While infiltration source-control technologies are increasingly used to manage the volume,
rate and quality of stormwater runoff, there is little guidance on their role and impact on …

This paper discusses the modelling of rainfall–flow (rainfall–run–off) and flow–routeing
processes in river systems within the context of real–time flood forecasting. It is argued that …

Long short-term memory (LSTM) models are recurrent neural networks from the field of deep
learning (DL) which have shown promise for time series modelling, especially in conditions …

Neural networks have been shown to be extremely effective rainfall-runoff models, where
the river discharge is predicted from meteorological inputs. However, the question remains …

Ensemble forecasting applied to the field of hydrology is currently an established area of
research embracing a broad spectrum of operational situations. This work catalogs the …

Rainfall-Runoff Modelling: The Primer, Second Edition is the follow-up of this popular and
authoritative text, first published in 2001. The book provides both a primer for the novice and …

This book is open access under a CC BY-NC 4.0 license. This revised, updated textbook
presents a systems approach to the planning, management, and operation of water …

This essay discusses some of the issues involved in the identification and predictions of
hydrological models given some calibration data. The reasons for the incompleteness of …

Meaningful quantification of data and structural uncertainties in conceptual rainfall‐runoff
modeling is a major scientific and engineering challenge. This paper focuses on the total …

Ambiguities in the representation of environmental processes have manifested themselves
in a plethora of hydrological models, differing in almost every aspect of their …