Predictions of floods, droughts, and fast drought‐flood transitions are required at different
time scales to develop management strategies targeted at minimizing negative societal and …

The African continent hosts some of the largest freshwater systems worldwide, characterized
by a large distribution and variability of surface waters that play a key role in the water …

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 …

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 …

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 …

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 …

Google's operational flood forecasting system was developed to provide accurate real-time
flood warnings to agencies and the public with a focus on riverine floods in large, gauged …

Abstract Model calibration and validation are critical in hydrological model robustness
assessment. Unfortunately, the commonly used split‐sample test (SST) framework for data …

Runoff prediction in ungauged and scarcely gauged catchments is a key research field in
surface water hydrology. There have been numerous studies before and since the launch of …

We present a new data set of attributes for 671 catchments in the contiguous United States
(CONUS) minimally impacted by human activities. This complements the daily time series of …