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

This study investigates the ability of long short-term memory (LSTM) neural networks to
perform streamflow prediction at ungauged basins. A set of state-of-the-art, hydrological …

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

Abstract Model calibration is the procedure of finding model settings such that simulated
model outputs best match the observed data. Model calibration is necessary when the …

Machine learning (ML) has played an increasing role in the hydrological sciences. In
particular, certain types of time series modeling strategies are popular for rainfall–runoff …

Hydrological models are usually calibrated to in‐situ streamflow observations with
reasonably long and uninterrupted records. This is challenging for poorly gage or ungaged …

Deep learning (DL) rainfall–runoff models outperform conceptual, process-based models in
a range of applications. However, it remains unclear whether DL models can produce …

Abstract Models play a pivotal role in advancing our understanding of Earth's physical
nature and environmental systems, aiding in their efficient planning and management. The …

Over the past few decades, the hydrology community has witnessed notable advancements
in streamflow prediction, particularly with the introduction of cutting-edge machine-learning …

Building accurate rainfall–runoff models is an integral part of hydrological science and
practice. The variety of modeling goals and applications have led to a large suite of …