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ExaSheds: Advancing Watershed System Understanding through Exascale Simulation and Machine Learning3 min read

by Christina Procopiou on July 29, 2019

GC-Future Water
IDEAS cover photo

East River Catchment, Gunnison County, Colorado.

As world population grows, so do concerns that water availability and water quality will continue to diminish. Changes in land use, climate change, and extreme weather exacerbate these concerns, which threaten not only our fresh water supply but also systems that rely on watershed exports, such as hydropower and agriculture.

Although watersheds are recognized as Earth’s key functional unit for assessing water resources, a predictive understanding of how watersheds respond to environmental perturbations is challenging due to the complex nature of the system. What happens along the flowpath of water in a watershed drives complex, multi-scale interactions among the plants, microorganisms, organic matter, fluids, and minerals. These processes, which vary significantly across watershed compartments and gradients, are responsible for biogeochemical cycles that determine downstream exports of carbon, metals and nutrients, such as nitrogen.

While formidable challenges exist in gaining a predictive understanding of watershed hydro-biogeochemical processes across scales and ecosystem compartments, several recent developments now provide a springboard for advancing prediction over scales relevant for resource management, with sufficient resolution, and in a tractable manner. New networked and autonomous sensing systems are providing diverse, streaming datasets associated with a wide range of watershed processes. Toward-exascale computing capabilities are making it easier to simulate multi-scale  hydrology-driven biogeochemical interactions–from bedrock to canopy, along significant lateral gradients, and across terrestrial and aquatic interfaces. And recent machine learning advances hold significant opportunity to transform our ability to rapidly use diverse datasets and physics-based models for rapidly predicting how watersheds respond to perturbations.

The new “ExaSheds” project led by Berkeley Lab PI Carl Steefel represents the first systematic effort to advance powerful machine learning and Exascale computing to transform our ability to predict watershed behavior and increase the use of ever-larger and more-complex data obtained from watershed field observations. In partnership with Oak Ridge National Laboratory, Pacific National Laboratory, and Los Alamos National Laboratory, the new ExaSheds project, which is funded by DOE Biological and Environmental Research, initially will take advantage of datasets being collected at the East River, Colorado watershed site that has been developed as part of Berkeley Lab’s DOE Watershed Function Science Focus Area.

The ExaSheds project is the first-ever systematic effort to advance powerful machine learning and Exascale computing to transform our ability to predict watershed behavior and increase the use of ever-larger and more-complex data obtained from watershed field observations.

Susan Hubbard is Berkeley Lab Associate Laboratory Director for EESA and lead of the Watershed Function SFA project. “The SFA’s vision is to transform watershed science through advanced computing and domain science,” she said. “Ultimately we hope to deliver predictions of how water availability and water quality change with early snowmelt and other perturbations – at unprecedented spatial resolution and mechanistic detail yet up to the river-basin scale.

“The ExaSheds team–which also includes co-PI Scott Painter (ORNL), Dipankar Dwivedi (LBNL), David Moulton (LLNL), Xingyuan Chen (PNNL), Ethan Coon (ORNL), and Ben Brown (LBNL)–will integrate and leverage spectacular watershed hydro-biogeochemistry and computational data science expertise in its effort to transform our ability to predict how watersheds are responding to a rapidly changing environment,” according to Hubbard.

“The ExaSheds project will develop groundbreaking capabilities critical for meeting this goal,” she said. “We expect the developed ExaSheds capabilities to be useful at the East River watershed site as well as to watersheds throughout the world.”

 

 

 

News & Events

Study Sheds Light on Microbial Communities in Earth’s Subsurface2 min read

August 16, 2023

  From the tops of tree canopies to the bottom of groundwater reservoirs, a vast amount of living organisms interact with nonliving components such as rock, water, and soil to shape this area of Earth known as the “critical zone.” Over half of Earth’s microbes are located in the subsurface critical zone, which ranges from…

Carl Steefel Honored in Goldschmidt Session on Reactive Transport2 min read

August 2, 2023

The contributions of Carl Steefel to the reactive transport modeling scientific community were recognized in a session held in his honor at the recent Goldschmidt 2023 conference (Lyon, France). Goldschmidt is the foremost annual, international conference on geochemistry and related subjects, organized by the European Association of Geochemistry and the Geochemical Society. The session was…

DOE Funds Projects to Advance Forest Carbon Dioxide Removal Efforts and Agricultural Soil Carbon Conservation4 min read

August 1, 2023

The DOE Office of Fossil Energy and Carbon Management (FECM) and Office of Technology Transitions (OTT) recently announced $5 million in funding for four projects–two from Berkeley Lab with EESA leadership. The projects selected offer “promising solutions” to the nation’s climate change challenges by helping to reduce greenhouse gas emissions and will “accelerate their deployment…

Quantifying the strength of the land carbon sink3 min read

July 26, 2023

This article first appeared at nature.berkeley.edu/news. The world’s forests, grasslands, and other terrestrial ecosystems have played a substantial role in offsetting human carbon emissions—a capability that UC Berkeley researchers say would be threatened by continued global change. The assessment, published today as a new review paper in Nature Reviews Earth & Environment, presents a comprehensive analysis of…

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