@phdthesis{Vogt2014,
  author    = {Vogt, Gernot},
  title     = {Future changes and signal analyses of climate means and extremes in the Mediterranean Area deduced from a CMIP3 multi-model ensemble},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-117369},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2014},
  abstract  = {Considering its social, economic and natural conditions the Mediterranean Area is a highly vulnerable region by designated affections of climate change. Furthermore, its climatic characteristics are subordinated to high natural variability and are steered by various elements, leading to strong seasonal alterations. Additionally, General Circulation Models project compelling trends in specific climate variables within this region. These circumstances recommend this region for the scientific analyses conducted within this study. Based on the data of the CMIP3 database, the fundamental aim of this study is a detailed investigation of the total variability and the accompanied uncertainty, which superpose these trends, in the projections of temperature, precipitation and sea-level pressure by GCMs and their specific realizations. Special focus in the whole study is dedicated to the German model ECHAM5/MPI-OM. Following this ambition detailed trends and mean values are calculated and displayed for meaningful time periods and compared to reanalysis data of ERA40 and NCEP. To provide quantitative comparison the mentioned data are interpolated to a common 3x3° grid. The total amount of variability is separated in its contributors by the application of an Analysis of Variance (ANOVA). For individual GCMs and their ensemble-members this is done with the application of a 1-way ANOVA, separating a treatment common to all ensemble-members and variability perturbating the signal given by different initial conditions. With the 2-way ANOVA the projections of numerous models and their realizations are analysed and the total amount of variability is separated into a common treatment effect, a linear bias between the models, an interaction coefficient and the residuals. By doing this, the study is fulfilled in a very detailed approach, by considering yearly and seasonal variations in various reasonable time periods of 1961-2000 to match up with the reanalysis data, from 1961-2050 to provide a transient time period, 2001-2098 with exclusive regard on future simulations and 1901-2098 to comprise a time period of maximum length. The statistical analyses are conducted for regional-averages on the one hand and with respect to individual grid-cells on the other hand. For each of these applications the SRES scenarios of A1B, A2 and B1 are utilized. Furthermore, the spatial approach of the ANOVA is substituted by a temporal approach detecting the temporal development of individual variables. Additionally, an attempt is made to enlarge the signal by applying selected statistical methods. In the detailed investigation it becomes evident, that the different parameters (i.e. length of temporal period, geographic location, climate variable, season, scenarios, models, etc…) have compelling impact on the results, either in enforcing or weakening them by different combinations. This holds on the one hand for the means and trends but also on the other hand for the contributions of the variabilities affecting the uncertainty and the signal. While temperature is a climate variable showing strong signals across these parameters, for precipitation mainly the noise comes to the fore, while for sea-level pressure a more differentiated result manifests. In turn, this recommends the distinguished consideration of the individual parameters in climate impact studies and processes in model generation, as the affecting parameters also provide information about the linkage within the system. Finally, an investigation of extreme precipitation is conducted, implementing the variables of the total amount of heavy precipitation, the frequency of heavy-precipitation events, the percentage of this heavy precipitation to overall precipitation and the mean daily intensity from events of heavy precipitation. Each time heavy precipitation is defined to exceed the 95th percentile of overall precipitation. Consecutively mean values of these variables are displayed for ECHAM5/MPI-OM and the multi-model mean and climate sensitivities, by means of their difference between their average of the past period of 1981-2000 and the average of one of the future periods of 2046-2065 or 2081-2100. Following this investigation again an ANOVA is conducted providing a quantitative measurement of the severity of change of trends in heavy precipitation across several GCMs. Besides it is a difficult task to account for extreme precipitation by GCMs, it is noteworthy that the investigated models differ highly in their projections, resulting partially in a more smoothed and meaningful multi-model mean. Seasonal alterations of the strength of this behaviour are quantitatively supported by the ANOVA.},
  subject      = {Klimaschwankung},
  language  = {en}
}
@phdthesis{Asam2014,
  author    = {Asam, Sarah},
  title     = {Potential of high resolution remote sensing data for leaf area index derivation using statistical and physical models},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-108399},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2014},
  abstract  = {Information on the state of the terrestrial vegetation cover is important for several ecological, economical, and planning issues. In this regard, vegetation properties such as the type, vitality, or density can be described by means of continuous biophysical parameters. One of these parameters is the leaf area index (LAI), which is defined as half the total leaf area per unit ground surface area. As leaves constitute the interface between the biosphere and the atmosphere, the LAI is used to model exchange processes between plants and their environment. However, to account for the variability of ecosystems, spatially and temporally explicit information on LAI is needed both for monitoring and modeling applications. Remote sensing aims at providing such information. LAI is commonly derived from remote sensing data by empirical-statistical or physical models. In the first approach, an empirical relationship between LAI measured in situ and the corresponding canopy spectral signature is established. Although this method achieves accurate LAI estimates, these relationships are only valid for the place and time at which the field data were sampled, which hampers automated LAI derivation. The physical approach uses a radiation transfer model to simulate canopy reflectance as a function of the scene's geometry and of leaf and canopy parameters, from which LAI is derived through model inversion based on remote sensing data. However, this model inversion is not stable, as it is an under-determined and ill-posed problem. Until now, LAI research focused either on the use of coarse resolution remote sensing data for global applications, or on LAI modeling over a confined area, mostly in forest and crop ecosystems, using medium to high spatial resolution data. This is why to date no study is available in which high spatial resolution data are used for LAI mapping in a heterogeneous, natural landscape such as alpine grasslands, although a growing amount of high spatial and temporal resolution remote sensing data would allow for an improved environmental monitoring. Therefore, issues related to model parameterization and inversion regularization techniques improving its stability have not yet been investigated for this ecosystem. This research gap was taken up by this thesis, in which the potential of high spatial resolution remote sensing data for grassland LAI estimation based on statistical and radiation transfer modeling is analyzed, and the achieved accuracy and robustness of the two approaches is compared. The objectives were an ecosystem-adapted radiation transfer model set-up and an optimized LAI derivation in mountainous grassland areas. Multi-temporal LAI in situ measurements as well as time series of RapidEye data from 2011 and 2012 over the catchment of the River Ammer in the Bavarian alpine upland were used. In order to obtain accurate in situ data, a comparison of the LAI derivation algorithms implemented in the LAI-2000 PCA instrument with destructively measured LAI was performed first. For optimizing the empirical-statistical approach, it was then analyzed how the selection of vegetation indices and regression models impacts LAI modeling, and how well these models can be transferred to other dates. It was shown that LAI can be derived with a mean accuracy of 80 \% using contemporaneous field data, but that the accuracy decreases to on average 51 \% when using these models on remote sensing data from other dates. The combined use of several data sets to create a regression which is used for LAI derivation at different points in time increased the LAI estimation accuracy to on average 65 \%. Thus, reduced field measurement labor comes at the cost of LAI error rates being increased by 10 - 30 \% as long as at least two campaigns are conducted. Further, it was shown that the use of RapidEye's red edge channel improves the LAI derivation by on average 5.4 \%. With regard to physical LAI modeling, special interest lay in assessing the accuracy improvements that can be achieved through model set-up and inversion regularization techniques. First, a global sensitivity analysis was applied to the radiation transfer model in order to identify the most important model parameters and most sensitive spectral features. After model parameterization, several inversion regularizations, namely the use of a multiple sample solution, the additional use of vegetation indices, and the addition of noise, were analyzed. Further, an approach to include the local scene's geometry in the retrieval process was introduced to account for the mountainous topography. LAI modeling accuracies of in average 70 \% were achieved using the best combination of regularization techniques, which is in the upper range of accuracies that were achieved in the few existing other grassland studies based on in situ or air-borne measured hyperspectral data. Finally, further physically derived vegetation parameters and inversion uncertainty measures were evaluated in detail to identify challenging modeling conditions, which was mostly neglected in other studies. An increased modeling uncertainty for extremely high and low LAI values was observed. This indicates an insufficiently wide model parameterization and a canopy deviation from model assumptions on some fields. Further, the LAI modeling accuracies varied strongly between the different scenes. From this observation it can be deduced that the radiometric quality of the remote sensing data, which might be reduced by atmospheric effects or unexpected surface reflectances, exerts a high influence on the LAI modeling accuracy. The major findings of the comparison between the empirical-statistical and physical LAI modeling approaches are the higher accuracies achieved by the empirical-statistical approach as long as contemporaneous field data are available, and the computationally efficiency of the statistical approach. However, when no or temporally unfitting in situ measurements are available, the physical approach achieves comparable or even higher accuracies. Furthermore, radiation transfer modeling enables the derivation of other leaf and canopy variables useful for ecological monitoring and modeling applications, as well as of pixel-wise uncertainty measures indicating the robustness and reliability of the model inversion and LAI derivation procedure. The established look-up tables can be used for further LAI derivation in Central European grassland also in other years. The use of high spatial resolution remote sensing data for LAI derivation enables a reliable land cover classification and thus a reduced LAI mapping error due to misclassifications. Furthermore, the RapidEye pixels being smaller than individual fields allow for a radiation transfer model inversion over homogeneous canopies in most cases, as canopy gaps or field parcels can be clearly distinguished. However, in case of unexpected local surface conditions such as blooming, litter, or canopy gaps, high spatial resolution data show corresponding strong deviations in reflectance values and hence LAI estimation, which would be reduced using coarser resolution data through the balancing effect of the surrounding surface reflectances. An optimal pixel size with regard to modeling accuracy hence depends on the canopy and landscape structure. Furthermore, a reduced spatial resolution would enable a considerable acceleration of the LAI map derivation. This illustration of the potential of RapidEye data and of the challenges associated to LAI derivation in heterogeneous grassland areas contributes to the development of robust LAI estimation procedures based on new and upcoming, spatially and temporally high resolution remote sensing imagery such as Landsat 8 and Sentinel-2.},
  subject      = {Optische Fernerkundung},
  language  = {en}
}
@phdthesis{Roedder2014,
  author    = {R{\"o}dder, Tobias},
  title     = {Spatio-temporal assessment of dynamics in discontinuous mountain permafrost - Investigation of small-scale influences on the ground thermal regime and active layer processes during snow melt},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-90629},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2014},
  abstract  = {The discontinuous mountain permafrost zone is characterized by its heterogeneous distribution of frozen ground and a small-scale variability of the ground thermal regime. Large parts of these areas are covered by glacial till and sediments that were exposed after the recession of the glaciers since the 19th century. As response to changed climatic conditions permafrost-affected areas will lose their ability as sediment storage and on the contrary, they will act as source areas for unconsolidated debris. Along with modified precipitation patterns the degradation of the discontinuous mountain permafrost zone will (temporarily) increase its predisposition for mass movement processes and thus has to be monitored in a differentiated way. Therefore, the spatio-temporal dynamics of frozen ground are assessed in this study based on results obtained in three glacier forefields in the Engadin (Swiss Alps) and at the Zugspitze (German Alps). Sophisticated techniques are required to uncover structural differences in the subsurface. Thus, the applicability of advanced geophysical methods is tested for alpine environments and proved by the good 3D-delineation of a permafrost body and by the detection of detailed processes in the active layer during snow melt. Electrical resistivity tomography (ERT) approaches (quasi-3D, daily monitoring) reveal their capabilities to detect subsurface resistivity changes both, in space and time. Processes and changes in regard to liquid water content and ice content are observed to exist at short distances even though the active layer is not subject to a considerable thickening over the past 7 years. The stability of the active layer is verified by borehole temperature data. No synchronous trend is recognized in permafrost temperatures and together with multi-annual electrical resistivity data they indicate degradation and aggradation processes to occur at the same time. Different heat transfer mechanisms, especially during winter, are recognized by means of temperature sensors above, at, and beneath the surface. Based on surface and borehole temperature data the snow cover is assessed as the major controlling factor for the thermal regime on a local scale. Beyond that, the debris size of the substrate, which modifies the snow cover and regulates air exchange processes above the ground, plays a crucial role as an additional buffer layer. A fundamental control over the stability of local permafrost patches is attributed to the ice-rich transient layer at the base of the active layer. The refreezing of melt water in spring is illustrated with diurnal ERT monitoring data from glacier forefield Murt{\`e}l. Based on these ERT and borehole temperature data a conceptual model of active layer processes between autumn and spring is developed. The latent heat that is inherent in the transient layer protects the permafrost beneath from additional energy input from the surface as long as the refreezing of melt water in spring prevails and sufficient ice is build up each spring. Permafrost sites without a transient layer show considerably higher temperatures at their table and are more prone to degradation in the years and decades ahead. As main investigation area a glacier forefield beneath the summits of Piz Murt{\`e}l and Piz Corvatsch in the Swiss Engadin was chosen. It is located west of the well-known rock glacier Murt{\`e}l. Here, a permafrost body inside and adjacent to the lateral moraine was investigated and could be delineated very well. In the surrounding glacier forefield no further indications of permafrost occurrence could be made. Geophysical data and temperature values from the surface and from a permafrost borehole were compared with long-term data from proximate glacier forefield Muragl (Engadin). Results from both sites show a considerable stability of the active layer depth in summer while at the same time geophysical data demonstrate annual changes in the amount of liquid water content and ice content in the course of years. A third investigation area is located in the German Alps. The Zugspitzplatt is a high mountain valley with considerably more precipitation and thicker snow cover compared to both Swiss sites. In close proximity to the present glacier and at a large talus slope beneath the summit crest ground ice could be observed. The high subsurface resistivity values and comparable data from existing studies at the Zugspitze may indicate the presence of sedimentary ice in the subsurface of the karstified Zugspitzplatt. Based on these complementary data from geophysical and temperature measurements as well as geomorphological field mapping the development of permafrost in glacier forefields under climate change conditions is analyzed with cooperation partners from the SPCC project. Ground temperature simulations forced with long-term climatological data are modeled to assess future permafrost development in glacier forefield Murt{\`e}l. Results suggest that permafrost is stable as long as the ice-rich layer between the active layer and the permafrost table exists. After a tipping point is reached, the disintegration of frozen ground starts to proceed rapidly from the top.},
  subject      = {Engadin},
  language  = {en}
}