@phdthesis{Mutz2013,
  author    = {Mutz, Sebastian},
  title     = {Dynamic Statistical Modelling of Climate-Related Mass Balance Changes in Norway},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-114799},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2013},
  abstract  = {The glaciers in Norway exert a strong influence on Norwegian economy and society. Unlike many glaciers elsewhere and despite ongoing climate change and warming, many of them showed renewed advances and positive net mass changes in the 1980's and 1990's, followed by rapid retreats and mass losses since 2000. This difference in behaviour may be attributed to differences and shifts in the glaciological regime - the differences in the magnitude of impacts of climatic and non-climatic geographical factors on the glacier mass. This study investigates the influence of various atmospheric variables on mass balance changes of a selection of glaciers in Norway by means of Pearson correlation analyses and cross-validated stepwise multiple regression analyses. The analyses are carried out for three time periods (1949-2008, 1949-1988, 1989-2008) separately in order to take into consideration the possible shift in the glaciological regime in the 1980's. The atmospheric variables are constructed from ERA40 and NCEP/NCAR re-analysis datasets and include regional means of seasonal air temperature and precipitation rates and atmospheric circulation indices. The multiple regression models trained in these time periods are then applied to predictors reconstructed from the CMIP3 climate model dataset to generate an estimate for mass changes from the year 1950 to 2100. The temporal overlap of estimates and observations is used for calibration. Finally, observed atmospheric states in seasons that are characterised by a particularly positive or negative mass balance are categorised into time periods of modelled climate by the application of a Bayesian classification procedure. The strongest influence on winter mass balance is exerted by different indices of the North Atlantic Oscillation (NAO), Northern Annular Mode (NAM) and precipitation. The correlation coefficients and explained variances determined from the multiple regression analyses reveal an East-West gradient, suggesting a weaker influence of the NAO and NAM on glaciers underlying a more continental regime. The highest correlation coefficients and explained variances were obtained for the 1989-2008 time period, which might be due to a strong and predominantly positive phase of the NAO. Multi-model ensemble means of the estimates show a mass loss for all three eastern glaciers, while the estimates for the more maritime glaciers are ambivalent. In general, the estimates show a greater sensitivity to the training time period than to the greenhouse gas emission scenarios according to which the climates were simulated. The average net mass change by the end of 2100 is negative for all glaciers except for the northern Engabreen. For many glaciers, the Bayesian classification of observed atmospheric states into time periods of modelled climate reveals a decrease in probability of atmospheric states favouring extremes in winter, and an increase in probability of atmospheric states favouring extreme mass loss in summer for the distant future (2071-2100). This pattern of probabilities for the ablation season is most pronounced for glaciers underlying a continental and intermediate regime.},
  subject      = {Norwegen},
  language  = {en}
}
@phdthesis{Dietz2013,
  author    = {Dietz, Andreas},
  title     = {Central Asian Snow Cover Characteristics between 1986 and 2012 derived from Time Series of Medium Resolution Remote Sensing Data},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-101221},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2013},
  abstract  = {The eminent importance of snow cover for climatic, hydrologic, anthropogenic, and economic reasons has been widely discussed in scientific literature. Up to 50\% of the Northern Hemisphere is covered by snow at least temporarily, turning snow to the most prevalent land cover types at all. Depending on regular precipitation and temperatures below freezing point it is obvious that a changing climate effects snow cover characteristics fundamentally. Such changes can have severe impacts on local, national, and even global scale. The region of Central Asia is not an exception from this general rule, but are the consequences accompanying past, present, and possible future changes in snow cover parameters of particular importance. Being characterized by continental climate with hot and dry summers most precipitation accumulates during winter and spring months in the form of snow. The population in this 4,000,000 km² vast area is strongly depending on irrigation to facilitate agriculture. Additionally, electricity is often generated by hydroelectric power stations. A large proportion of the employed water originates from snow melt during spring months, implying that changes in snow cover characteristics will automatically affect both the total amount of obtainable water and the time when this water becomes available. The presented thesis explores the question how the spatial extent of snow covered surface has evolved since the year 1986. This investigation is based on the processing of medium resolution remote sensing data originating from daily MODIS and AVHRR sensors, thus forming a unique approach of snow cover analysis in terms of temporal and spatial resolution. Not only duration but also onset and melt of snow coverage are tracked over time, analyzing for systematic changes within this 26 years lasting time span. AVHRR data are processed from raw Level 1B orbit data to Level 3 thematic snow cover products. Both, AVHRR and MODIS snow maps undergo a further post-processing, producing daily full-area mosaics while completely eliminating inherent cloud cover. Snow cover parameters are derived based on these daily and cloud-free time series, allowing for a detailed analysis of current status and changes. The results confirm the predictions made by coarse resolution predictions from climate models: Central Asian snow cover is changing, posing new challenges for the ecosystem and future water supply. The changes, however, are not aimed at only one direction. Regions with decreasing snow cover exist as well as those where the duration of snow cover increases. A shift towards earlier snow cover start and melt can be observed, posing a serious challenge to water management authorities due to a changed runoff regime.},
  subject      = {Zentralasien},
  language  = {en}
}
@phdthesis{Fritsch2013,
  author    = {Fritsch, Sebastian},
  title     = {Spatial and temporal patterns of crop yield and marginal land in the Aral Sea Basin: derivation by combining multi-scale and multi-temporal remote sensing data with alight use efficiency model},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-87939},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2013},
  abstract  = {Irrigated agriculture in the Khorezm region in the arid inner Aral Sea Basin faces enormous challenges due to a legacy of cotton monoculture and non-sustainable water use. Regional crop growth monitoring and yield estimation continuously gain in importance, especially with regard to climate change and food security issues. Remote sensing is the ideal tool for regional-scale analysis, especially in regions where ground-truth data collection is difficult and data availability is scarce. New satellite systems promise higher spatial and temporal resolutions. So-called light use efficiency (LUE) models are based on the fraction of photosynthetic active radiation absorbed by vegetation (FPAR), a biophysical parameter that can be derived from satellite measurements. The general objective of this thesis was to use satellite data, in conjunction with an adapted LUE model, for inferring crop yield of cotton and rice at field (6.5 m) and regional (250 m) scale for multiple years (2003-2009), in order to assess crop yield variations in the study area. Intensive field measurements of FPAR were conducted in the Khorezm region during the growing season 2009. RapidEye imagery was acquired approximately bi-weekly during this time. The normalized difference vegetation index (NDVI) was calculated for all images. Linear regression between image-based NDVI and field-based FPAR was conducted. The analyses resulted in high correlations, and the resulting regression equations were used to generate time series of FPAR at the RapidEye level. RapidEye-based FPAR was subsequently aggregated to the MODIS scale and used to validate the existing MODIS FPAR product. This step was carried out to evaluate the applicability of MODIS FPAR for regional vegetation monitoring. The validation revealed that the MODIS product generally overestimates RapidEye FPAR by about 6 to 15 \%. Mixture of crop types was found to be a problem at the 1 km scale, but less severe at the 250 m scale. Consequently, high resolution FPAR was used to calibrate 8-day, 250 m MODIS NDVI data, this time by linear regression of RapidEye-based FPAR against MODIS-based NDVI. The established FPAR datasets, for both RapidEye and MODIS, were subsequently assimilated into a LUE model as the driving variable. This model operated at both satellite scales, and both required an estimation of further parameters like the photosynthetic active radiation (PAR) or the actual light use efficiency (LUEact). The latter is influenced by crop stress factors like temperature or water stress, which were taken account of in the model. Water stress was especially important, and calculated via the ratio of the actual (ETact) to the potential, crop-specific evapotranspiration (ETc). Results showed that water stress typically occurred between the beginning of May and mid-September and beginning of May and end of July for cotton and rice crops, respectively. The mean water stress showed only minor differences between years. Exceptions occurred in 2008 and 2009, where the mean water stress was higher and lower, respectively. In 2008, this was likely caused by generally reduced water availability in the whole region. Model estimations were evaluated using field-based harvest information (RapidEye) and statistical information at district level (MODIS). The results showed that the model at both the RapidEye and the MODIS scale can estimate regional crop yield with acceptable accuracy. The RMSE for the RapidEye scale amounted to 29.1 \% for cotton and 30.4 \% for rice, respectively. At the MODIS scale, depending on the year and evaluated at Oblast level, the RMSE ranged from 10.5 \% to 23.8 \% for cotton and from -0.4 \% to -19.4 \% for rice. Altogether, the RapidEye scale model slightly underestimated cotton (bias = 0.22) and rice yield (bias = 0.11). The MODIS-scale model, on the other hand, also underestimated official rice yield (bias from 0.01 to 0.87), but overestimated official cotton yield (bias from -0.28 to -0.6). Evaluation of the MODIS scale revealed that predictions were very accurate for some districts, but less for others. The produced crop yield maps indicated that crop yield generally decreases with distance to the river. The lowest yields can be found in the southern districts, close to the desert. From a temporal point of view, there were areas characterized by low crop yields over the span of the seven years investigated. The study at hand showed that light use efficiency-based modeling, based on remote sensing data, is a viable way for regional crop yield prediction. The found accuracies were good within the boundaries of related research. From a methodological viewpoint, the work carried out made several improvements to the existing LUE models reported in the literature, e.g. the calibration of FPAR for the study region using in situ and high resolution RapidEye imagery and the incorporation of crop-specific water stress in the calculation.},
  subject      = {Fernerkundung},
  language  = {en}
}
@phdthesis{Schwindt2013,
  author    = {Schwindt, Daniel},
  title     = {Permafrost in ventilated talus slopes below the timberline - A multi-methodological study on the ground thermal regime and its impact on the temporal variability and spatial heterogeneity of permafrost at three sites in the Swiss Alps},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-90099},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2013},
  abstract  = {In the central Alps permafrost can be expected above 2300 m a.s.l., at altitudes where mean annual air temperatures are below -1 °C. Isolated permafrost occurrences can be detected in north-exposed talus slopes, far below the timberline, where mean annual air temperatures are positive. Driving factors are assumed to be a low income of solar radiation, a thick organic layer with high insulation capacities as well as the thermally induced chimney effect. Aim of this study is to achieve a deeper understanding of the factors determining the site-specific thermal regime, as well as the spatially limited and temporally highly variable permafrost occurrences in vegetated talus slopes. Three supercooled talus slopes in the Swiss Alps were chosen for investigation. Substantially different characteristics were a central criterion in the selection of study sites. Located in the Upper Engadin, climatic conditions, altitude as well as dimensions of the talus slopes are comparable for the study sites Val Bever and Val Susauna; major differences are rooted in the nature of talus substrate and in humus- and vegetation distribution. Characteristics of the Br{\"u}eltobel site, located in the Appenzeller Alps, diverge with regard to climatic conditions, altitude and dimensions of the talus slope; humus- and vegetation compositions are comparable to the Val Susauna site. Confirmation and characterisation of ground ice is accomplished by the application of electrical resistivity and seismic refraction tomography. The estimation of the spatial permafrost distribution is based on quasi-3D resistivity imaging. For the confirmation of permafrost and the analysis of its temporal variability electrical resistivity monitoring arrays were constructed and installed at all study sites, to allow year-round measurements. In addition to resistivity monitoring, the - up to now - first seismic refraction tomography winter monitoring was conducted at the Val Susauna to analyse the permafrost evolution during the winter half-year. Investigations of the ground thermal regime were based on the analysis of temperature logger data. Besides recording air- and ground surface temperatures, focus was set on the temperature evolution in vents and in the organic layer. To analyse the relationship between permafrost distribution on the one hand and humus- and vegetation distribution on the other hand, an extensive mapping of humus characteristics and vegetation composition was conducted at Val Susauna. The existence of permafrost could be proven at all study sites. Spatially, permafrost bodies show a narrow transition to neighbouring, unfrozen areas. As observed at Val Susauna, the permafrost distribution strongly correlates with areas with exceptionally thick organic layer, high percentages of mosses and lichens in the undergrowth and dwarf grown trees. The temporal variability of permafrost has proven to be exceptionally high, with the magnitude of seasonal variations distinctly exceeding intra-annual changes. Thereby, the winter season is characterised by a significant supercooling. During snowmelt a growth in volumetric ice content is induced by refreezing of percolating meltwater on the supercooled talus. The results confirmed the fundamental influence of the chimney effect on the existence and temporal variability of permafrost in talus slopes. Divergences in the effectiveness of the thermal regime were detected between the study sites. These are based on differences in the nature of talus material, humus characteristics and vegetation composition. During summer, the organic material is usually dry at the daytime, inducing a high insulation capability and a protection of the subsurface against high atmospheric temperatures. Bouldery talus slopes typically show an organic layer that is fragmented by large boulders, which induces a strongly reduced insulation capability and allows an efficient heat exchange by convective airflow and percolating precipitation water. In the winter half-year, the thermal conductivity of the organic layer increases massively under moist or frozen conditions, allowing an efficient, conductive cooling of the talus material. The convective cooling in bouldery talus slopes affects an earlier onset and a higher magnitude of supercooling than under consistent humus conditions. Here, conductive heat flow is dominant and the cooling in autumn is buffered by a prolonged zero curtain. The snow cover has proven to be incapable of prohibiting an efficient supercooling of the talus slope in winter, almost independent from thickness.},
  subject      = {Engadin},
  language  = {en}
}