There is a growing consensus that solutions to complex science and engineering problems
require novel methodologies that are able to integrate traditional physics-based modeling …

Deep Learning (DL) methods have gained significant recognition in hydrology and water
resources applications in recent years. Beginning with a discussion on fundamental …

Physics-based models are often used to study engineering and environmental systems. The
ability to model these systems is the key to achieving our future environmental sustainability …

There exists a substantial disparity in the distribution of streamflow gauge and basin
characteristic information, with a majority of flood observations being recorded from a limited …

Lake thermal dynamics have been considerably impacted by climate change, with potential
adverse effects on aquatic ecosystems. To better understand the potential impacts of future …

Excellent water quality (WQ) is an indispensable element in ensuring sustainable water
resource development. It is highly associated with the 3rd (good health and well-being), the …

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 …

River water quality is crucial to ecosystem health and water security, yet its deterioration
under climate change is often overlooked in climate risk assessments. Here we review how …

The scholarly literature on the links between Artificial Intelligence and the United Nations'
Sustainable Development Goals is burgeoning as climate change and the biotic crisis …

The global decline of water quality in rivers and streams has resulted in a pressing need to
design new watershed management strategies. Water quality can be affected by multiple …