  Delivering useful hydrological forecasts is critical for urban and
agricultural water management, hydropower generation, flood protection and
management, drought mitigation and alleviation, and river basin planning and
management, among others. In this work, we present and appraise a new simple
and flexible methodology for hydrological time series forecasting. This
methodology relies on (a) at least two individual forecasting methods and (b)
the median combiner of forecasts. The appraisal is made by using a big dataset
consisted of 90-year-long mean annual river flow time series from approximately
600 stations. Covering large parts of North America and Europe, these stations
represent various climate and catchment characteristics, and thus can
collectively support benchmarking. Five individual forecasting methods and 26
variants of the introduced methodology are applied to each time series. The
application is made in one-step ahead forecasting mode. The individual methods
are the last-observation benchmark, simple exponential smoothing, complex
exponential smoothing, automatic autoregressive fractionally integrated moving
average (ARFIMA) and Facebook's Prophet, while the 26 variants are defined by
all the possible combinations (per two, three, four or five) of the five
afore-mentioned methods. The new methodology is identified as well-performing
in the long run, especially when more than two individual forecasting methods
are combined within its framework. Moreover, the possibility of case-informed
integrations of diverse hydrological forecasting methods within systematic
frameworks is algorithmically investigated and discussed. The related
investigations encompass linear regression analyses, which aim at finding
interpretable relationships between the values of a representative forecasting
performance metric and the values of selected river flow statistics...
