Understanding and predicting the quality of inland waters are challenging, particularly in the
context of intensifying climate extremes expected in the future. These challenges arise partly …

Hybrid hydroclimatic forecasting systems employ data-driven (statistical or machine
learning) methods to harness and integrate a broad variety of predictions from dynamical …

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 …

The most accurate rainfall–runoff predictions are currently based on deep learning. There is
a concern among hydrologists that the predictive accuracy of data-driven models based on …

Regional rainfall–runoff modeling is an old but still mostly outstanding problem in the
hydrological sciences. The problem currently is that traditional hydrological models degrade …

High-quality datasets are essential to support hydrological science and modeling. Several
CAMELS (Catchment Attributes and Meteorology for Large-sample Studies) datasets exist …

Predictions of hydrologic variables across the entire water cycle have significant value for
water resources management as well as downstream applications such as ecosystem and …

Long short‐term memory (LSTM) networks offer unprecedented accuracy for prediction in
ungauged basins. We trained and tested several LSTMs on 531 basins from the CAMELS …

Rainfall–runoff modelling is one of the key challenges in the field of hydrology. Various
approaches exist, ranging from physically based over conceptual to fully data-driven …

The goal of this commentary is to critically evaluate the use of popular performance metrics
in hydrologic modeling. We focus on the Nash‐Sutcliffe Efficiency (NSE) and the Kling …