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Flooding is a vast problem in many parts of the world, including Europe. It occurs mainly due to extreme weather conditions (e.g. heavy rainfall and snowmelt) and the consequences of flood events can be devastating. Flood risk is mainly defined as a combination of the probability of an event and its potential adverse impacts. Therefore, it covers three major dynamic components: hazard (physical characteristics of a flood event), exposure (people and their physical environment that being exposed to flood), and vulnerability (the elements at risk). Floods are natural phenomena and cannot be fully prevented. However, their risk can be managed and mitigated. For a sound flood risk management and mitigation, a proper risk assessment is needed. First of all, this is attained by a clear understanding of the flood risk dynamics. For instance, human activity may contribute to an increase in flood risk. Anthropogenic climate change causes higher intensity of rainfall and sea level rise and therefore an increase in scale and frequency of the flood events. On the other hand, inappropriate management of risk and structural protection measures may not be very effective for risk reduction. Additionally, due to the growth of number of assets and people within the flood-prone areas, risk increases. To address these issues, the first objective of this thesis is to perform a sensitivity analysis to understand the impacts of changes in each flood risk component on overall risk and further their mutual interactions. A multitude of changes along the risk chain are simulated by regional flood model (RFM) where all processes from atmosphere through catchment and river system to damage mechanisms are taken into consideration. The impacts of changes in risk components are explored by plausible change scenarios for the mesoscale Mulde catchment (sub-basin of the Elbe) in Germany.
A proper risk assessment is ensured by the reasonable representation of the real-world flood event. Traditionally, flood risk is assessed by assuming homogeneous return periods of flood peaks throughout the considered catchment. However, in reality, flood events are spatially heterogeneous and therefore traditional assumption misestimates flood risk especially for large regions. In this thesis, two different studies investigate the importance of spatial dependence in large scale flood risk assessment for different spatial scales. In the first one, the “real” spatial dependence of return period of flood damages is represented by continuous risk modelling approach where spatially coherent patterns of hydrological and meteorological controls (i.e. soil moisture and weather patterns) are included. Further the risk estimations under this modelled dependence assumption are compared with two other assumptions on the spatial dependence of return periods of flood damages: complete dependence (homogeneous return periods) and independence (randomly generated heterogeneous return periods) for the Elbe catchment in Germany. The second study represents the “real” spatial dependence by multivariate dependence models. Similar to the first study, the three different assumptions on the spatial dependence of return periods of flood damages are compared, but at national (United Kingdom and Germany) and continental (Europe) scales. Furthermore, the impacts of the different models, tail dependence, and the structural flood protection level on the flood risk under different spatial dependence assumptions are investigated.
The outcomes of the sensitivity analysis framework suggest that flood risk can vary dramatically as a result of possible change scenarios. The risk components that have not received much attention (e.g. changes in dike systems and in vulnerability) may mask the influence of climate change that is often investigated component.
The results of the spatial dependence research in this thesis further show that the damage under the false assumption of complete dependence is 100 % larger than the damage under the modelled dependence assumption, for the events with return periods greater than approximately 200 years in the Elbe catchment. The complete dependence assumption overestimates the 200-year flood damage, a benchmark indicator for the insurance industry, by 139 %, 188 % and 246 % for the UK, Germany and Europe, respectively. The misestimation of risk under different assumptions can vary from upstream to downstream of the catchment. Besides, tail dependence in the model and flood protection level in the catchments can affect the risk estimation and the differences between different spatial dependence assumptions.
In conclusion, the broader consideration of the risk components, which possibly affect the flood risk in a comprehensive way, and the consideration of the spatial dependence of flood return periods are strongly recommended for a better understanding of flood risk and consequently for a sound flood risk management and mitigation.
Today, the Mekong Delta in the southern of Vietnam is home for 18 million people. The delta also accounts for more than half of the country’s food production and 80% of the exported rice. Due to the low elevation, it is highly susceptible to the risk of fluvial and coastal flooding. Although extreme floods often result in excessive damages and economic losses, the annual flood pulse from the Mekong is vital to sustain agricultural cultivation and livelihoods of million delta inhabitants.
Delta-wise risk management and adaptation strategies are required to mitigate the adverse impacts from extreme events while capitalising benefits from floods. However, a proper flood risk management has not been implemented in the VMD, because the quantification of flood damage is often overlooked and the risks are thus not quantified. So far, flood management has been exclusively focused on engineering measures, i.e. high- and low- dyke systems, aiming at flood-free or partial inundation control without any consideration of the actual risks or a cost-benefit analysis. Therefore, an analysis of future delta flood dynamics driven these stressors is valuable to facilitate the transition from sole hazard control towards a risk management approach, which is more cost-effective and also robust against future changes in risk.
Built on these research gaps, this thesis investigates the current state and future projections of flood hazard, damage and risk to rice cultivation, the most important economic activity in the VMD. The study quantifies the changes in risk and hazard brought by the development of delta-based flood control measures in the last decades, and analyses the expected changes in risk driven by the changing climate, rising sea-level and deltaic land subsidence, and finally the development of hydropower projects in the Mekong Basin. For this purpose, flood trend analyses and comprehensive hydraulic modelling were performed, together with the development of a concept to quantify flood damage and risk to rice plantation.
The analysis of observed flood levels revealed strong and robust increasing trends of peak and duration downstream of the high-dyke areas with a step change in 2000/2001, i.e. after the disastrous flood which initiated the high-dyke development. These changes were in contrast to the negative trends detected upstream, suggested that high-dyke development has shifted flood hazard downstream. Findings of the trend’s analysis were later confirmed by hydraulic simulations of the two recent extreme floods in 2000 and 2011, where the hydrological boundaries and dyke system settings were interchanged.
However, the high-dyke system was not the only and often not the main cause for a shift of flood hazard, as a comparative analysis of these two extreme floods proved. The high-dyke development was responsible for 20–90% of the observed changes in flood level between 2000 and 2011, with large spatial variances. The particular flood hydrograph of the two events had the highest contribution in the northern part of the delta, while the tidal level had 2–3 times higher influence than the high-dyke in the lower-central and coastal areas downstream of high-dyke areas. The impact of the high-dyke development was highest in the areas closely downstream of the high-dyke area just south of the Cambodia-Vietnam border. The hydraulic simulations also validated that the concurrence of the flood peak with spring tides, i.e. high sea level along the coast, amplified the flood level and inundation in the central and coastal regions substantially.
The risk assessment quantified the economic losses of rice cultivation to USD 25.0 and 115 million (0.02–0.1% of the total GDP of Vietnam in 2011) corresponding to the 10-year and the 100-year floods, with an expected annual damage of about USD 4.5 million. A particular finding is that the flood damage was highly sensitive to flood timing. Here, a 10-year event with an early peak, i.e. late August-September, could cause as much damage as a 100-year event that peaked in October. This finding underlines the importance of a reliable early flood warning, which could substantially reduce the damage to rice crops and thus the risk.
The developed risk assessment concept was furthermore applied to investigate two high-dyke development alternatives, which are currently under discussion among the administrative bodies in Vietnam, but also in the public. The first option favouring the utilization of the current high-dyke compartments as flood retention areas instead for rice cropping during the flood season could reduce flood hazard and expected losses by 5–40%, depending on the region of the delta. On the contrary, the second option promoting the further extension of the areas protected by high-dyke to facilitate third rice crop planting on a larger area, tripled the current expected annual flood damage. This finding challenges the expected economic benefit of triple rice cultivation, in addition to the already known reducing of nutrient supply by floodplain sedimentation and thus higher costs for fertilizers.
The economic benefits of the high-dyke and triple rice cropping system is further challenged by the changes in the flood dynamics to be expected in future. For the middle of the 21st century (2036-2065) the effective sea-level rise an increase of the inundation extent by 20–27% was projected. This corresponds to an increase of flood damage to rice crops in dry, normal and wet year by USD 26.0, 40.0 and 82.0 million in dry, normal and wet year compared to the baseline period 1971-2000.
Hydraulic simulations indicated that the planned massive development of hydropower dams in the Mekong Basin could potentially compensate the increase in flood hazard and agriculture losses stemming from climate change. However, the benefits of dams as mitigation of flood losses are highly uncertain, because a) the actual development of the dams is highly disputed, b) the operation of the dams is primarily targeted at power generation, not flood control, and c) this would require international agreements and cooperation, which is difficult to achieve in South-East Asia. The theoretical flood mitigation benefit is additionally challenged by a number of negative impacts of the dam development, e.g. disruption of floodplain inundation in normal, non-extreme flood years. Adding to the certain reduction of sediment and nutrient load to the floodplains, hydropower dams will drastically impair rice and agriculture production, the basis livelihoods of million delta inhabitants.
In conclusion, the VMD is expected to face increasing threats of tidal induced floods in the coming decades. Protection of the entire delta coastline solely with “hard” engineering flood protection structures is neither technically nor economically feasible, adaptation and mitigation actions are urgently required. Better control and reduction of groundwater abstraction is thus strongly recommended as an immediate and high priority action to reduce the land subsidence and thus tidal flooding and salinity intrusion in the delta. Hydropower development in the Mekong basin might offer some theoretical flood protection for the Mekong delta, but due to uncertainties in the operation of the dams and a number of negative effects, the dam development cannot be recommended as a strategy for flood management. For the Vietnamese authorities, it is advisable to properly maintain the existing flood protection structures and to develop flexible risk-based flood management plans. In this context the study showed that the high-dyke compartments can be utilized for emergency flood management in extreme events. For this purpose, a reliable flood forecast is essential, and the action plan should be materialised in official documents and legislation to assure commitment and consistency in the implementation and operation.
Technological progress allows for producing ever more complex predictive models on the basis of increasingly big datasets. For risk management of natural hazards, a multitude of models is needed as basis for decision-making, e.g. in the evaluation of observational data, for the prediction of hazard scenarios, or for statistical estimates of expected damage. The question arises, how modern modelling approaches like machine learning or data-mining can be meaningfully deployed in this thematic field. In addition, with respect to data availability and accessibility, the trend is towards open data. Topic of this thesis is therefore to investigate the possibilities and limitations of machine learning and open geospatial data in the field of flood risk modelling in the broad sense. As this overarching topic is broad in scope, individual relevant aspects are identified and inspected in detail.
A prominent data source in the flood context is satellite-based mapping of inundated areas, for example made openly available by the Copernicus service of the European Union. Great expectations are directed towards these products in scientific literature, both for acute support of relief forces during emergency response action, and for modelling via hydrodynamic models or for damage estimation. Therefore, a focus of this work was set on evaluating these flood masks. From the observation that the quality of these products is insufficient in forested and built-up areas, a procedure for subsequent improvement via machine learning was developed. This procedure is based on a classification algorithm that only requires training data from a particular class to be predicted, in this specific case data of flooded areas, but not of the negative class (dry areas). The application for hurricane Harvey in Houston shows the high potential of this method, which depends on the quality of the initial flood mask.
Next, it is investigated how much the predicted statistical risk from a process-based model chain is dependent on implemented physical process details. Thereby it is demonstrated what a risk study based on established models can deliver. Even for fluvial flooding, such model chains are already quite complex, though, and are hardly available for compound or cascading events comprising torrential rainfall, flash floods, and other processes. In the fourth chapter of this thesis it is therefore tested whether machine learning based on comprehensive damage data can offer a more direct path towards damage modelling, that avoids explicit conception of such a model chain. For that purpose, a state-collected dataset of damaged buildings from the severe El Niño event 2017 in Peru is used. In this context, the possibilities of data-mining for extracting process knowledge are explored as well. It can be shown that various openly available geodata sources contain useful information for flood hazard and damage modelling for complex events, e.g. satellite-based rainfall measurements, topographic and hydrographic information, mapped settlement areas, as well as indicators from spectral data. Further, insights on damaging processes are discovered, which mainly are in line with prior expectations. The maximum intensity of rainfall, for example, acts stronger in cities and steep canyons, while the sum of rain was found more informative in low-lying river catchments and forested areas. Rural areas of Peru exhibited higher vulnerability in the presented study compared to urban areas. However, the general limitations of the methods and the dependence on specific datasets and algorithms also become obvious.
In the overarching discussion, the different methods – process-based modelling, predictive machine learning, and data-mining – are evaluated with respect to the overall research questions. In the case of hazard observation it seems that a focus on novel algorithms makes sense for future research. In the subtopic of hazard modelling, especially for river floods, the improvement of physical models and the integration of process-based and statistical procedures is suggested. For damage modelling the large and representative datasets necessary for the broad application of machine learning are still lacking. Therefore, the improvement of the data basis in the field of damage is currently regarded as more important than the selection of algorithms.
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.