Process-based modelling offers interpretability and physical consistency in many domains of
geosciences but struggles to leverage large datasets efficiently. Machine-learning methods …

Rapid and accurate detection of time-delayed water quality indicators (WQIs) is the key to
achieving fast feedback regulation of wastewater treatment plants (WWTPs) that enables its …

There is a scarcity of streamflow data owing to the limited availability of gauge networks or
delayed gauging in most parts of the world. To overcome this challenge and reproduce long …

The runoff prediction can provide scientific basis for flood control, disaster reduction and
water resources planning. Due to a large number of uncertainties in runoff prediction, it is …

This study presents a new approach to understand the causes of groundwater drought
events with interpretable deep learning (DL) models. As prerequisites, accurate long short …

Streamflow prediction in ungauged catchments is a challenging task in hydrological studies.
Recently, data-driven models have demonstrated their superiority over traditional …

Water quality prediction is an important means for scientific water environment management
and early warning measures. However, its reliability is often restricted by the uncertainties of …

Precise streamflow forecasting in river systems is crucial for water resources management
and flood risk assessment. The Tagus Headwaters River Basin (THRB) in Spain is a key …

Land use change and excessive nitrogen (N) loading threaten the health of receiving water
bodies worldwide. However, the role of hydrological connectivity in linking watershed land …

Increasing algal blooms in freshwater lakes have become a serious challenge facing the
world. Short-term forecast of chlorophyll-a concentration (Chla) is essential for providing …