Robert J. Abrahart

TL;DR: An open access web site that can be used by hydrologists and other scientists to evaluate time series models and includes an open forum that is intended to encourage further discussion and debate on the topic of hydrological performance evaluation metrics.

TL;DR: The field is now firmly established and the research community involved has much to offer hydrological science, but it will be necessary to converge on more objective and consistent protocols for selecting and treating inputs prior to model development; extracting physically meaningful insights from each proposed solution; and improving transparency in the benchmarking and reporting of experimental case studies.

TL;DR: In this paper, the authors used Artificial Neural Networks (ANNs) to predict T-year flood events and the index flood (the median of the annual maximum series) for 850 catchments across the UK.

TL;DR: In this paper, the internal behavior of an artificial neural network rainfall runoff model is examined and it is demonstrated that specific architectural features can be interpreted with respect to the quasi-physical dynamics of a parsimonious water balance model.

TL;DR: Evaluating six published data fusion strategies for hydrological forecasting based on two contrasting catchments reveals unequal aptitudes for fixing different categories of problematic catchment behaviour and, in such cases, the best method were a good deal better than their closest rival(s).