NVCL - ExaSheds: Advancing Watershed System Science using Machine Learning and Data Intensive Extreme-Scale Simulation

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ExaSheds: Advancing Watershed System Science using Machine Learning and Data Intensive Extreme-Scale Simulation

As the world population grows, so do concerns that water availability and water quality will continue to diminish. With leadership class computers, big data, and machine learning

machine learning (ML)

Machine Learning (ML) refers to the field and practice of using algorithms that are able to “learn” by extracting patterns from a large body of data.

Source: Center of Excellence Source

combined in learning-assisted, physics-based simulation tools, we have an opportunity to fundamentally change how watershed function is understood and predicted in this collaborative project.

Keywords	big data, high performance computing, high-performance computing (HPC) High-performance computing – the most powerful and largest scale computing systems – enables researchers to study systems that would otherwise be impractical, or impossible, to investigate in the real world due to their complexity or the danger they pose Source: DOE Office of Science Source machine learning (ML), water, watershed
TYPE	Project

Keywords

big data, high performance computing, machine learning (ML), water, watershed

TYPE

Project

Principal Investigator (PI)

Carl Steefel

Scott Painter

Lead Institution

Other Collaborators

U.S. Geological Survey University of Texas, Austin

ATS simulation of hydrologic function in the East-Taylor HUC8 sub-basin. The figure shows liquid saturation, with a value of 1.0 corresponding to ponded water. Digital data from the simulation will be used to develop surrogate (ML) models. (Image credit: ExaSheds Project Staff, 2023)

For more information

Carl Steefel
Lawrence Berkeley National Laboratory
Scott Painter
Oak Ridge National Laboratory

https://exasheds.org/about/