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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With more machine learning methods being involved in social and environmental research activities, we are addressing the role of available information for model training in model performance. We tested the abilities of several machine learning models for short-term hydrological forecasting by inferring linkages with all available predictors or only with those pre-selected by a hydrologist. The models used in this study were multivariate linear regression, the M5 model tree, multilayer perceptron (MLP) artificial neural network, and the long short-term memory (LSTM) model. We used two river catchments in contrasting runoff generation conditions to try to infer the ability of different model structures to automatically select the best predictor set from all those available in the dataset and compared models’ performance with that of a model operating on predictors prescribed by a hydrologist. Additionally, we tested how shuffling of the initial dataset improved model performance. We can conclude that in rainfall-driven catchments, the models performed generally better on a dataset prescribed by a hydrologist, while in mixed-snowmelt and baseflow-driven catchments, the automatic selection of predictors was preferable.

This article aims to discuss the contributions of morphometric and geomorphological cartography for the analysis of the relief in sister basins. It presents the application of a methodology that proposes the organization of a sequence of cartographic documents: Declivity or Clinographic, Horizontal Dissection, Vertical Dissection, Relief Energy and Geomorphological Detail. The hydrographic basin, a spatial mapping unit, is understood as a system, as well as an integrative functional unit for the analysis of geomorphological dynamics. There for, we applied the cartographic techniques mentioned in sister basins, with the case study of the basins of Piracicamirim and Tijuco Preto rivers, both tributaries of Piracicaba River, located in Peripheral Depression of São Paulo. The results obtained from the analysis of the organized documents contributed to the identification of areas potentially susceptible to the unleashing of denudatory processes, as well as gauged the denudatory features, especially the linear ones. Of particular note were sectors that presented marked denudatory features that were associated to classes with high morphometric potential in areas of pedological/lithological fragility and intense dynamics of land use and occupation, marked by sugar cane monoculture.