementation of the method, this thesis shall enable other researcher to further investigate on similar research questions by using Event Coincidence Analysis.
Hyperspectral remote sensing of the spatial and temporal heterogeneity of low Arctic vegetation
(2019)
Arctic tundra ecosystems are experiencing warming twice the global average and Arctic vegetation is responding in complex and heterogeneous ways. Shifting productivity, growth, species composition, and phenology at local and regional scales have implications for ecosystem functioning as well as the global carbon and energy balance. Optical remote sensing is an effective tool for monitoring ecosystem functioning in this remote biome. However, limited field-based spectral characterization of the spatial and temporal heterogeneity limits the accuracy of quantitative optical remote sensing at landscape scales. To address this research gap and support current and future satellite missions, three central research questions were posed:
• Does canopy-level spectral variability differ between dominant low Arctic vegetation communities and does this variability change between major phenological phases?
• How does canopy-level vegetation colour images recorded with high and low spectral resolution devices relate to phenological changes in leaf-level photosynthetic pigment concentrations?
• How does spatial aggregation of high spectral resolution data from the ground to satellite scale influence low Arctic tundra vegetation signatures and thereby what is the potential of upcoming hyperspectral spaceborne systems for low Arctic vegetation characterization?
To answer these questions a unique and detailed database was assembled. Field-based canopy-level spectral reflectance measurements, nadir digital photographs, and photosynthetic pigment concentrations of dominant low Arctic vegetation communities were acquired at three major phenological phases representing early, peak and late season. Data were collected in 2015 and 2016 in the Toolik Lake Research Natural Area located in north central Alaska on the North Slope of the Brooks Range. In addition to field data an aerial AISA hyperspectral image was acquired in the late season of 2016. Simulations of broadband Sentinel-2 and hyperspectral Environmental and Mapping Analysis Program (EnMAP) satellite reflectance spectra from ground-based reflectance spectra as well as simulations of EnMAP imagery from aerial hyperspectral imagery were also obtained.
Results showed that canopy-level spectral variability within and between vegetation communities differed by phenological phase. The late season was identified as the most discriminative for identifying many dominant vegetation communities using both ground-based and simulated hyperspectral reflectance spectra. This was due to an overall reduction in spectral variability and comparable or greater differences in spectral reflectance between vegetation communities in the visible near infrared spectrum.
Red, green, and blue (RGB) indices extracted from nadir digital photographs and pigment-driven vegetation indices extracted from ground-based spectral measurements showed strong significant relationships. RGB indices also showed moderate relationships with chlorophyll and carotenoid pigment concentrations. The observed relationships with the broadband RGB channels of the digital camera indicate that vegetation colour strongly influences the response of pigment-driven spectral indices and digital cameras can track the seasonal development and degradation of photosynthetic pigments.
Spatial aggregation of hyperspectral data from the ground to airborne, to simulated satel-lite scale was influenced by non-photosynthetic components as demonstrated by the distinct shift of the red edge to shorter wavelengths. Correspondence between spectral reflectance at the three scales was highest in the red spectrum and lowest in the near infra-red. By artificially mixing litter spectra at different proportions to ground-based spectra, correspondence with aerial and satellite spectra increased. Greater proportions of litter were required to achieve correspondence at the satellite scale.
Overall this thesis found that integrating multiple temporal, spectral, and spatial data is necessary to monitor the complexity and heterogeneity of Arctic tundra ecosystems. The identification of spectrally similar vegetation communities can be optimized using non-peak season hyperspectral data leading to more detailed identification of vegetation communities. The results also highlight the power of vegetation colour to link ground-based and satellite data. Finally, a detailed characterization non-photosynthetic ecosystem components is crucial for accurate interpretation of vegetation signals at landscape scales.
Durch die anthropogene Nutzung sind viele Auen in Mitteleuropa verändert worden, wobei insbesondere die Retentionsflächen stark verringert wurden. Während Auen seit längerem im Fokus der wissenschaftlichen Bearbeitung stehen, gibt es bisher große Wissensdefizite in der Frage der Auenreaktivierungen. Zum einen sind derartige Projekte bisher kaum verwirklicht und zum anderen ist ein langfristiges Monitoring notwendig, um die Anpassung von Biozönosen an die veränderten Standortbedingungen beobachten zu können. Um die Folgen derartiger Eingriffe zu analysieren, bieten sich computergestützte Modellierungen der Landschaftsentwicklung an, wie sie in der vorliegenden Arbeit verwirklicht wurden. Ziel der Arbeit war, mit Hilfe eines Geografischen Informationssystems (GIS) das Entwicklungspotenzial der Landschaft bei verschiedenen Rückdeichungsvarianten auf der Ebene der Biotoptypen darzustellen. Dabei ging es nicht um die Erstellung eines allgemein gültigen Auenmodells sondern um die Erarbeitung eines Modells für einen konkreten Anwendungsfall. Der erarbeitete Ansatz sollte zudem für die landschaftsplanerische Praxis geeignet sein. Als Beispielgebiete wurden Flächen an der Mittleren Elbe bei Rogätz und Sandau, beide im nördlichen Teil von Sachsen-Anhalt, ausgewählt. Die vorliegende Arbeit gliedert sich in zwei Teile. Im ersten Teil werden Erhebungen und Auswertungen als Grundlage der Modellentwicklung dargestellt. Dazu wurden die Biotoptypen der Beispielgebiete flächendeckend erhoben und mit punktuellen Vegetationserhebungen ergänzt. Aus dem Forschungsprojekt "Rückgewinnung von Retentionsflächen und Altauenreaktivierung an der Mittleren Elbe in Sachsen-Anhalt" des Bundesministeriums für Bildung und Forschung (BMBF) standen standortökologische Daten der Hydrologie und Bodenkunde zur Verfügung. Ziel der Auswertung war, Schlüsselfaktoren für Hydrologie und Bodenbedingungen innerhalb der rezenten Aue zu identifizieren, die zur Ausprägung bestimmter Biotoptypen führen. Im zweiten Teil der Arbeit wurde ein Modell für Biotoptypenpotenziale auf den geplanten Rück–deichungsflächen entwickelt. Das Modell bearbeitet die Datenbank der verwendeten GIS-Dateien, die auf Daten zum Bestand beruht und um solche der Prognose der Standortökologie (Hydrologie und Boden) im Rückdeichungsfalle aus dem BMBF-Projekt erweitert wurde. Weitere Voraussetzung für die Modellierung war die Erarbeitung von Leitbildern, in denen unterschiedliche Nutzungsszenarios für die Landschaft nach Deichrückverlegung hypothetisch festgelegt wurden. Insbesondere die Nutzungsintensität wurde variiert, von einer Variante intensiver land- und forstwirtschaftlicher Nutzung über sogenannte integrierte Entwicklungsziele aus dem BMBF-Projekt bis hin zu einer Variante der Naturschutznutzung. Zusätzlich wurde eine zukünftige Potentielle Natürliche Vegetation modelliert. Eine Überprüfung des Modell fand für den Raum der rezenten Aue in der intensiven Nutzungsvariante statt, die der gegenwärtigen Nutzung am nächsten kommt. Werden Informationen des Bestandsbiotoptyps als Korrekturgröße in das Modell einbezogen, konnte für viele Biotoptypen eine Trefferquote von über 90 % erreicht werden. Bei flächenmäßig weniger bedeutenden Bio–toptypen lag dieser Wert aufgrund der schmaleren Datenbasis zwischen 20 und 40 %. Als Ergebnis liegt für unterschiedliche Deichvarianten und Leitbilder in den Beispielgebieten die Landschaftsentwicklung als Biotoppotenzial vor. Als eine vereinfachte Regionalisierung der punktuellen Vegetationsdaten wurde im Modell geprüft, inwieweit die modellierten Biotopflächen der Charakteristik der pflanzensoziologischen Aufnahmen aus der rezenten Aue entsprechen. In dem Falle wurde die Pflanzengesellschaft der jeweiligen ökologisch im Rahmen der Untersuchung einheitlichen Flächeneinheit zugeordnet. Anteilig lässt sich damit die Biotopprognosefläche pflanzensoziologisch konkretisieren. Die vorliegende Arbeit gehört zu den bisher wenigen Arbeiten, die sich mit den Folgen von Auenreaktivierung auf die Entwicklung der Landschaft auseinandersetzen. Sie zeigt eine Möglichkeit auf, Prognosemodelle für Biotoptypen und Vegetation anhand begrenzter Felduntersuchungen zu entwerfen. Derartige Modelle können zum Verständnis von Eingriffen in den Naturhaushalt, wie sie die Deichrückverlegungen darstellen, beitragen und eine Folgenabschätzung unterstützen.