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Whether the floods experienced during the last decade in Germany and in other European countries are triggered or worsened by human activities has been the subject of a great deal of debate. Possible anthropogenic activities leading to increased flood risk include river regulation measures, intensified land use and forestry, and emissions of greenhouse gases causing a change in the global climate. This article discusses the latter by reviewing the existing knowledge on the subject. First, the relevance, capabilities, and limitations of climate models for the simulation and analysis of flood risk under aspects of the anthropogenic climate change are described. Special consideration is given here to differences between the "typical" spatial scale of climate models and hydrological flood models. Second, observations of trends in climate variables relevant for river flooding issues are summarized. Special emphasis is put on the Rhine and other German catchment areas. Third, the possibilities of modeling the different parts of the "cascade of flood risk" are summarized, introducing the special features of meteorological, hydrological, and river hydraulic models.

Abstract. Data-driven models have been recently suggested to surrogate computationally expensive hydrodynamic models to map flood hazards. However, most studies focused on developing models for the same area or the same precipitation event. It is thus not obvious how transferable the models are in space. This study evaluates the performance of a convolutional neural network (CNN) based on the U-Net architecture and the random forest (RF) algorithm to predict flood water depth, the models' transferability in space and performance improvement using transfer learning techniques. We used three study areas in Berlin to train, validate and test the models. The results showed that (1) the RF models outperformed the CNN models for predictions within the training domain, presumable at the cost of overfitting; (2) the CNN models had significantly higher potential than the RF models to generalize beyond the training domain; and (3) the CNN models could better benefit from transfer learning technique to boost their performance outside training domains than RF models.

Abstract. Flood detention areas serve the primary purpose of controlled water storage during large flood events in order to decrease the flood risk downstream along the river. These areas are often used for agricultural production. While various damage estimation methods exist for urban areas, there are only a few, most often simpler approaches for loss estimation in rural areas. The loss assessment can provide an estimate of the financial provisions required for the farmers' compensation (e.g., in the context of cost-benefit analyses of detention measures). Flood risk is a combination of potential damage and probability of flooding. Losses in agricultural areas exhibit a strong seasonal pattern, and the flooding probability also has a seasonal variation. In the present study, flood risk is assessed for a planned detention area alongside the Elbe River in Germany based on two loss and probability estimation approaches of different time frames, namely a monthly and an annual approach. The results show that the overall potential damage in the proposed detention area amounts to approximately 40 000 € a−1, with approximately equal losses for each of the main land uses, agriculture and road infrastructure. A sensitivity analysis showed that the probability of flooding (i.e., the frequency of operation of the detention area) has the largest impact on the overall flood risk.

In December 2000, the Water Framework Directive (WFD) of the European Union (EU) was enforced (EC, 2000) to provide a new legislative basis for water management in Europe. The main goal of the WFD is the implementation of river basin water management plans in which comprehensive studies of the current status of the surface and ground water bodies must be reported and management programs must be enforced with cost-effective measures with which a good ecological condition of the water bodies can be attained and sustained.

Abstract. In this paper, we analyse the effectiveness of flood management measures based on the concept known as "retaining water in the landscape". The investigated measures include afforestation, micro-ponds and small-reservoirs. A comparative and model-based methodological approach has been developed and applied for three meso-scale catchments located in different European hydro-climatological regions: Poyo (184 km2) in the Spanish Mediterranean, Upper Iller (954 km2) in the German Alps and Kamp (621 km2) in Northeast-Austria representing the Continental hydro-climate. This comparative analysis has found general similarities in spite of the particular differences among studied areas. In general terms, the flood reduction through the concept of "retaining water in the landscape" depends on the following factors: the storage capacity increase in the catchment resulting from such measures, the characteristics of the rainfall event, the antecedent soil moisture condition and the spatial distribution of such flood management measures in the catchment. In general, our study has shown that, this concept is effective for small and medium events, but almost negligible for the largest and less frequent floods: this holds true for all different hydro-climatic regions, and with different land-use, soils and morphological settings.