Floods In Germany Research Articles

A dataset of floating debris accumulation at bridges after July 2021 flood in Germany and Belgium

This paper presents a dataset documenting 71 floating debris accumulations at bridges following an extreme hydrological event that hit Belgium and Germany in July 2021. Data were collected from various sources including public authorities’ documents, public online databases, post event pictures and field visits. The dataset covers bridges geometry, flood conditions and debris accumulation. In particular, it systematically details accumulations dimensions and quantifies accumulations components, which contain a significant portion of anthropogenic objects, in addition to driftwood. This dataset constitutes a unique set of invaluable data to better understand debris accumulation at bridges but also to analyze their impact on both the affected structures and flood conditions.

A downward-counterfactual analysis of flash floods in Germany

A downward-counterfactual analysis of flash floods in Germany

Remote sensing‐based mapping of structural building damage in the Ahr valley

AbstractFlood damage data are needed for various applications. Structural damage of buildings can reflect not only the economic damage but also the life‐threatening condition of a building, which provide crucial information for disaster response and recovery. Since traditional on‐site data collection shortly after a disaster is challenging, remote sensing data can be of great help, cover a wider area and be deployed earlier in time than on‐site surveys. However, this has its challenges and limitations. We elucidate on that by presenting two case studies from flash floods in Germany. First, we assessed the reliability of an existing flood damage schema, which differentiates from minor (structural) damage to complete building collapse. We compared two on‐site raters of the 2016 Braunsbach flood, reaching an excellent level of reliability. Second, we mapped structural building damage after the flood in the Ahr valley in 2021 using a textured 3D mesh and orthophotos. Here, we evaluated the remote sense‐based damage mapping done by three raters. Although the heterogeneity of ratings using remote sensing data is larger than among on‐site ratings, we consider it fit‐for‐purpose when compared with on‐site mapping, especially for event documentation and as basis for financial damage estimation and less complex numerical modelling.

Differential Exposure to Climate Change? Evidence from the 2021 Floods in Germany

We analyze the exposure of different income groups to the 2021 floods in Germany, which serve as an exemplary case of natural disasters intensified by anthropogenic climate change. To this end, we link official geo-coded satellite data on flood-affected buildings to neighborhood-level information on socio-economic status. We then document the empirical relationship between flood damages and household income. We limit comparisons to the vicinity of affected rivers and absorb a rich set of regional fixed effects to assess the differential exposure at the local level. Average household income is around 1,500 euros or three percent lower in flood-affected neighborhoods than in non-affected neighborhoods nearby. Average flood exposure is more than three times as high in the bottom sixty than in the upper forty percent of neighborhoods in terms of average household income. Our study is the first to document this regressive exposure along the income distribution based on actual flood damage data in Europe.

Toward a New Flood Assessment Paradigm: From Exceedance Probabilities to the Expected Maximum Floods and Damages

AbstractTo assess flood risks, we seek to estimate the probability distribution of the worst possible single‐event over a contiguous period of N years rather than the cumulative losses expected over a planning horizon. For this we use the probability distribution FN of extreme flood events over a multi‐year period, which is different from using the conventional single‐valued exceedance probability of 1/N years. FN can be used to estimate the hazard and then proceed to the estimation of risk, which we define as the “largest expected damage” over the set period. It also allows for a more coherent determination of design values, which descend from fully acknowledging the aleatoric uncertainty of the underlying natural river flow process. The epistemic uncertainty is removed by marginalizing the aleatoric‐epistemic uncertainty joint distribution over the parameter space. The advantage of the proposed Bayesian approach, which can be summarized in 12 steps, is demonstrated for the 2021 River Ahr flood in Germany, which caused casualties and huge material damage. Adopting the multi‐year maxima extreme value distribution can potentially lead to the reclassification of vulnerability levels for flood‐prone areas and reconsideration of current policy‐making and flood risk communication.

Pro-environmental voting when climate change is made salient: Evidence from high-resolution flooding data

Experiencing events such as extreme heat, flooding, or wildfires may affect political preferences and voting patterns, but existing evidence is mixed. Further, although scientists attribute the increasing incidence and severity of these events to climate change, it is typically uncertain whether the public makes this connection and, therefore, the channel leading from extreme weather events to political outcomes remains unclear. Here we consider a setting in which this connection was made very salient. We use high-resolution flooding and building-level damage data to identify spatially finely disaggregated effects of a large flood in Germany on pro-environmental voting. The flood’s destructiveness and temporal proximity to a general election entailed that media and politicians paid significant attention to the flood, drawing a connection to climate change. Our analysis shows that experiencing damage increases pro-environmental voting, suggesting that first-hand experiences of extreme weather events that are attributed to climate change affect political preferences.

Aware but not prepared: understanding situational awareness during the century flood in Germany in 2021

In July 2021, intense rainfall in parts of Western Europe was followed by unprecedented flash flooding. The flooding killed many people, mostly in Germany, which was subsequently blamed on a lack of preparation by either authorities or the broader populace. Knowledge about people's awareness of a hazard, its impacts and possible adaptation can help improve hazard management. We used social media data to assess situational awareness, sentiments and behaviour before, during and after the flood. We analysed nearly 58,000 German Twitter (now X) messages about the flood using machine learning-based unsupervised topic modelling. We showed that message frequency translated into four phases with message content suggesting sender priorities shifted in each phase of the flood. Besides messages with weather updates, correlated topics included ‘Solidarity, recovery and aid’ and ‘Grief and empathy’ (together 22% of all tweets) and a set of four topics about climate change attribution, extreme weather, long-term flood protection measures and politics (together 38% of all tweets). Many topics depended on the phase of the flood and on the distance from the affected areas. For those near the affected areas, tweets about evacuation (in peak phase) and damage assessment (in recovery phase) were particularly prominent. The high engagement on Twitter to seek weather information might indicate awareness of the extreme climatic conditions before the flood, but the severity was unexpected. Social media data provided unprecedent citizen-generated real-time information. We discuss how rapid automated analysis could contribute to disaster communication and risk mitigation.

The three A's of social capital in crises: Challenges with the availability, accessibility and activatability of social support

Social capital has become a major factor for analysing vulnerabilities and resilience in the context of disaster studies in recent years. Usually, it is studied along its three forms of bonding, bridging, and linking social capital, and it is often framed as a static characteristic that a person either has at his or her disposal or not. Based on the results of case studies conducted in Germany and Estonia focusing on four different crises (floods in Germany; long-term disruption of electricity due to a major storm in Estonia; a cyber-attack in Estonia; as well as the COVID-19 pandemic in both countries) we claim that this description and analysis of social capital does not allow for a comprehensive understanding of all the challenges disaster management has to deal with to decrease vulnerabilities and increase resilience. Using qualitative content analysis, we present a heuristic framework which not only asks whether bonding, bridging, and linking social capital is available to individuals, but also whether social capital is accessible and activatable when responding to or recovering from a disaster. In doing so, the paper helps to improve the overall usability of official or unofficial social support to cope with crises.

The wisdom of crowds for improved disaster resilience: a near-real-time analysis of crowdsourced social media data on the 2021 flood in Germany

Transformative disaster resilience in times of climate change underscores the importance of reflexive governance, facilitation of socio-technical advancement, co-creation of knowledge, and innovative and bottom-up approaches. However, implementing these capacity-building processes by relying on census-based datasets and nomothetic (or top-down) approaches remains challenging for many jurisdictions. Web 2.0 knowledge sharing via online social networks, whereas, provides a unique opportunity and valuable data sources to complement existing approaches, understand dynamics within large communities of individuals, and incorporate collective intelligence into disaster resilience studies. Using Twitter data (passive crowdsourcing) and an online survey, this study draws on the wisdom of crowds and public judgment in near-real-time disaster phases when the flood disaster hit Germany in July 2021. Latent Dirichlet Allocation, an unsupervised machine learning technique for Topic Modeling, was applied to the corpora of two data sources to identify topics associated with different disaster phases. In addition to semantic (textual) analysis, spatiotemporal patterns of online disaster communication were analyzed to determine the contribution patterns associated with the affected areas. Finally, the extracted topics discussed online were compiled into five themes related to disaster resilience capacities (preventive, anticipative, absorptive, adaptive, and transformative). The near-real-time collective sensing approach reflected optimized diversity and a spectrum of people’s experiences and knowledge regarding flooding disasters and highlighted communities’ sociocultural characteristics. This bottom-up approach could be an innovative alternative to traditional participatory techniques of organizing meetings and workshops for situational analysis and timely unfolding of such events at a fraction of the cost to inform disaster resilience initiatives.

Weather insurance in European crop and horticulture production

Weather risks threaten food production and put farms’ profitability at risk. Insurance solutions can compensate for the resulting financial losses and thereby serves as important risk management tool. While insurance providers in Europe have recently largely expanded the number and scope of offered weather insurance solutions, this remained undocumented in scientific literature. We here provide a structured assessment of agricultural insurance products that are on the market to cover weather risks in crop and horticulture production in dynamic European insurance markets, focusing on Austria, France, Germany, Italy, Spain and Switzerland. We systematically collect information on supplied insurance products (N = 107) and find large diversity in market concentrations, offered products, insured weather risks, and political market interventions. We find that for most economically relevant weather risks (especially for hail), insurance solutions are available to farmers, but we identify certain insurance protection gaps (e.g., flood in Germany). Additionally, drought and heat risks seem underrepresented in current insurance products, especially given their economic relevance. Indemnity insurance (where payouts are based on assessed losses) is offered most frequently, but weather index insurance (where payouts depend on the realization of an index, such as cumulative precipitation) is also increasingly available in Europe. Most of current weather index insurance solutions have a unique design, indicating a preference to stand out from the competition, and we observe little knowledge spillovers between markets. There are different levels of political market intervention and in particular a trend to introduce premium subsidies and towards a convergence of premium subsidy levels between markets. We highlight the need to align any political market intervention to other policy goals and to the transition to a more sustainable agriculture. Finally, we underline the important role of research in improving current insurance systems.

Differential Exposure to Climate Change? Evidence from the 2021 Floods in Germany

Differential Exposure to Climate Change? Evidence from the 2021 Floods in Germany

Tropospheric parameters from multi-GNSS and numerical weather models: case study of severe precipitation and flooding in Germany in July 2021

Storms and floods are the most frequent natural disaster in western and central Europe. Due to climate change, intensive storms with prolonged rain episodes will continue to cause even more destructive flooding. The good understanding and forecasting of such events are thus of uttermost importance. One of the ways to improve weather forecasts is the assimilation of external data, such as the global navigation satellite systems (GNSS). In this study, a preparation of the multi-GNSS tropospheric products—zenith total delays, tropospheric gradients and slant total delays—for future operational assimilation is shown. For a severe precipitation event in July 2021 in Germany, the GNSS parameters from three systems—GPS, GLONASS and Galileo—are compared to three Numerical Weather Models (NWMs)—ERA5 reanalysis of ECMWF, ICON run by the German Weather Service and GFS run by the US Weather Service. The flood that followed the rainfall was the deadliest natural disaster in Germany since 1962. The results show that all considered GNSS solutions have a similar level of agreement with the NWMs. However, for the flood region in western Germany, the biases from the multi-GNSS solutions are smaller compared to the GPS-only solution. From the models, ICON has the highest agreement with the GNSS data for all considered tropospheric parameters. The best agreement with the ICON is probably due to its high horizontal resolution and, thus, low representative errors and the fine tuning of DWD’s regional model for the specific region (Germany).

Brief communication: Impact forecasting could substantially improve the emergency management of deadly floods: case study July 2021 floods in Germany

Abstract. Floods affect more people than any other natural hazard; thus flood warning and disaster management are of utmost importance. However, the operational hydrological forecasts do not provide information about affected areas and impact but only discharge and water levels at gauges. We show that a simple hydrodynamic model operating with readily available data is able to provide highly localized information on the expected flood extent and impacts, with simulation times enabling operational flood warning. We demonstrate that such an impact forecast would have indicated the deadly potential of the 2021 flood in western Germany with sufficient lead time.

Innovative Vulnerability and Risk Assessment of Urban Areas against Flood Events: Prognosis of Structural Damage with a New Approach Considering Flow Velocity

The floods in 2002 and 2013, as well as the recent flood of 2021, caused billions Euros worth of property damage in Germany. The aim of the project Innovative Vulnerability and Risk Assessment of Urban Areas against Flood Events (INNOVARU) involved the development of a practicable flood damage model that enables realistic damage statements for the residential building stock. In addition to the determination of local flood risks, it also takes into account the vulnerability of individual buildings and allows for the prognosis of structural damage. In this paper, we discuss an improved method for the prognosis of structural damage due to flood impact. Detailed correlations between inundation level and flow velocities depending on the vulnerability of the building types, as well as the number of storeys, are considered. Because reliable damage data from events with high flow velocities were not available, an innovative approach was adopted to cover a wide range of flow velocities. The proposed approach combines comprehensive damage data collected after the 2002 flood in Germany with damage data of the 2011 Tohoku earthquake tsunami in Japan. The application of the developed methods enables a reliable reinterpretation of the structural damage caused by the August flood of 2002 in six study areas in the Free State of Saxony.

Water Harvesting from Air by Metal-Organic Frameworks: What Happens Next?

Thousands have lived without love, but none without water. The floods in Germany and China that took place in July 2021 have highlighted the need for drinkable water supply in post-disaster regions. Of all the materials that could bring potable water to those in a dire time of need, metal-organic frameworks have raised a few eyebrows because they could directly capture water from air, even under very dry conditions. But before water harvesting technologies based on metal-organic frameworks could serve their purposes by providing potable water, there are clearly more things to be done to resolve the current limitations on supporting materials, as well as the information asymmetry.

Environmental Strategies for Air Pollution Mitigation in Industrial Areas Using the Clean Development Mechanism (CDM)

a series of environmental hazards as an impact of air pollution and climate change, as an example, the heat waves of over 40°C in Egypt and Greece and the burning forests in Turkey as well as the floods in Germany. (https://ec.europa.eu/clima/change/consequences_en). Developed countries and the early industrializers created their wealth and reached their high standards of living pumping into the atmosphere huge amount of Green House Gases (GHG) long before the world understood the consequences of climate change. Whereas developing countries are now faced by too many restrictions when it comes to industrialization as to keep the GHG gas level maintained. Putting these countries in a situation of not being able to achieve any progress (Hegazy 2014) (Ministry of State for Environmental Affairs 2010).

Here Comes the Flood, but Not Failure? Lessons to Learn after the Heavy Rain and Pluvial Floods in Germany 2021

Floods are a known natural hazard in Germany, but the amount of precipitation and ensuing high death toll and damages after the events especially from 14 to 15 July 2021 came as a surprise. Almost immediately questions about failure in the early warning chains and the effectiveness of the German response emerged, also internationally. This article presents lessons to learn and argues against a blame culture. The findings are based on comparisons with findings from previous research projects carried out in the Rhein-Erft Kreis and the city of Cologne, as well as on discussions with operational relief forces after the 2021 events. The main disaster aspects of the 2021 flood are related to issuing and understanding warnings, a lack of information and data exchange, unfolding upon a situation of an ongoing pandemic and aggravated further by critical infrastructure failure. Increasing frequencies of flash floods and other extremes due to climate change are just one side of the transformation and challenge, Germany and neighbouring countries are facing. The vulnerability paradox also heavily contributes to it; German society became increasingly vulnerable to failure due to an increased dependency on its infrastructure and emergency system, and the ensuing expectations of the public for a perfect system.

Optimal Estimation MSG-SEVIRI Clear-Sky Total Column Water Vapour Retrieval Using the Split Window Difference

A new algorithm for the retrieval of day-time total column water vapour (TCWV) from measurements of a MSG-SEVIRI (Meteosat Second Generation Spinning Enhanced Visible and Infrared Imager) instrument is presented. The retrieval is based on a forward operator, at the core of which lies Radiative Transfer for TIROS Operational Vertical Sounder (RTTOV). This forward model relates TCWV and surface temperature to brightness temperatures in the split window at 11 and 12µm with the use of a first guess for temperature and humidity profiles from the ERA5 reanalysis. The forward model is then embedded in a full Optimal Estimation (OE) method, which yields pixel by pixel uncertainty estimates and performance indicators. The algorithm is applicable to any instrument which features the split window configuration, given a first guess for atmospheric conditions (i.e., from NWP) and an estimate of surface emissivity at 11 µm. The algorithm was developed within the framework of RealPEP (Near-Realtime Quantitative Precipitation Estimation and Prediction) in which the advancement of the estimation and nowcasting of extreme precipitation and flooding in Germany are studied. Thus, processing and validation has been limited to the German domain. Three independent ground-based TCWV observation data sets were used as reference, i.e., AERONET (Aerosol Robotic Network), GNSS Germany (Global Navigation Satellite System) and measurements from two MWR (Microwave Radiometer) sites. The validation concludes with good agreement, with absolute biases between 0.11 and 2.85 kg/m2, root mean square deviations (rmsds) between 1.63 and 3.24 kg/m2 and Pearson correlation coefficients ranging from 0.96 to 0.98. The retrievals uncertainty estimates were evaluated against AERONET. The comparison suggests that, in sum, uncertainties are estimated well, while still some error sources seem to be over- and underestimated. In limited case studies it could be shown that SEVIRI TCWV is capable to both display large scale variabilities in water vapour fields and reproduce the daily course of water vapour exposed by ground-based observations.

Spatiotemporal analysis of heavy rain-induced flood occurrences in Germany using a novel event database approach

Flash floods are a worldwide threat to humans, which is why they are being intensively studied using historical event records. As measurements and event data increase, databases are becoming increasingly important for flash flood research. However, the recent literature on flood databases lacks technical details as well as discussions about a suitable database design for scientific investigations. In this paper, we thus show how an event database for the investigation of heavy rain-induced flood occurrences can be created. Based on the HiOS dataset (a German dataset with ~ 23,800 flash flood and pluvial flood events), we exemplify the database design and explore the spatiotemporal characteristics of floods caused by heavy rain in Germany. We outline all aspects relevant to database setup: from database requirements and system architecture through table and attribute design to a key and relationship definition. Furthermore, we clarify why a spatial database with interfaces for GIS softwares should be chosen, why a damage-based event definition is preferable to a hydrometeorological definition, and how table attributes support differentiated analyses. By means of the database, we investigated frequency, temporal evolution, spatial distribution and patterns, fatalities and injuries, as well as the seasonality of heavy rain-induced floods in Germany. The results indicate that floods caused by heavy rain occur throughout Germany but with a tendency toward fewer events in the northern direction. Across the country, we identified seven hot spots in urbanized and mountainous regions. Although heavy rain-induced floods in Germany take place mostly between noon and late afternoon, most people are injured and killed in events starting in the evening. Our investigation indicates an increased incidence of flash flood and pluvial flood-related injuries and fatalities in the identified hot spots. Overall, we observe a pronounced summer seasonality of the heavy rain-induced flood events. This study highlights the importance of event databases for flash flood research and advances our understanding of heavy rain-induced flood occurrences in Germany.

Floods, flood policies and changes in welfare and inequality: Evidence from Germany

Floods in Germany have caused significant economic damage and are expected to become more frequent. The economic damage expected as a result of floods will affect households differently across income levels. A method, rooted in economic theory, is proposed to estimate changes in household welfare and income inequality resulting from expected flood damage. It is found that welfare losses after floods disproportionately harm low income households and increase inequality by 0.14%. Families with dependent children and householders of retirement age in the lowest income quartile are the most affected. In order to collect additional resources to cover the cost of the financial relief, the of additional income, real estate and energy taxes are investigated. These instruments yield a reduction in income inequality. It is also shown that rewarding investment in mitigation and insurance, and supporting low income households can be jointly achieved when re-allocating additional energy-tax revenues.