Understanding and predicting the quality of inland waters are challenging, particularly in the
context of intensifying climate extremes expected in the future. These challenges arise partly …

Rivers and river habitats around the world are under sustained pressure from human
activities and the changing global environment. Our ability to quantify and manage the river …

This paper proposes a graph-based meta learning approach to separately predict water
quantity and quality variables for river segments in stream networks. Given the …

Deep learning (DL) models are increasingly used to make accurate hindcasts of
management‐relevant variables, but they are less commonly used in forecasting …

Modeling environmental ecosystems is critical for the sustainability of our planet, but is
extremely challenging due to the complex underlying processes driven by interactions …

Physics-based groundwater flow equations are powerful tools for water resource
assessment under different hydrological and climatic conditions. How these conditions affect …

Abstract The Hybrid Optimization Software Suite (HOSS), which combines the finite-discrete
element method (FDEM), is an advanced approach for simulating high-fidelity fracture and …

Deep learning (DL) models are increasingly used to forecast water quality variables for use
in decision making. Ingesting recent observations of the forecasted variable has been …

Spatiotemporal deep learning (DL) has emerged as a promising paradigm for hydrological
simulation compared with lumped models using basin-averaged inputs. However, existing …

Accurate prediction of water quality and quantity is crucial for sustainable development and
human well-being. However, existing data-driven methods often suffer from spatial biases in …