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.

Sea surface temperature (SST) patterns can – as surface climate forcing – affect weather and climate at large distances. One example is El Niño-Southern Oscillation (ENSO) that causes climate anomalies around the globe via teleconnections. Although several studies identified and characterized these teleconnections, our understanding of climate processes remains incomplete, since interactions and feedbacks are typically exhibited at unique or multiple temporal and spatial scales. This study characterizes the interactions between the cells of a global SST data set at different temporal and spatial scales using climate networks. These networks are constructed using wavelet multi-scale correlation that investigate the correlation between the SST time series at a range of scales allowing instantaneously deeper insights into the correlation patterns compared to traditional methods like empirical orthogonal functions or classical correlation analysis. This allows us to identify and visualise regions of – at a certain timescale – similarly evolving SSTs and distinguish them from those with long-range teleconnections to other ocean regions. Our findings re-confirm accepted knowledge about known highly linked SST patterns like ENSO and the Pacific Decadal Oscillation, but also suggest new insights into the characteristics and origins of long-range teleconnections like the connection between ENSO and Indian Ocean Dipole.

Plant-derived Transcription Factors for Orthologous Regulation of Gene Expression in the Yeast Saccharomyces cerevisiae Control of gene expression by transcription factors (TFs) is central in many synthetic biology projects where tailored expression of one or multiple genes is often needed. As TFs from evolutionary distant organisms are unlikely to affect gene expression in a host of choice, they represent excellent candidates for establishing orthogonal control systems. To establish orthogonal regulators for use in yeast (Saccharomyces cerevisiae), we chose TFs from the plant Arabidopsis thaliana. We established a library of 106 different combinations of chromosomally integrated TFs, activation domains (yeast GAL4 AD, herpes simplex virus VP64, and plant EDLL) and synthetic promoters harbouring cognate cis-regulatory motifs driving a yEGFP reporter. Transcriptional output of the different driver / reporter combinations varied over a wide spectrum, with EDLL being a considerably stronger transcription activation domain in yeast, than the GAL4 activation domain, in particular when fused to Arabidopsis NAC TFs. Notably, the strength of several NAC - EDLL fusions exceeded that of the strong yeast TDH3 promoter by 6- to 10-fold. We furthermore show that plant TFs can be used to build regulatory systems encoded by centromeric or episomal plasmids. Our library of TF – DNA-binding site combinations offers an excellent tool for diverse synthetic biology applications in yeast. COMPASS: Rapid combinatorial optimization of biochemical pathways based on artificial transcription factors We established a high-throughput cloning method, called COMPASS for COMbinatorial Pathway ASSembly, for the balanced expression of multiple genes in Saccharomyces cerevisiae. COMPASS employs orthogonal, plant-derived artificial transcription factors (ATFs) for controlling the expression of pathway genes, and homologous recombination-based cloning for the generation of thousands of individual DNA constructs in parallel. The method relies on a positive selection of correctly assembled pathway variants from both, in vivo and in vitro cloning procedures. To decrease the turnaround time in genomic engineering, we equipped COMPASS with multi-locus CRISPR/Cas9-mediated modification capacity. In its current realization, COMPASS allows combinatorial optimization of up to ten pathway genes, each transcriptionally controlled by nine different ATFs spanning a 10-fold difference in expression strength. The application of COMPASS was demonstrated by generating cell libraries producing beta-carotene and co-producing beta-ionone and biosensor-responsive naringenin. COMPASS will have many applications in other synthetic biology projects that require gene expression balancing. CaPRedit: Genome editing using CRISPR-Cas9 and plant-derived transcriptional regulators for the redirection of flux through the FPP branch-point in yeast. Technologies developed over the past decade have made Saccharomyces cerevisiae a promising platform for production of different natural products. We developed CRISPR/Ca9- and plant derived regulator-mediated genome editing approach (CaPRedit) to greatly accelerate strain modification and to facilitate very low to very high expression of key enzymes using inducible regulators. CaPRedit can be implemented to enhance the production of yeast endogenous or heterologous metabolites in the yeast S. cerevisiae. The CaPRedit system aims to faciltiate modification of multiple targets within a complex metabolic pathway through providing new tools for increased expression of genes encoding rate-limiting enzymes, decreased expression of essential genes, and removed expression of competing pathways. This approach is based on CRISPR/Cas9-mediated one-step double-strand breaks to integrate modules containing IPTG-inducible plant-derived artificial transcription factor and promoter pair(s) in a desired locus or loci. Here, we used CaPRedit to redirect the yeast endogenous metabolic flux toward production of farnesyl diphosphate (FPP), a central precursor of nearly all yeast isoprenoid products, by overexpression of the enzymes lead to produce FPP from glutamate. We found significantly higher beta-carotene accumulation in the CaPRedit-mediated modified strain than in the wild type (WT) strain. More specifically, CaPRedit_FPP 1.0 strain was generated, in which three genes involved in FPP synthesis, tHMG1, ERG20, and GDH2, were inducibly overexpressed under the control of strong plant-derived ATFPs. The beta–carotene accumulated in CaPRedit_FPP 1.0 strain to a level 1.3-fold higher than the previously reported optimized strain that carries the same overexpressed genes (as well as additional genetic modifications to redirect yeast endogenous metabolism toward FPP production). Furthermore, the genetic modifications implemented in CaPRedit_FPP 1.0 strain resulted in only a very small growth defect (growth rate relative to the WT is ~ -0.03).