Big Data and machine learning (ML) technologies have the potential to impact many facets
of environment and water management (EWM). Big Data are information assets …

Saltwater intrusion (SWI) into coastal aquifers is a growing problem for the drinking water
supply of coastal communities worldwide, including for the sustainability of coastal …

Regional rainfall–runoff modeling is an old but still mostly outstanding problem in the
hydrological sciences. The problem currently is that traditional hydrological models degrade …

This paper is derived from a keynote talk given at the Google's 2020 Flood Forecasting
Meets Machine Learning Workshop. Recent experiments applying deep learning to rainfall …

In just the past 5 years, the field of Earth observation has progressed beyond the offerings of
conventional space-agency-based platforms to include a plethora of sensing opportunities …

The diversity in hydrologic models has historically led to great controversy on the correct
approach to process-based hydrologic modeling, with debates centered on the adequacy of …

Hydrological model calibration combining Earth observations and in situ measurements is a
promising solution to overcome the limitations of the traditional streamflow‐only calibration …

Twelve actual evaporation datasets are evaluated for their ability to improve the
performance of the fully distributed mesoscale Hydrologic Model (mHM). The datasets …

Streamflow forecasting over gauged and ungauged basins play a vital role in water
resources planning, especially under the changing climate. Increased availability of large …

This study evaluates the ability of different gridded rainfall datasets to plausibly represent the
spatiotemporal patterns of multiple hydrological processes (ie streamflow, actual …