Process-based hydrological models have a long history dating back to the 1960s. Criticized
by some as over-parameterized, overly complex, and difficult to use, a more nuanced view is …

Nowadays, mathematical models of hydrological systems are used routinely to guide
decision making in diverse subjects, such as: environmental and risk assessments, design …

This paper is derived from a keynote talk given at the Google's 2020 Flood Forecasting
Meets Machine Learning Workshop. Recent experiments applying deep learning to rainfall …

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 Earth below ground is the subject of interest for many geophysical as well as geological
investigations. Even though most practitioners would agree that all available information …

Abstract The Prediction in Ungauged Basins (PUB) initiative of the International Association
of Hydrological Sciences (IAHS), launched in 2003 and concluded by the PUB Symposium …

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 …

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 …

This paper presents a discussion of some of the issues associated with the multiple sources
of uncertainty and non-stationarity in the analysis and modelling of hydrological systems …

This review and commentary sets out the need for authoritative and concise information on
the expected error distributions and magnitudes in observational data. We discuss the …