The Nash–Sutcliffe efficiency (NSE) is a widely used score in hydrology, but it is not common in the other environmental sciences. One of the reasons for its unpopularity is that its scientific meaning is somehow unclear in the literature. This study attempts to establish a solid foundation for NSE from the viewpoint of signal progressing. This approach is shown to yield profound explanations to many open problems related to NSE. A generalized NSE that can be used in general cases is proposed.

Although flood early warning systems (FEWS) are promising, they inevitably issue false alarms. Many false alarms undermine the credibility of FEWS, which we call a cry wolf effect. Here, we present a simple model that can simulate the cry wolf effect. Our model implies that the cry wolf effect is important if a community is heavily protected by infrastructure and few floods occur. The cry wolf effects get more important as the natural scientific skill to predict flood events is improved.

One common approach to cope with floods is the implementation of structural flood protection measures, such as levees. Numerous scholars have problematized this approach and shown that increasing levels of flood protection can generate a false sense of security and attract more people to the risky areas. We briefly review the literature on this topic and then propose a research agenda to explore the unintended consequences of structural flood protection.

There is large uncertainty in the future development of flood patterns, e.g., due to climate change. We quantify relevant uncertainties and show how they can be used for flood protection planning. We find that one ought to include an estimate of uncertainty that cannot be quantified from available data (hidden uncertainty), since projections and data at hand often cover only a limited range of the uncertainty spectrum. Furthermore, dependencies between climate projections must be accounted for.