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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.
This dissertation consists of five self-contained essays, addressing different aspects of career choices, especially the choice of entrepreneurship, under risk and ambiguity. In Chapter 2, the first essay develops an occupational choice model with boundedly rational agents, who lack information, receive noisy feedback, and are restricted in their decisions by their personality, to analyze and explain puzzling empirical evidence on entrepreneurial decision processes. In the second essay, in Chapter 3, I contribute to the literature on entrepreneurial choice by constructing a general career choice model on the basis of the assumption that outcomes are partially ambiguous. The third essay, in Chapter 4, theoretically and empirically analyzes the impact of media on career choices, where information on entrepreneurship provided by the media is treated as an informational shock affecting prior beliefs. The fourth essay, presented in Chapter 5, contains an empirical analysis of the effects of cyclical macro variables (GDP and unemployment) on innovative start-ups in Germany. In the fifth, and last, essay in Chapter 6, we examine whether information on personality is useful for advice, using the example of career advice.
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.
The relationship between climate and forest productivity is an intensively studied subject in forest science. This Thesis is embedded within the general framework of future forest growth under climate change and its implications for the ongoing forest conversion. My objective is to investigate the future forest productivity at different spatial scales (from a single specific forest stand to aggregated information across Germany) with focus on oak-pine forests in the federal state of Brandenburg. The overarching question is: how are the oak-pine forests affected by climate change described by a variety of climate scenarios. I answer this question by using a model based analysis of tree growth processes and responses to different climate scenarios with emphasis on drought events. In addition, a method is developed which considers climate change uncertainty of forest management planning.
As a first 'screening' of climate change impacts on forest productivity, I calculated the change in net primary production on the base of a large set of climate scenarios for different tree species and the total area of Germany. Temperature increases up to 3 K lead to positive effects on the net primary production of all selected tree species. But, in water-limited regions this positive net primary production trend is dependent on the length of drought periods which results in a larger uncertainty regarding future forest productivity. One of the regions with the highest uncertainty of net primary production development is the federal state of Brandenburg.
To enhance the understanding and ability of model based analysis of tree growth sensitivity to drought stress two water uptake approaches in pure pine and mixed oak-pine stands are contrasted. The first water uptake approach consists of an empirical function for root water uptake. The second approach is more mechanistic and calculates the differences of soil water potential along a soil-plant-atmosphere continuum. I assumed the total root resistance to vary at low, medium and high total root resistance levels. For validation purposes three data sets on different tree growth relevant time scales are used. Results show that, except the mechanistic water uptake approach with high total root resistance, all transpiration outputs exceeded observed values. On the other hand high transpiration led to a better match of observed soil water content. The strongest correlation between simulated and observed annual tree ring width occurred with the mechanistic water uptake approach and high total root resistance. The findings highlight the importance of severe drought as a main reason for small diameter increment, best supported by the mechanistic water uptake approach with high root resistance. However, if all aspects of the data sets are considered no approach can be judged superior to the other. I conclude that the uncertainty of future productivity of water-limited forest ecosystems under changing environmental conditions is linked to simulated root water uptake.
Finally my study aimed at the impacts of climate change combined with management scenarios on an oak-pine forest to evaluate growth, biomass and the amount of harvested timber. The pine and the oak trees are 104 and 9 years old respectively. Three different management scenarios with different thinning intensities and different climate scenarios are used to simulate the performance of management strategies which explicitly account for the risks associated with achieving three predefined objectives (maximum carbon storage, maximum harvested timber, intermediate). I found out that in most cases there is no general management strategy which fits best to different objectives. The analysis of variance in the growth related model outputs showed an increase of climate uncertainty with increasing climate warming. Interestingly, the increase of climate-induced uncertainty is much higher from 2 to 3 K than from 0 to 2 K.
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.
Soils contain a large amount of carbon (C) that is a critical regulator of the global C budget. Already small changes in the processes governing soil C cycling have the potential to release considerable amounts of CO2, a greenhouse gas (GHG), adding additional radiative forcing to the atmosphere and hence to changing climate. Increased temperatures will probably create a feedback, causing soils to release more GHGs. Furthermore changes in soil C balance impact soil fertility and soil quality, potentially degrading soils and reducing soils function as important resource. Consequently the assessment of soil C dynamics under present, recent past and future environmental conditions is not only of scientific interest and requires an integrated consideration of main factors and processes governing soil C dynamics. To perform this assessment an eco-hydrological modelling tool was used and extended by a process-based description of coupled soil carbon and nitrogen turnover. The extended model aims at delivering sound information on soil C storage changes beside changes in water quality, quantity and vegetation growth under global change impacts in meso- to macro-scale river basins, exemplary demonstrated for a Central European river basin (the Elbe). As a result this study: ▪ Provides information on joint effects of land-use (land cover and land management) and climate changes on croplands soil C balance in the Elbe river basin (Central Europe) presently and in the future. ▪ Evaluates which processes, and at what level of process detail, have to be considered to perform an integrated simulation of soil C dynamics at the meso- to macro-scale and demonstrates the model’s capability to simulate these processes compared to observations. ▪ Proposes a process description relating soil C pools and turnover properties to readily measurable quantities. This reduces the number of model parameters, enhances the comparability of model results to observations, and delivers same performance simulating long-term soil C dynamics as other models. ▪ Presents an extensive assessment of the parameter and input data uncertainty and their importance both temporally and spatially on modelling soil C dynamics. For the basin scale assessments it is estimated that croplands in the Elbe basin currently act as a net source of carbon (net annual C flux of 11 g C m-2 yr-1, 1.57 106 tons CO2 yr-1 entire croplands on average). Although this highly depends on the amount of harvest by-products remaining on the field. Future anticipated climate change and observed climate change in the basin already accelerates soil C loss and increases source strengths (additional 3.2 g C m-2 yr-1, 0.48 106 tons CO2 yr-1 entire croplands). But anticipated changes of agro-economic conditions, translating to altered crop share distributions, display stronger effects on soil C storage than climate change. Depending on future use of land expected to fall out of agricultural use in the future (~ 30 % of croplands area as “surplus” land), the basin either considerably looses soil C and the net annual C flux to the atmosphere increases (surplus used as black fallow) or the basin converts to a net sink of C (sequestering 0.44 106 tons CO2 yr-1 under extensified use as ley-arable) or reacts with decrease in source strength when using bioenergy crops. Bioenergy crops additionally offer a considerable potential for fossil fuel substitution (~37 PJ, 1015 J per year), whereas the basin wide use of harvest by-products for energy generation has to be seen critically although offering an annual energy potential of approximately 125 PJ. Harvest by-products play a central role in soil C reproduction and a percentage between 50 and 80 % should remain on the fields in order to maintain soil quality and fertility. The established modelling tool allows quantifying climate, land use and major land management impacts on soil C balance. New is that the SOM turnover description is embedded in an eco-hydrological river basin model, allowing an integrated consideration of water quantity, water quality, vegetation growth, agricultural productivity and soil carbon changes under different environmental conditions. The methodology and assessment presented here demonstrates the potential for integrated assessment of soil C dynamics alongside with other ecosystem services under global change impacts and provides information on the potentials of soils for climate change mitigation (soil C sequestration) and on their soil fertility status.
Uncertainties are pervasive in the Earth System modelling. This is not just due to a lack of knowledge about physical processes but has its seeds in intrinsic, i.e. inevitable and irreducible, uncertainties concerning the process of modelling as well. Therefore, it is indispensable to quantify uncertainty in order to determine, which are robust results under this inherent uncertainty. The central goal of this thesis is to explore how uncertainties map on the properties of interest such as phase space topology and qualitative dynamics of the system. We will address several types of uncertainty and apply methods of dynamical systems theory on a trendsetting field of climate research, i.e. the Indian monsoon. For the systematic analysis concerning the different facets of uncertainty, a box model of the Indian monsoon is investigated, which shows a saddle node bifurcation against those parameters that influence the heat budget of the system and that goes along with a regime shift from a wet to a dry summer monsoon. As some of these parameters are crucially influenced by anthropogenic perturbations, the question is whether the occurrence of this bifurcation is robust against uncertainties in parameters and in the number of considered processes and secondly, whether the bifurcation can be reached under climate change. Results indicate, for example, the robustness of the bifurcation point against all considered parameter uncertainties. The possibility of reaching the critical point under climate change seems rather improbable. A novel method is applied for the analysis of the occurrence and the position of the bifurcation point in the monsoon model against parameter uncertainties. This method combines two standard approaches: a bifurcation analysis with multi-parameter ensemble simulations. As a model-independent and therefore universal procedure, this method allows investigating the uncertainty referring to a bifurcation in a high dimensional parameter space in many other models. With the monsoon model the uncertainty about the external influence of El Niño / Southern Oscillation (ENSO) is determined. There is evidence that ENSO influences the variability of the Indian monsoon, but the underlying physical mechanism is discussed controversially. As a contribution to the debate three different hypotheses are tested of how ENSO and the Indian summer monsoon are linked. In this thesis the coupling through the trade winds is identified as key in linking these two key climate constituents. On the basis of this physical mechanism the observed monsoon rainfall data can be reproduced to a great extent. Moreover, this mechanism can be identified in two general circulation models (GCMs) for the present day situation and for future projections under climate change. Furthermore, uncertainties in the process of coupling models are investigated, where the focus is on a comparison of forced dynamics as opposed to fully coupled dynamics. The former describes a particular type of coupling, where the dynamics from one sub-module is substituted by data. Intrinsic uncertainties and constraints are identified that prevent the consistency of a forced model with its fully coupled counterpart. Qualitative discrepancies between the two modelling approaches are highlighted, which lead to an overestimation of predictability and produce artificial predictability in the forced system. The results suggest that bistability and intermittent predictability, when found in a forced model set-up, should always be cross-validated with alternative coupling designs before being taken for granted. All in this, this thesis contributes to the fundamental issue of dealing with uncertainties the climate modelling community is confronted with. Although some uncertainties allow for including them in the interpretation of the model results, intrinsic uncertainties could be identified, which are inevitable within a certain modelling paradigm and are provoked by the specific modelling approach.
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.