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 on-line 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 focussed on 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.

L-moments method is briefly described and compared with classical methods of moments and maximal likelihood. Algorithms are given for calculating parameters of some distributions widely used in engineering hydrology, meteorology, etc. The advantages of L-moments method relative to alternative methods are analyzed for several examples.

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 study is focused on the comparison of streamflow composition simulated with three well-known rainfall–runoff (RR) models (ECOMAG, HBV, SWAT) against hydrograph decomposition evaluated with End-Member Mixing Analysis (EMMA). In situ observations at two small mountain testbed catchments located in the south of Pacific Russia are used. All applied RR models and EMMA analysis demonstrate that two neighboring catchments disagree significantly on the mutual dynamics of the runoff sources. The RR models' benchmark test is based on proximity to EMMA hydrograph composition. Different aggregation intervals (season, month, and pentad) were applied to find a reasonable generalization period ensuring the clarity of results. ECOMAG is most conformable to EMMA outcome; HBV reflects flood events well enough; SWAT exhibits distinctive behavior compared to the other models. It is shown that, along with standard efficiency criteria of simulated and observed runoff proximity, EMMA analysis might provide useful auxiliary information for the validation of modelling results.

The aim of this study is to apply mathematical modeling to describe the hydrological regime of small testbed catchments located in the territory of the Verkhne-Ussuriisky Biocenological...