Hydroclimatic time series analysis focuses on a few feature types (e.g., autocorrelations, trends, extremes), which describe a small portion of the entire information content of the observations. Aiming to exploit a larger part of the available information and, thus, to deliver more reliable results (e.g., in hydroclimatic time series clustering contexts), here we approach hydroclimatic time series analysis differently, i.e., by performing massive feature extraction. In this respect, we develop a big data framework for hydroclimatic variable behaviour characterization. This framework relies on approximately 60 diverse features and is completely automatic (in the sense that it does not depend on the hydroclimatic process at hand). We apply the new framework to characterize mean monthly temperature, total monthly precipitation and mean monthly river flow. The applications are conducted at the global scale by exploiting 40-year-long time series originating from over 13 000 stations. We extract interpretable knowledge on seasonality, trends, autocorrelation, long-range dependence and entropy, and on feature types that are met less frequently. We further compare the examined hydroclimatic variable types in terms of this knowledge and, identify patterns related to the spatial variability of the features. For this latter purpose, we also propose and exploit a hydroclimatic time series clustering methodology. This new methodology is based on Breiman's random forests. The descriptive and exploratory insights gained by the global-scale applications prove the usefulness of the adopted feature compilation in hydroclimatic contexts. Moreover, the spatially coherent patterns characterizing the clusters delivered by the new methodology build confidence in its future exploitation...

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.

The leaky pipeline phenomenon refers to the disproportionate decline of female scientists at higher academic career levels and is a major problem in the natural sciences. Identifying the underlying causes is challenging, and thus, solving the problem remains difficult. To better understand the reasons for the leaky pipeline, we assess the perceptions and impacts of gender bias and imbalance—two major drivers of the leakage—at different academic career levels with an anonymous survey in geoscience academia (n=1,220). The survey results show that both genders view male geoscientists as substantially more gender biased than female scientists. Moreover, female geoscientists are more than twice as likely to experience negative gender bias at their workplaces and scientific organizations compared to male geoscientists. There are also pronounced gender differences regarding (i) the relevance of role models, (ii) family-friendly working conditions, and (iii) the approval of gender quotas for academic positions. Given the male dominance in senior career levels, our results emphasize that those feeling less impacted by the negative consequences of gender bias and imbalance are the ones in position to tackle the problem. We thus call for actions to better address gender biases and to ensure a balanced gender representation at decision-making levels to ultimately retain more women in geoscience academia.