Recently, deep learning (DL) has emerged as a revolutionary tool for transforming industries and scientific disciplines. We argue that DL can offer a complementary avenue toward advancing hydrology. New methods are being developed to interpret the knowledge learned by deep networks. We argue that open competitions, integrating DL and process-based models, more data sharing, data collection from citizen scientists, and improved education will be needed to incubate advances in hydrology.

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.

We now lack sufficient empirical observations of consumptive use of water by humans and their economy, so it is worth considering what we can do with the withdrawal-based water use data we already possess. Fortunately, a wide range of applied water management and policy questions can be addressed using currently available withdrawal data. This discussion identifies important data collection problems and argues that the withdrawal data we already possess are adequate for some important purposes.

Media play a key role in the communication between scientists and the general public. However, the interaction between scientists and journalists is not always straightforward. In this opinion paper, we present insights from hydrologists and journalists into the benefits, aftermath and potential pitfalls of science–media interaction. We aim to encourage scientists to participate in the diverse and evolving media landscape, and we call on the scientific community to support scientists who do so.

This paper provides the connection between two simple equations describing groundwater flow at different scales: the Darcy equation describes groundwater flow at pore scale, the linear reservoir equation at catchment scale. The connection between the two appears to be very simple. The two parameters of the equations are proportional, depending on the porosity of the subsoil and the resistance for the groundwater to enter the surface drainage network.