The trifecta of predicting, assimilating, and downscaling streamflow has arrived with generative diffusion AI models.
Stefan Kollet
Editor, Water Resources Research
Posted inEditors' Highlights
Let’s Not Forget About Long Droughts
Why do conceptual hydrologic models struggle to model long-term droughts? A new study investigates.
Posted inEditors' Highlights
Robustness Through Diversity: Learning from Heterogeneous Aquifers
Learning from diverse aquifer structures, which are all over the place, leads to robust inverse methods.
Posted inEditors' Highlights
Episodic Tales of Salt
When episodic pulses of road salt hit after a winter storm, the impact can be like a lightning strike for the environment.
Posted inEditors' Highlights
Beavers are Not Concerned About Groundwater
But, scientists are! A new study illuminates the complex interactions of beaver dam induced ponding and floodplain inundation with shallow groundwater storage and flow patterns.
Posted inEditors' Highlights
Deep Learning Goes Multi-Tasking
In hydrological modeling, predicting multiple tasks helps in identifying physical rules and generalizations.
Posted inEditors' Highlights
Rock Solid Augmentation: AI-Driven Digital Rock Analysis
Boosting digital rock images with AI-powered augmentation and quality analysis could improve subsurface engineering decisions.
Posted inEditors' Highlights
Listening to Groundwater Dynamics
Deep learning from shallow passive seismic data reveals groundwater table depth information in space and time.
Posted inEditors' Highlights
Learning Data Assimilation Without the Help of the Gaussian Assumption
Major Earth system processes are non-linear and non-Gaussian, and so should be our data assimilation approaches.
Posted inEditors' Highlights
Using Big Data for Monitoring Network Design and Beyond
Large data sets can be generated using deep learning to improve the design of observation networks for monitoring subsurface flow and transport.