This perspective discusses the importance of characterizing, quantifying, and accounting for
various sources of uncertainties involved in different layers of hydrometeorological and …

The formation of urban coastal flooding is mainly ruled by the interaction between rainfall-
runoff and storm surge. This study aims to advance the understanding of coastal urban flood …

Large-scale afforestation programmes are generally presented as effective ways of
increasing the terrestrial carbon sink while preserving water availability and biodiversity. Yet …

Highlights•Data space inversion (DSI) methods enable fast prediction of system
performance.•A new DSI method is developed based on machine learning and ensemble …

Water resources planning and management are dependable on an adequate integration of
physical, chemical, biological, and socio-economic realities of multiple water users. The …

Accurate estimates of groundwater modeling in arid regions have a crucial role in reaching a
sustainable management of groundwater sources. However, groundwater modeling has …

Numerical modeling is a significant tool to understand the dynamic characteristics of
contaminants transport in groundwater. The automatic calibration of highly parametrized and …

Highly transient boundary conditions affect mixing of dissolved solutes in groundwater. An
example of these transient boundary conditions occurs at the surface water-groundwater …

Groundwater flooding can cause severe damages to homes, utilities, and infrastructure and
yield significant economic and social costs. Numerical models are used to understand these …

Abstract Physics-Informed Neural Networks (PINN) are a machine learning tool that can be
used to solve direct and inverse problems related to models described by Partial Differential …