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Floods continue to be the leading cause of economic damages and fatalities among natural disasters worldwide. As future climate and exposure changes are projected to intensify these damages, the need for more accurate and scalable flood risk models is rising. Over the past decade, macro-scale flood risk models have evolved from initial proof-of-concepts to indispensable tools for decision-making at global-, nationaland, increasingly, the local-level. This progress has been propelled by the advent of high-performance computing and the availability of global, space-based datasets. However, despite such advancements, these models are rarely validated and consistently fall short of the accuracy achieved by high-resolution local models. While capabilities have improved, significant gaps persist in understanding the behaviours of such macro-scale models, particularly their tendency to overestimate risk. This dissertation aims to address such gaps by examining the scale transfers inherent in the construction and application of coarse macroscale models. To achieve this, four studies are presented that, collectively, address exposure, hazard, and vulnerability components of risk affected by upscaling or downscaling.
The first study focuses on a type of downscaling where coarse flood hazard inundation grids are enhanced to a finer resolution. While such inundation downscaling has been employed in numerous global model chains, ours is the first study to focus specifically on this component, providing an evaluation of the state of the art and a novel algorithm. Findings demonstrate that our novel algorithm is eight times faster than existing methods, offers a slight improvement in accuracy, and generates more physically coherent flood maps in hydraulically challenging regions. When applied to a case study, the algorithm generated a 4m resolution inundation map from 30m hydrodynamic model outputs in 33 s, a 60-fold improvement in runtime with a 25% increase in RMSE compared with direct hydrodynamic modelling. All evaluated downscaling algorithms yielded better accuracy than the coarse hydrodynamic model when compared to observations, demonstrating similar limits of coarse hydrodynamic models reported by others. The substitution of downscaling into flood risk model chains, in place of high-resolution modelling, can drastically improve the lead time of impactbased forecasts and the efficiency of hazard map production. With downscaling, local regions could obtain high resolution local inundation maps by post-processing a global model without the need for expensive modelling or expertise.
The second study focuses on hazard aggregation and its implications for exposure, investigating implicit aggregations commonly used to intersect hazard grids with coarse exposure models. This research introduces a novel spatial classification framework to understand the effects of rescaling flood hazard grids to a coarser resolution. The study derives closed-form analytical solutions for the location and direction of bias from flood grid aggregation, showing that bias will always be present in regions near the edge of inundation. For example, inundation area will be positively biased when water depth grids are aggregated, while volume will be negatively biased when water elevation grids are aggregated. Extending the analysis to effects of hazard aggregation on building exposure, this study shows that exposure in regions at the edge of inundation are an order of magnitude more sensitive to aggregation errors than hazard alone. Among the two aggregation routines considered, averaging water surface elevation grids better preserved flood depths at buildings than averaging of water depth grids. The study provides the first mathematical proof and generalizeable treatment of flood hazard grid aggregation, demonstrating important mechanisms to help flood risk modellers understand and control model behaviour.
The final two studies focus on the aggregation of vulnerability models or flood damage functions, investigating the practice of applying per-asset functions to aggregate exposure models. Both studies extend Jensen’s inequality, a well-known 1906 mathematical proof, to demonstrate how the aggregation of flood damage functions leads to bias. Applying Jensen’s proof in this new context, results show that typically concave flood damage functions will introduce a positive bias (overestimation) when aggregated. This behaviour was further investigated with a simulation experiment including 2 million buildings in Germany, four global flood hazard simulations and three aggregation scenarios. The results show that positive aggregation bias is not distributed evenly in space, meaning some regions identified as “hot spots of risk” in assessments may in fact just be hot spots of aggregation bias. This study provides the first application of Jensen’s inequality to explain the overestimates reported elsewhere and advice for modellers to minimize such artifacts.
In total, this dissertation investigates the complex ways aggregation and disaggregation influence the behaviour of risk models, focusing on the scale-transfers underpinning macro-scale flood risk assessments. Extending a key finding of the flood hazard literature to the broader context of flood risk, this dissertation concludes that all else equal, coarse models overestimate risk. This dissertation goes beyond previous studies by providing mathematical proofs for how and where such bias emerges in aggregation routines, offering a mechanistic explanation for coarse model overestimates. It shows that this bias is spatially heterogeneous, necessitating a deep understanding of how rescaling may bias models to effectively reduce or communicate uncertainties. Further, the dissertation offers specific recommendations to help modellers minimize scale transfers in problematic regions. In conclusion, I argue that such aggregation errors are epistemic, stemming from choices in model structure, and therefore hold greater potential and impetus for study and mitigation. This deeper understanding of uncertainties is essential for improving macro-scale flood risk models and their effectiveness in equitable, holistic, and sustainable flood management.
The European Water Framework Directive (WFD) has identified river morphological alteration and diffuse pollution as the two main pressures affecting water bodies in Europe at the catchment scale. Consequently, river restoration has become a priority to achieve the WFD's objective of good ecological status. However, little is known about the effects of stream morphological changes, such as re-meandering, on in-stream nitrate retention at the river network scale. Therefore, catchment nitrate modeling is necessary to guide the implementation of spatially targeted and cost-effective mitigation measures. Meanwhile, Germany, like many other regions in central Europe, has experienced consecutive summer droughts from 2015-2018, resulting in significant changes in river nitrate concentrations in various catchments. However, the mechanistic exploration of catchment nitrate responses to changing weather conditions is still lacking.
Firstly, a fully distributed, process-based catchment Nitrate model (mHM-Nitrate) was used, which was properly calibrated and comprehensively evaluated at numerous spatially distributed nitrate sampling locations. Three calibration schemes were designed, taking into account land use, stream order, and mean nitrate concentrations, and they varied in spatial coverage but used data from the same period (2011–2019). The model performance for discharge was similar among the three schemes, with Nash-Sutcliffe Efficiency (NSE) scores ranging from 0.88 to 0.92. However, for nitrate concentrations, scheme 2 outperformed schemes 1 and 3 when compared to observed data from eight gauging stations. This was likely because scheme 2 incorporated a diverse range of data, including low discharge values and nitrate concentrations, and thus provided a better representation of within-catchment heterogenous. Therefore, the study suggests that strategically selecting gauging stations that reflect the full range of within-catchment heterogeneity is more important for calibration than simply increasing the number of stations.
Secondly, the mHM-Nitrate model was used to reveal the causal relations between sequential droughts and nitrate concentration in the Bode catchment (3200 km2) in central Germany, where stream nitrate concentrations exhibited contrasting trends from upstream to downstream reaches. The model was evaluated using data from six gauging stations, reflecting different levels of runoff components and their associated nitrate-mixing from upstream to downstream. Results indicated that the mHM-Nitrate model reproduced dynamics of daily discharge and nitrate concentration well, with Nash-Sutcliffe Efficiency ≥ 0.73 for discharge and Kling-Gupta Efficiency ≥ 0.50 for nitrate concentration at most stations. Particularly, the spatially contrasting trends of nitrate concentration were successfully captured by the model. The decrease of nitrate concentration in the lowland area in drought years (2015-2018) was presumably due to (1) limited terrestrial export loading (ca. 40% lower than that of normal years 2004-2014), and (2) increased in-stream retention efficiency (20% higher in summer within the whole river network). From a mechanistic modelling perspective, this study provided insights into spatially heterogeneous flow and nitrate dynamics and effects of sequential droughts, which shed light on water-quality responses to future climate change, as droughts are projected to be more frequent.
Thirdly, this study investigated the effects of stream restoration via re-meandering on in-stream nitrate retention at network-scale in the well-monitored Bode catchment. The mHM-Nitrate model showed good performance in reproducing daily discharge and nitrate concentrations, with median Kling-Gupta values of 0.78 and 0.74, respectively. The mean and standard deviation of gross nitrate retention efficiency, which accounted for both denitrification and assimilatory uptake, were 5.1 ± 0.61% and 74.7 ± 23.2% in winter and summer, respectively, within the stream network. The study found that in the summer, denitrification rates were about two times higher in lowland sub-catchments dominated by agricultural lands than in mountainous sub-catchments dominated by forested areas, with median ± SD of 204 ± 22.6 and 102 ± 22.1 mg N m-2 d-1, respectively. Similarly, assimilatory uptake rates were approximately five times higher in streams surrounded by lowland agricultural areas than in those in higher-elevation, forested areas, with median ± SD of 200 ± 27.1 and 39.1 ± 8.7 mg N m-2 d-1, respectively. Therefore, restoration strategies targeting lowland agricultural areas may have greater potential for increasing nitrate retention. The study also found that restoring stream sinuosity could increase net nitrate retention efficiency by up to 25.4 ± 5.3%, with greater effects seen in small streams. These results suggest that restoration efforts should consider augmenting stream sinuosity to increase nitrate retention and decrease nitrate concentrations at the catchment scale.
Today, more than half of the world’s population lives in urban areas. With a high density of population and assets, urban areas are not only the economic, cultural and social hubs of every society, they are also highly susceptible to natural disasters. As a consequence of rising sea levels and an expected increase in extreme weather events caused by a changing climate in combination with growing cities, flooding is an increasing threat to many urban agglomerations around the globe.
To mitigate the destructive consequences of flooding, appropriate risk management and adaptation strategies are required. So far, flood risk management in urban areas is almost exclusively focused on managing river and coastal flooding. Often overlooked is the risk from small-scale rainfall-triggered flooding, where the rainfall intensity of rainstorms exceeds the capacity of urban drainage systems, leading to immediate flooding. Referred to as pluvial flooding, this flood type exclusive to urban areas has caused severe losses in cities around the world. Without further intervention, losses from pluvial flooding are expected to increase in many urban areas due to an increase of impervious surfaces compounded with an aging drainage infrastructure and a projected increase in heavy precipitation events. While this requires the integration of pluvial flood risk into risk management plans, so far little is known about the adverse consequences of pluvial flooding due to a lack of both detailed data sets and studies on pluvial flood impacts. As a consequence, methods for reliably estimating pluvial flood losses, needed for pluvial flood risk assessment, are still missing.
Therefore, this thesis investigates how pluvial flood losses to private households can be reliably estimated, based on an improved understanding of the drivers of pluvial flood loss. For this purpose, detailed data from pluvial flood-affected households was collected through structured telephone- and web-surveys following pluvial flood events in Germany and the Netherlands.
Pluvial flood losses to households are the result of complex interactions between impact characteristics such as the water depth and a household’s resistance as determined by its risk awareness, preparedness, emergency response, building properties and other influencing factors. Both exploratory analysis and machine-learning approaches were used to analyze differences in resistance and impacts between households and their effects on the resulting losses. The comparison of case studies showed that the awareness around pluvial flooding among private households is quite low. Low awareness not only challenges the effective dissemination of early warnings, but was also found to influence the implementation of private precautionary measures. The latter were predominately implemented by households with previous experience of pluvial flooding. Even cases where previous flood events affected a different part of the same city did not lead to an increase in preparedness of the surveyed households, highlighting the need to account for small-scale variability in both impact and resistance parameters when assessing pluvial flood risk.
While it was concluded that the combination of low awareness, ineffective early warning and the fact that only a minority of buildings were adapted to pluvial flooding impaired the coping capacities of private households, the often low water levels still enabled households to mitigate or even prevent losses through a timely and effective emergency response.
These findings were confirmed by the detection of loss-influencing variables, showing that cases in which households were able to prevent any loss to the building structure are predominately explained by resistance variables such as the household’s risk awareness, while the degree of loss is mainly explained by impact variables.
Based on the important loss-influencing variables detected, different flood loss models were developed. Similar to flood loss models for river floods, the empirical data from the preceding data collection was used to train flood loss models describing the relationship between impact and resistance parameters and the resulting loss to building structures. Different approaches were adapted from river flood loss models using both models with the water depth as only predictor for building structure loss and models incorporating additional variables from the preceding variable detection routine.
The high predictive errors of all compared models showed that point predictions are not suitable for estimating losses on the building level, as they severely impair the reliability of the estimates. For that reason, a new probabilistic framework based on Bayesian inference was introduced that is able to provide predictive distributions instead of single loss estimates. These distributions not only give a range of probable losses, they also provide information on how likely a specific loss value is, representing the uncertainty in the loss estimate.
Using probabilistic loss models, it was found that the certainty and reliability of a loss estimate on the building level is not only determined by the use of additional predictors as shown in previous studies, but also by the choice of response distribution defining the shape of the predictive distribution. Here, a mix between a beta and a Bernoulli distribution to account for households that are able to prevent losses to their building’s structure was found to provide significantly more certain and reliable estimates than previous approaches using Gaussian or non-parametric response distributions.
The successful model transfer and post-event application to estimate building structure loss in Houston, TX, caused by pluvial flooding during Hurricane Harvey confirmed previous findings, and demonstrated the potential of the newly developed multi-variable beta model for future risk assessments. The highly detailed input data set constructed from openly available data sources containing over 304,000 affected buildings in Harris County further showed the potential of data-driven, building-level loss models for pluvial flood risk assessment.
In conclusion, pluvial flood losses to private households are the result of complex interactions between impact and resistance variables, which should be represented in loss models. The local occurrence of pluvial floods requires loss estimates on high spatial resolutions, i.e. on the building level, where losses are variable and uncertainties are high.
Therefore, probabilistic loss estimates describing the uncertainty of the estimate should be used instead of point predictions. While the performance of probabilistic models on the building level are mainly driven by the choice of response distribution, multi-variable models are recommended for two reasons:
First, additional resistance variables improve the detection of cases in which households were able to prevent structural losses.
Second, the added variability of additional predictors provides a better representation of the uncertainties when loss estimates from multiple buildings are aggregated.
This leads to the conclusion that data-driven probabilistic loss models on the building level allow for a reliable loss estimation at an unprecedented level of detail, with a consistent quantification of uncertainties on all aggregation levels. This makes the presented approach suitable for a wide range of applications, from decision support in spatial planning to impact- based early warning systems.
Natural extreme events are an integral part of nature on planet earth. Usually these events are only considered hazardous to humans, in case they are exposed. In this case, however, natural hazards can have devastating impacts on human societies. Especially hydro-meteorological hazards have a high damage potential in form of e.g. riverine and pluvial floods, winter storms, hurricanes and tornadoes, which can occur all over the globe. Along with an increasingly warm climate also an increase in extreme weather which potentially triggers natural hazards can be expected. Yet, not only changing natural systems, but also changing societal systems contribute to an increasing risk associated with these hazards. These can comprise increasing exposure and possibly also increasing vulnerability to the impacts of natural events. Thus, appropriate risk management is required to adapt all parts of society to existing and upcoming risks at various spatial scales. One essential part of risk management is the risk assessment including the estimation of the economic impacts. However, reliable methods for the estimation of economic impacts due to hydro-meteorological hazards are still missing. Therefore, this thesis deals with the question of how the reliability of hazard damage estimates can be improved, represented and propagated across all spatial scales. This question is investigated using the specific example of economic impacts to companies as a result of riverine floods in Germany.
Flood damage models aim to describe the damage processes during a given flood event. In other words they describe the vulnerability of a specific object to a flood. The models can be based on empirical data sets collected after flood events. In this thesis tree-based models trained with survey data are used for the estimation of direct economic flood impacts on the objects. It is found that these machine learning models, in conjunction with increasing sizes of data sets used to derive the models, outperform state-of-the-art damage models. However, despite the performance improvements induced by using multiple variables and more data points, large prediction errors remain at the object level. The occurrence of the high errors was explained by a further investigation using distributions derived from tree-based models. The investigation showed that direct economic impacts to individual objects cannot be modeled by a normal distribution. Yet, most state-of-the-art approaches assume a normal distribution and take mean values as point estimators. Subsequently, the predictions are unlikely values within the distributions resulting in high errors. At larger spatial scales more objects are considered for the damage estimation. This leads to a better fit of the damage estimates to a normal distribution. Consequently, also the performance of the point estimators get better, although large errors can still occur due to the variance of the normal distribution. It is recommended to use distributions instead of point estimates in order to represent the reliability of damage estimates.
In addition current approaches also mostly ignore the uncertainty associated with the characteristics of the hazard and the exposed objects. For a given flood event e.g. the estimation of the water level at a certain building is prone to uncertainties. Current approaches define exposed objects mostly by the use of land use data sets. These data sets often show inconsistencies, which introduce additional uncertainties. Furthermore, state-of-the-art approaches also imply problems of missing consistency when predicting the damage at different spatial scales. This is due to the use of different types of exposure data sets for model derivation and application. In order to face these issues a novel object-based method was developed in this thesis. The method enables a seamless estimation of hydro-meteorological hazard damage across spatial scales including uncertainty quantification. The application and validation of the method resulted in plausible estimations at all spatial scales without overestimating the uncertainty.
Mainly newly available data sets containing individual buildings make the application of the method possible as they allow for the identification of flood affected objects by overlaying the data sets with water masks. However, the identification of affected objects with two different water masks revealed huge differences in the number of identified objects. Thus, more effort is needed for their identification, since the number of objects affected determines the order of magnitude of the economic flood impacts to a large extent.
In general the method represents the uncertainties associated with the three components of risk namely hazard, exposure and vulnerability, in form of probability distributions. The object-based approach enables a consistent propagation of these uncertainties in space. Aside from the propagation of damage estimates and their uncertainties across spatial scales, a propagation between models estimating direct and indirect economic impacts was demonstrated. This enables the inclusion of uncertainties associated with the direct economic impacts within the estimation of the indirect economic impacts. Consequently, the modeling procedure facilitates the representation of the reliability of estimated total economic impacts. The representation of the estimates' reliability prevents reasoning based on a false certainty, which might be attributed to point estimates. Therefore, the developed approach facilitates a meaningful flood risk management and adaptation planning.
The successful post-event application and the representation of the uncertainties qualifies the method also for the use for future risk assessments. Thus, the developed method enables the representation of the assumptions made for the future risk assessments, which is crucial information for future risk management. This is an important step forward, since the representation of reliability associated with all components of risk is currently lacking in all state-of-the-art methods assessing future risk.
In conclusion, the use of object-based methods giving results in the form of distributions instead of point estimations is recommended. The improvement of the model performance by the means of multi-variable models and additional data points is possible, but small. Uncertainties associated with all components of damage estimation should be included and represented within the results. Furthermore, the findings of the thesis suggest that, at larger scales, the influence of the uncertainty associated with the vulnerability is smaller than those associated with the hazard and exposure. This leads to the conclusion that for an increased reliability of flood damage estimations and risk assessments, the improvement and active inclusion of hazard and exposure, including their uncertainties, is needed in addition to the improvements of the models describing the vulnerability of the objects.
Uncertainty is an essential part of atmospheric processes and thus inherent to weather forecasts. Nevertheless, weather forecasts and warnings are still predominately issued as deterministic (yes or no) forecasts, although research suggests that providing weather forecast users with additional information about the forecast uncertainty can enhance the preparation of mitigation measures. Communicating forecast uncertainty would allow for a provision of information on possible future events at an earlier time. The desired benefit is to enable the users to start with preparatory protective action at an earlier stage of time based on the their own risk assessment and decision threshold. But not all users have the same threshold for taking action. In the course of the project WEXICOM (‘Wetterwarnungen: Von der Extremereignis-Information zu Kommunikation und Handlung’) funded by the Deutscher Wetterdienst (DWD), three studies were conducted between the years 2012 and 2016 to reveal how weather forecasts and warnings are reflected in weather-related decision-making. The studies asked which factors influence the perception of forecasts and the decision to take protective action and how forecast users make sense of probabilistic information and the additional lead time. In a first exploratory study conducted in 2012, members of emergency services in Germany were asked questions about how weather warnings are communicated to professional endusers in the emergency community and how the warnings are converted into mitigation measures. A large number of open questions were selected to identify new topics of interest. The questions covered topics like users’ confidence in forecasts, their understanding of probabilistic information as well as their lead time and decision thresholds to start with preparatory mitigation measures. Results show that emergency service personnel generally have a good sense of uncertainty inherent in weather forecasts. Although no single probability threshold could be identified for organisations to start with preparatory mitigation measures, it became clear that emergency services tend to avoid forecasts based on low probabilities as a basis for their decisions. Based on this findings, a second study conducted with residents of Berlin in 2014 further investigated the question of decision thresholds. The survey questions related to the topics of the perception of and prior experience with severe weather, trustworthiness of forecasters and confidence in weather forecasts, and socio-demographic and social-economic characteristics. Within the questionnaire a scenario was created to determine individual decision thresholds and see whether subgroups of the sample lead to different thresholds. The results show that people’s willingness to act tends to be higher and decision thresholds tend to be lower if the expected weather event is more severe or the property at risk is of higher value. Several influencing factors of risk perception have significant effects such as education, housing status and ability to act, whereas socio-demographic determinants alone are often not sufficient to fully grasp risk perception and protection behaviour. Parallel to the quantitative studies, an interview study was conducted with 27 members of German civil protection between 2012 and 2016. The results show that the latest developments in (numerical) weather forecasting do not necessarily fit the current practice of German emergency services. These practices are mostly carried out on alarms and ground truth in a reactive manner rather than on anticipation based on prognosis or forecasts. As the potential consequences rather than the event characteristics determine protective action, the findings support the call and need for impact-based warnings. Forecasters will rely on impact data and need to learn the users’ understanding of impact. Therefore, it is recommended to enhance weather communication not only by improving computer models and observation tools, but also by focusing on the aspects of communication and collaboration. Using information about uncertainty demands awareness about and acceptance of the limits of knowledge, hence, the capabilities of the forecaster to anticipate future developments of the atmosphere and the capabilities of the user to make sense of this information.
Species respond to environmental change by dynamically adjusting their geographical ranges. Robust predictions of these changes are prerequisites to inform dynamic and sustainable conservation strategies. Correlative species distribution models (SDMs) relate species’ occurrence records to prevailing environmental factors to describe the environmental niche. They have been widely applied in global change context as they have comparably low data requirements and allow for rapid assessments of potential future species’ distributions. However, due to their static nature, transient responses to environmental change are essentially ignored in SDMs. Furthermore, neither dispersal nor demographic processes and biotic interactions are explicitly incorporated. Therefore, it has often been suggested to link statistical and mechanistic modelling approaches in order to make more realistic predictions of species’ distributions for scenarios of environmental change. In this thesis, I present two different ways of such linkage. (i) Mechanistic modelling can act as virtual playground for testing statistical models and allows extensive exploration of specific questions. I promote this ‘virtual ecologist’ approach as a powerful evaluation framework for testing sampling protocols, analyses and modelling tools. Also, I employ such an approach to systematically assess the effects of transient dynamics and ecological properties and processes on the prediction accuracy of SDMs for climate change projections. That way, relevant mechanisms are identified that shape the species’ response to altered environmental conditions and which should hence be considered when trying to project species’ distribution through time. (ii) I supplement SDM projections of potential future habitat for black grouse in Switzerland with an individual-based population model. By explicitly considering complex interactions between habitat availability and demographic processes, this allows for a more direct assessment of expected population response to environmental change and associated extinction risks. However, predictions were highly variable across simulations emphasising the need for principal evaluation tools like sensitivity analysis to assess uncertainty and robustness in dynamic range predictions. Furthermore, I identify data coverage of the environmental niche as a likely cause for contrasted range predictions between SDM algorithms. SDMs may fail to make reliable predictions for truncated and edge niches, meaning that portions of the niche are not represented in the data or niche edges coincide with data limits. Overall, my thesis contributes to an improved understanding of uncertainty factors in predictions of range dynamics and presents ways how to deal with these. Finally I provide preliminary guidelines for predictive modelling of dynamic species’ response to environmental change, identify key challenges for future research and discuss emerging developments.
The length of the vegetation period (VP) plays a central role for the interannual variation of carbon fixation of terrestrial ecosystems. Observational data analysis has indicated that the length of the VP has increased in the last decades in the northern latitudes mainly due to an advancement of bud burst (BB). This phenomenon has been widely discussed in the context of Global Warming because phenology is correlated to temperatures. Analyzing the patterns of spring phenology over the last century in Southern Germany provided two main findings: - The strong advancement of spring phases especially in the decade before 1999 is not a singular event in the course of the 20th century. Similar trends were also observed in earlier decades. Distinct periods of varying trend behavior for important spring phases could be distinguished. - Marked differences in trend behavior between the early and late spring phases were detected. Early spring phases changed as regards the magnitude of their negative trends from strong negative trends between 1931 and 1948 to moderate negative trends between 1948 and 1984 and back to strong negative trends between 1984 and 1999. Late spring phases showed a different behavior. Negative trends between 1931 and 1948 are followed by marked positive trends between 1948 and 1984 and then strong negative trends between 1984 and 1999. This marked difference in trend development between early and late spring phases was also found all over Germany for the two periods 1951 to 1984 and 1984 to 1999. The dominating influence of temperature on spring phenology and its modifying effect on autumn phenology was confirmed in this thesis. However, - temperature functions determining spring phenology were not significantly correlated with a global annual CO2 signal which was taken as a proxy for a Global Warming pattern. - an index for large scale regional circulation patterns (NAO index) could only to a small part explain the observed phenological variability in spring. The observed different trend behavior of early and late spring phases is explained by the differing behavior of mean March and April temperatures. Mean March temperatures have increased on average over the 20th century accompanied by an increasing variation in the last 50 years. April temperatures, however, decreased between the end of the 1940s and the mid-1980s, followed by a marked warming after the mid-1980s. It can be concluded that the advancement of spring phenology in recent decades are part of multi-decadal fluctuations over the 20th century that vary with the species and the relevant seasonal temperatures. Because of these fluctuations a correlation with an observed Global Warming signal could not be found. On average all investigated spring phases advanced between 5 and 20 days between 1951 and 1999 for all Natural Regions in Germany. A marked difference be! tween late and early spring phases is due to the above mentioned differing behavior before and after the mid-1980s. Leaf coloring (LC) was delayed between 1951 and 1984 for all tree species. However, after 1984 LC was advanced. Length of the VP increased between 1951 and 1999 for all considered tree species by an average of ten days throughout Germany. It is predominately the change in spring phases which contributes to a change in the potentially absorbed radiation. Additionally, it is the late spring species that are relatively more favored by an advanced BB because they can additionally exploit longer days and higher temperatures per day advancement. To assess the relative change in potentially absorbed radiation among species, changes in both spring and autumn phenology have to be considered as well as where these changes are located in the year. For the detection of the marked difference between early and late spring phenology a new time series construction method was developed. This method allowed the derivation of reliable time series that spanned over 100 years and the construction of locally combined time series increasing the available data for model development. Apart from analyzed protocolling errors, microclimatic site influences, genetic variation and the observers were identified as sources of uncertainty of phenological observational data. It was concluded that 99% of all phenological observations at a certain site will vary within approximately 24 days around the parametric mean. This supports to the proposed 30-day rule to detect outliers. New phenology models that predict local BB from daily temperature time series were developed. These models were based on simple interactions between inhibitory and promotory agents that are assumed to control the developmental status of a plant. Apart from the fact that, in general, the new models fitted and predicted the observations better than classical models, the main modeling results were: - The bias of the classical models, i.e. overestimation of early observations and underestimation of late observations, could be reduced but not completely removed. - The different favored model structures for each species indicated that for the late spring phases photoperiod played a more dominant role than for early spring phases. - Chilling only plays a subordinate role for spring BB compared to temperatures directly preceding BB.