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

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 …

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 …

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 …

Abstract Machine learning (ML) algorithms have produced remarkable advances in
streamflow prediction, exceeding the performance of calibrated conceptual and physics …

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

Ingesting near-real-time observation data is a critical component of many operational
hydrological forecasting systems. In this paper we compare two strategies for ingesting near …

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 …

Accurate and timely flood prediction can reduce the risk of flooding, bolster preparedness,
and help build resilience. In this study, we have developed a flood forecasting system …