This paper is the outcome of a community initiative to identify major unsolved scientific problems in hydrology motivated by a need for stronger harmonisation of research efforts. The procedure involved a public consultation through online media, followed by two workshops through which a large number of potential science questions were collated, prioritised, and synthesised. In spite of the diversity of the participants (230 scientists in total), the process revealed much about community priorities and the state of our science: a preference for continuity in research questions rather than radical departures or redirections from past and current work. Questions remain focused on the process-based understanding of hydrological variability and causality at all space and time scales. Increased attention to environmental change drives a new emphasis on understanding how change propagates across interfaces within the hydrological system and across disciplinary boundaries. In particular, the expansion of the human footprint raises a new set of questions related to human interactions with nature and water cycle feedbacks in the context of complex water management problems. We hope that this reflection and synthesis of the 23 unsolved problems in hydrology will help guide research efforts for some years to come. ARTICLE HISTORY

This paper presents a score that can be used for evaluating probabilistic forecasts of multicategory events. The score is a reinterpretation of the logarithmic score or ignorance score, now formulated as the relative entropy or Kullback–Leibler divergence of the forecast distribution from the observation distribution. Using the information–theoretical concepts of entropy and relative entropy, a decomposition into three components is presented, analogous to the classic decomposition of the Brier score. The information–theoretical twins of the components uncertainty, resolution, and reliability provide diagnostic information about the quality of forecasts. The overall score measures the information conveyed by the forecast. As was shown recently, information theory provides a sound framework for forecast verification. The new decomposition, which has proven to be very useful for the Brier score and is widely used, can help acceptance of the logarithmic score in meteorology.

Several commonly-used nonparametric change-point detection methods are analysed in terms of power, ability and accuracy of the estimated change-point location. The analysis is performed with synthetic data for different sample sizes, two types of change and different magnitudes of change. The methods studied are the Pettitt method, a method based on the Cramér von Mises (CvM) two-sample test statistic and a variant of the CUSUM method. The methods differ considerably in behaviour. For all methods the spread of estimated change-point location increases significantly for points near one of the ends of the sample. Series of annual maximum runoff for four stations on the Yangtze River in China are used to examine the performance of the methods on real data. It was found that the CvM-based test gave the best results, but all three methods suffer from bias and low detection rates for change points near the ends of the series.

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<div> <div> <div> <div>Automatic control of water systems such as canals for shipping, irrigation systems, drainage systems, or sewer systems, is a complex problem. While the system state is continuously changing, in almost all cases the weir, gates, or pumps are adjusted only at set times. This mixes continuous and discrete time. Moreover, it may be necessary to take action in response to the occurrence of an event in the system. So, part of the system evolves continuously, another part changes stepwise at given times, while yet another part responds to events in the system or its surroundings that may occur at arbitrary times.</div> <div>Basic limitations on controlling these systems when ignoring their hybrid nature are demonstrated for the case of a Dutch sewer system. The control schemes under discussion are: local event driven control for a group of pump stations, sampled data control for a group of pump stations, hierarchical control with sampled data control for the group, and event driven control for the individual stations.</div> </div> </div> </div>