Spatially explicit quantification of design storms is essential for flood risk assessment and planning. However, available datasets are mainly based on spatially interpolated station-based design storms. Since the spatial interpolation of the data inherits a large potential for uncertainty, we develop an approach to be able to derive spatially explicit design storms on the basis of weather radar data. We find that our approach leads to an improved spatial representation of design storms.

In socio-hydrology, human–water interactions are investigated. Researchers have two major methodologies in socio-hydrology, namely mathematical modeling and empirical data analysis. Here we propose a new method for bringing the synergic effect of models and data to socio-hydrology. We apply sequential data assimilation, which has been widely used in geoscience, to a flood risk model to analyze the human–flood interactions by model–data integration.

In the climate/hydrology network, each node represents a geographical location of climatological data, and links between nodes are set up based on their interaction or similar variability. Here, using network theory, we first generate a node-ranking measure and then prioritize the rain gauges to identify influential and expandable stations across Germany. To show the applicability of the proposed approach, we also compared the results with existing traditional and contemporary network measures.