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

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<p>Os principais contribuintes antropogênicos para a contaminação do ar são as emissões veiculares e industriais. Santa Catarina é um estado com setor industrial desenvolvido. Destaca-se não apenas pelo número, como também pela diversificação de atividades fabris. Dentre essas atividades, há a de produção de peças cerâmicas vermelhas (tijolos e telhas - as olarias). Há uma concentração representativa dessas empresas no sul desse estado. Suspeita-se que as emissões atmosféricas das olarias comprometam a qualidade do ar, de forma danosa a saúde da população. Entretanto, quase não foram realizados estudos aprofundados, nem da qualidade do ar, muito menos das emissões do setor. Esse trabalho visa verificar como é tratada a questão das emissões atmosféricas por parte das olarias. Tal levantamento foi realizado por duas maneiras: aplicação de questionários e levantamento de informações de licenciamento ambiental. Nos questionários, notou-se que apenas 13% das olarias afirmaram manter os seus equipamentos de tratamento de efluentes atmosféricos ativos durante todo o período da queima dos fornos. Na análise de documentos vinculados ao licenciamento ambiental, averiguou-se que uma grande parcela das olarias não realiza laudos de emissões, como também não verifica o adequado funcionamento dos seus equipamentos de controle de poluição atmosférica. Apesar dos laudos de emissão indicarem compatibilidade com os limites legais de emissão, a concentração dessas empresas em um espaço geográfico restrito indica que provavelmente haja impacto significativo na qualidade do ar.</p>

Daily streamflow dynamics can be accurately simulated by conceptual models as simple as a single bucket in some catchments, while they require more complex configurations in other catchments. However, without resorting to calibration, anticipating where and why a given model structure may be appropriate remains difficult. In this work, we explored the feasibility of relating suitable model structures to the climate and streamflow characteristics of 508 catchments in Brazil. Specifically, we tested four model structures using up to three reservoirs, where each reservoir is intended to represent a catchment function: the rainfall‐runoff threshold, the fast, and the slow hydrograph response. We hypothesized a relationship between suitable model structures and hydrological signatures of aridity (IA) and baseflow index (IB). Our results show that different classes of signatures resulted in distinct patterns of model performance. Wet catchments (IA < 0.9) with low baseflow (IB < 0.4) were the easiest to model, with a single‐reservoir model presenting a relatively good performance. In the case of low baseflow, adding a rainfall‐runoff threshold reservoir resulted in better performance than adding a slow response reservoir, whereas in the case of high baseflow (IB < 0.6) the opposite occurred. In the case of low baseflow, the inclusion of a slow response reservoir helped the simulation of dry catchments (IA < 1.1), but not of wet ones, which we attributed to the impact of permeability in dry catchments. These results indicate a path toward model structure identification from streamflow signatures and potentially from landscape features.