Zugl.: Göttingen, Univ., Diss., 2008

Herein work is researching extremes asymptotic of Pareto random values. Here is analyzing geometrically maximum (minimum) stability tasks, also asymptotically tasks, when succession value is geometrical and geometrically stability of lower extremes. Aim of this work is to check if Pareto distribution values are stable maximum and minimum distributions and to continue researches in the area of lower extremes structures. It was proved that maximum (minimum) distribution (when ) is geometrically stable maximum (minimum) distribution, while others – asymptotically k-stable. When , maximum (minimum) distribution is asymptotically stable, only maximum distribution is also Pareto distribution, but with the displacement, while other - asymptotically k-stable.

Techn. Universiẗat, Diss., 2002--Berlin.

Globally, species are experiencing geographical range shifts as a result of increased frequency and severity of extreme weather events exceeding their realized thermal niche boundaries. Using thermal limit approximations, relative heat indices can predict species extinction-colonization patterns over broad spatial scales. Locally, microclimate refugia can act as buffers against short term thermal extremes and improve species persistence probabilities. Opportunities to explore the role of microclimates in local species extinctions have recently emerged with advances in unmanned aerial vehicle (UAV) and thermal imaging technologies. My first chapter proposed a UAV-based methodology facilitating direct and accurate air temperature measurements at biologically relevant scales for butterfly species. These high-resolution microclimate measurements enabled broad-scale thermal limit approximation model applications to patch-level measurements using a verified thermal positioning index. In my second chapter, I evaluated the applicability of broad-scale models for predictions of local species distributions and abundances. The methodology proposed in Chapter 1 was used to generate patch-specific thermal position indices for butterfly species observed and surveyed in our study patches. Patch-level measurements of thermally tolerable area (overheating index) helped predict aspects of butterfly abundance, presence, and overall species richness, along with other environmental metrics that are relevant for butterfly biology. This thesis explores a frontier of direct UAV-based microclimate measurements and underscores the importance of considering thermal extremes to understand butterfly distribution and abundance, even in protected habitats.

In this thesis we present a numerical and analytical study of modelling extremes in chaotic dynamical systems. We study a range of examples with different dependency structures, and different clustering characteristics. We compare our analysis to the extreme statistics observed for financial returns data, and hence consider the modelling potential of using chaotic systems for understanding financial returns. As part of the study we use the block maxima approach and the peak over threshold method to compute the distribution parameters that arise in the corresponding extreme value distributions. We compare these computations to the theoretical answers, and moreover we obtain error bounds on the rate of convergence of these schemes. In particular we investigate the optimal block size when applying the block maxima method. Since the time series of observations on a dynamical system have dependency we must therefore go beyond the classic approach of studying extremes for independent identically distributed random variables. This is the main purpose of our study. As part of this thesis, we also study clustering in financial returns, and again investigate the potential of using dynamical systems models. Moreover we can also compare numerical quantification of clustering with theoretical approaches. As further work, we measure the dependency structures in our models using a rescaled range analysis. We also make preliminary investigations into record statistics for dynamical systems models, and relate our findings to record statistics in financial data, and to other models (such as random walk models).

The search for life beyond Earth is the driving force behind several future missions to Mars. An essential task in the lead-up to these missions is a critical assessment of the habitability for, and feasibility of, life. However, little research has been conducted on this issue, and our understanding of the plausibility for life on Mars remains unconstrained. Owing to the anoxic and iron-rich nature of Mars, microbial iron reduction (MIR) represents a compelling candidate metabolism to operate in the Martian subsurface, past and present. The objectives of this thesis are to address the feasibility of MIR on Mars by i) better defining the habitability of MIR on Earth, and ii) assessing the range and availability of organic electron donors in the subsurface of Earth and Mars. Samples collected from Mars-relevant environments on Earth were used to initiate MIR enrichment cultures at 4°C, 15°C and 30°C. Results indicate MIR is widespread in riverbed and subglacial sediments but not sediments from desert or recent volcanic plains. The iron-reducing microorganisms in subglacial enrichments are at least psychrotolerant and in some cases psychrophilc. Culture-independent methods highlighted the changes in diversity between temperature conditions for subglacial sediments, and indicated that members of the prolific MIR Geobacteraceae family are common. The genera Geobacter and Desulfosporosinus are responsible for MIR in the majority of enrichments. Long-term anoxia and the availability of redox constituents are the major factors controlling MIR in these environments. A MIR enrichment culture was unable to use shales and kerogens as the sole source of electron donors for MIR, despite the presence of known electron donors. Furthermore, MIR was inhibited by the presence of certain kerogens. The causes of inhibition are unknown, and are likely to be a combination of chemical and physical factors. Experiments were conducted to assess the ability of three pure strains and a MIR enrichment to use non-proteinogenic amino acids common to carbonaceous meteorites as electron donors for MIR. Results demonstrate that γ-aminobutyric acid served as an electron donor for the enrichment culture, but no other amino acids supported MIR by this or other iron-reducing cultures. The D-form of chiral amino acids was found to exert a strong inhibitory effect, which decreased in line with concentration. Theoretical calculations using published meteoritic accretion rates onto the surface of Mars indicate that the build up inhibitory amino acids may place important constrains on habitability over geologic time scales. Contamination of a pure strain of Geobacter metallireducens with a strain of Clostridium revealed a syntrophic relationship between these microorganisms. Anaerobic heterotrophs are likely to play an important role in maintaining an available supply of electron donors for MIR and similar chemoorganic metabolisms operating in the subsurface. This research indicates that MIR remains a feasible metabolism to operate on Mars providing a readily available redox couple is present. However, given the observed inhibition in the presence of bulk carbonaceous material and certain amino acids found in meteorites, the use of extraterrestrial carbonaceous material in the Martian subsurface for microbial iron reduction is questionable, and should be the focus of future research.

Canadian Prairie agriculture, in general, is expected to benefit under climate change with increasing mean temperatures projected for the immediate future. However, a number of knowledge gaps still exist. Foremost among these is the measurement of the effects of extreme climate events in a given year as well as their long-term impact on the supply of agricultural products, and also the financial situation of farms. In addition, the economic impacts of climate change on livestock operations are relatively under-studied. In particular, knowledge of the impacts on Prairie beef cattle remains more guesswork than research-based evidence. This dissertation assesses the impact of changes in the normal climate as well as the impact of climate extremes by including projected inter-annual climate variability. The economic impact of these changes on crops, beef cattle activities and the viability of farms in mixed operation settings is measured. Correspondingly, this work presents alternative adaptation measures and their likely use in managing mixed farm operations for future extreme weather events. For the analysis, two study sites are selected: (1) the Oldman River Basin of Alberta, called Pincher Creek, and (2) the Swift Current Creek Basin of Saskatchewan, called Swift Current. This study is a part of a larger project entitled “Vulnerability and Adaptation to Climate Extremes in the Americas” and the study sites are intended to represent the project catchment areas in the provinces of Alberta and Saskatchewan. I develop what I call a MF-CCE model (Mixed Farm model for the economic impact assessment of Climate Change and Extremes). The MF-CCE is a whole farm simulation model that integrates models of beef cattle production, crop production and climate changes into farm level economic decisions. Simulations are conducted over a 30-year period in each climate scenario: the first of these is a baseline climate scenario from 1971-2000, and I also simulate future climate change impacts for the 2041-2070 era. The modelled farms produce enough crops, hay and pasture to support the beef cattle feed demand. Pasture demand and supply are linked by specific pasture requirements and productivity. Beef herd feed grain demand and on-farm supply are linked by a linear programming optimization algorithm. Crop mix for the market is selected through the development of a multi-year linear programming problem that maximizes the present value of gross margins. Crop and hay productivity are estimated through the Food and Agriculture Organization’s (FAO’s) AquaCrop (version 3) modeling framework, while annual pasture productivity is estimated using the Forage Calculator for Native Rangeland obtained from the Saskatchewan Research Council (SRC). The AquaCrop is a water-driven crop simulation model, termed a crop water productivity (WP) model which simulates the yield response of herbaceous crops to water availability and use. The model is believed to be superior in simulating crop yield in the conditions where water is a key limiting factor in crop production (FAO, 2011). Summarizing the results of the simulation, prairie crop production is expected to benefit under the simulated climate change scenario. Increases in crop productivity generate about 60% higher profits in the Pincher Creek site and about 57% more for the Swift Current site. Due to increases in grain and hay productivity, more area is made available to produce grain for the market. This effectively doubles the crop net return at the Pincher Creek site and triples the crop return at the Swift Current site. A consideration of future pasture response to the climate change scenario is important in estimating climate change consequences for live beef production as well as on the economic return of a mixed farm. If the pasture productivity decreases, as assumed under the regular pasture yield scenario in the study, appropriate adaptation is necessary for the farm to benefit from future climate change. Under this scenario, beef production activities in the future are projected to gain by 50% in Pincher Creek and 40% in Swift Current compared to the baseline scenario. If pasture productivity under the future scenario increases in a manner similar to crop yield increases, existing pastureland will be enough to maintain beef herds into the future. In turn, this strategy will mitigate the cost of beef herd adaptation during climate extremes, and instead gains from beef cattle production would be 35% higher in Swift Current and 6% higher in Pincher Creek relative to gains under regular pasture yield conditions. At the farm level, with beef cattle and crop production combined, substantial gains are projected for both of the study sites. Farm net profit is estimated to increase by more than 35% at the Pincher Creek site and more than 140% at the Swift Current site under the future scenario. Income risk will also be lower in this scenario, as highlighted by a lower coefficient of variation of net farm profit. Farm financial indicators tracked in this study – farm cash flow, family cash flow, and farm net worth – all indicate that the farm’s financial position will be much better in the future climate scenario. At the Pincher Creek site, a few problematic liquidity events are forecasted under the future climate scenario, but in light of significant improvements in other economic indicators, overall, this effect is negligible. The appropriate choice of adaptation strategies for managing beef herds during extreme climate events plays an important role in determining the profitability of not only beef cattle activities, but also the financial position at the whole farm level. However, the choice of adaptations is contextual: the preference of adaptation strategy differs across activities, farms and period of study. For beef cattle activities, maintaining the beef herd without any compromise on herd size and implementing a regular feeding plan is preferred to other adaptation alternatives. At the whole farm level for the Pincher Creek site, culling the herd is preferred under the baseline scenario, while the purchasing feed option is preferred under the future climate scenario. At the Swift Current site, culling the herd is the preferred strategy under both scenarios. Commodity prices and the cost of farm inputs profoundly affect the economic position of the farm under the future climate change scenario. If commodity prices and cost of production remain the same as under the baseline scenario, future farm net profit is estimated to be 50% higher for the Pincher Creek site and about 25% higher for the Swift Current site, compared to profits under projected future prices. This result implies that the pure effect of climate change could be much higher if costs and prices do not change. Results of this dissertation indicate that average Prairie mixed farms, as represented by these study farms, remain economically viable under both the baseline and future scenarios. The results also suggest that the overall gain to these farms under a future climate change scenario would be positive. The potential severity of extreme climate events in the future, at least for the future scenario period simulated in this study, would not be significant enough to threaten the future economic viability of Prairie agriculture. However, the research also highlights the importance of policies that support farmers when they endure losses in years of extreme climate events. Further research on evaluating different Best Management Practices (BMPs) in dealing with droughts, for example, would be helpful in taking advantage of future climate change. Policy development to enhance the longer-term adaptive capacity of Prairie farmers, such as development of early warning systems for climate extremes, or the development of drought tolerant cultivars of crops and forages, would be most helpful in coping with climate extremes in the future.

The climate of the Neoproterozoic Snowball Earth is tested in the UKMO Unified Model, specifically the HadCM3 climate model. The model is largely left unchanged, but the boundary conditions, both external and initial, are adjusted to create experiments based on the Snowball Earth hypothesis. The model can reproduce multiple equilibrium climates, as have been seen in energy balance models of the Earth's climate. The modelled present day and Neoproterozoic versions of Earth can both reproduce both ice capped and ice covered climate states. Neither can reproduce a climate which remains ice free throughout the year, even with an equilibrated ocean or elevated levels of C02. In all cases the ice free climate reverts toward the ice capped climate after the first polar winter. The modelled Neoproterozoic ice covered climate, that is the climate of Snowball Earth, has a climate very different from the present day. These changes are mostly driven by the lower thermal inertia, latitudinal temperature differences and the changed meridional circulation that results. The weather of the modelled Snowball Earth climate is also very different, dom- inated by a strong diurnal variation due to solar heating, as opposed to the more varied weather in the present day. The model responds well to the conditions of the Snowball Earth climate, with temperatures similar to those predicted by a simple physical model. The model responds less well to high levels of C02 in the Snowball Earth climate. The ice model also allows excessive heat and moisture to escape from the ocean into the atmosphere compared to that that would be predicted from solid ice coverage of the ocean. The exit from a Snowball Earth state was also tested within the model. Neither an decrease in albedo nor an increase in CO2 is unable to increase the temperature of the climate system sufficiently to exit the Snowball Earth state.

<p>The hydrologic cycle may be described in essence as the process of water rising and falling in its various phases between land and atmosphere. In this minimal description of the hydrologic cycle two features come into focus: intermittency and irreversibility. In this dissertation intermittency and irreversibility are investigated broadly in the soil-plant-atmosphere system. The theory of intermittency and irreversibility is addressed here in three ways: (1) through its effect on components of the soil-plant-atmosphere system, (2) through development of a measure of the degree of irreversibility in time-series, and (3) by the investigation of the dynamical sources of this intermittency. First, soil infiltration and spring frost risk are treated as two examples of hydrologic intermittency with very different characters and implications for the soil plant system. An investigation of the water budget in simplified soil moisture models reveals that simple bucket models of infiltration perform well against more accurate representation of intra-storm infiltration dynamics in determining the surface water partitioning. Damaging spring frost is presented as a ``biologically-defined extreme event'' and thus as a more subtle form of hydrologic intermittency. This work represents the first theoretical development of a biologically-defined extreme and highlights the importance of the interplay between daily temperature mean and variance in determining the changes in damaging frost risk in a warming climate. Second, a statistical measure of directionality/asymmetry is developed for stationary time-series based on analogies with the theory of nonequilibrium thermodynamics. This measure is then applied to a set of DNA sequences as an example of a discrete sequence with limited state-space. The DNA sequences are found to be statistically asymmetric and further that the local degree of asymmetry is a reliable indicator of the coding/noncoding status of the DNA segment. Third, the phenomenology of rainfall occurrence is compared with canonical examples of dynamical intermittency to determine whether these simple dynamical features may display a dominant signature in rainfall processes. Summer convective rainfall is found to be broadly consistent with Type-III intermittency. Following on this result we studied daytime atmospheric boundary layer dynamics with a view toward developing simplified models that may further elucidate the interaction the interaction between land surface conditions and convective rainfall triggering.</p> / Dissertation

The current generation of climate models in the Coupled Model Intercomparison Project Phase 5 (CMIP5) is used to assess the future changes in daily precipitation and its extremes. The simple average of all the models, i.e. the multi-model ensemble mean (MMEM), has been widely used due to its simplicity and better performance than most individual models. Weighting techniques are also proposed to deal with the systematic biases within the models. However, both methods are designed to reduce the uncertainties for the study of climate mean state. They will induce problems when the climate extremes are of interest. We utilize a Bayesian weighting method to investigate the rainfall mean state and perform a probability density function based assessment of daily rainfall extremes. Satellite measurement is used to evaluate the short historical period. The weighting method can be only applied to regions rather than hemispheric scale, and thus three tropical regions including the Amazon, Congo, and Southeast Asia are studied. The method based on the Gamma distribution for daily precipitation is demonstrated to perform much better than the MMEM with respect to the extreme events. A use of the Kolmogorov-Smirnov statistic for the distribution assessment indicates the method is more applicable in three tropical wet regions over land mentioned above. This is consistent with previous studies showing the Gamma distribution is more suitable for daily rainfall in wet regions. Both methods provide consistent results. The three regions display significant changes at the end of the 21st century. The Amazon will be drier, while the Congo will not have large changes in mean rainfall. However, both of the Amazon and Congo will have large rainfall variability, implying more droughts and floods. The Amazon will have 7.5% more little-rain days (defined as > 0.5 mm/d) and 4.5 mm/d larger 95th percentile for 2092-2099, and the Congo will have 2.5% more little-rain days and 1 mm/d larger 95th percentile. Southeast Asia will be dryer in the western part and wetter in the eastern part, which is consistent with the different changes in the 5th percentile. It will also experience heavier rainfall events with much larger increases in the 95th percentile. The future changes, especially the increase in rainfall extremes, are very likely associated with the strengthening of hydrological cycle. / text