Over the past decades, the scientific community has made significant efforts to simulate
flooding conditions using a variety of complex physically based models. Despite all …

Long-term gridded precipitation products (GPPs) are crucial for climatology and hydrological
research to overcome the limitations of gauge observations. Climate Hazards Group …

This study proposes a new hybrid model for monthly streamflow predictions by coupling a
physically based distributed hydrological model with a deep learning (DL) model …

Accurate forecasts of streamflow (Q flow) are crucial for optimal management of water
reservoir systems and preparing for catastrophic events such as floods. Although several …

Precipitation is the most important driver of the hydrological cycle but is challenging to
estimate over large scales from satellites and models. Here, we assessed the performance …

Accurate water level forecasting is important to understand and provide an early warning of
flood risk and discharge. It is also crucial for many plants and animal species that needs …

This paper presents the development of hybrid machine learning models to forecast the
natural flows of water bodies. Five models were considered under the analysis: extreme …

In data scarce regions, satellite-derived products can be used as alternatives to ground
observed rainfall. However, satellite-derived data quality assurance and its quick use are of …

The temperature of river water (TRW) is an important factor in river ecosystem predictions.
This study aims to compare two different types of numerical model for predicting daily TRW …

Machine learning (ML) applications in hydrology are revolutionizing our understanding and
prediction of hydrological processes, driven by advancements in artificial intelligence and …