The Watershed Function SFA is developing a predictive understanding of how mountainous watersheds retain and release water, nutrients, carbon, and metals. In particular, the SFA is developing understanding and tools to measure and predict how droughts, early snowmelt, and other perturbations impact downstream water availability and biogeochemical cycling at episodic to decadal timescales.
Advanced Simulation Capability for Environmental Management (ASCEM) is a software project that aims at developing next-generation, science-based reactive flow and transport simulation capabilities (and supporting modeling toolsets) within a high-performance computing framework, to address the U.S. Department of Energy, Environmental Management’s waste storage and environmental cleanup challenges.
The Next-Generation Ecosystem Experiments (NGEE Arctic) seeks to address challenges by quantifying the physical, chemical, and biological behavior of terrestrial ecosystems in Alaska.
Berkeley Lab’s Predictive Agricultural Initiative, as part of the UC Global Food Initiative launched in late 2014, focuses on mining existing data to understand the impacts of changing climate on California agriculture. For this project, in collaboration with UC Davis, Lab scientists work to develop new scientific approaches to increase food production, while simultaneously decreasing inputs of water and fertilizers.
EESA’s Genomes-to-Watershed and NGEE-Arctic projects seek to take advantage of new scientific software capabilities by incorporating a recently initiated DOE-Advanced Scientific Computing Research (ASCR)–BER-funded project, entitled Interoperable Design of Extreme-Scale Application Software (IDEAS). This project pursues the development and demonstration of new approaches for producing, using, and supporting scientific software. It will establish methodologies and tools that facilitate delivery of software as reusable, interoperable components.
The Small Business Innovation Research (SBIR) project concentrates on the creation of a predictive assimilation framework (PAF) for contaminated sites. This PAF would autonomously assimilate different site-related data streams into numerical models, and provide information on current (and future) site and system behavior to site stakeholders. The technical and scientific capabilities of the PAF are developed and tested by incorporating (into adequate numerical models) a variety of hydrological, geophysical and biogeochemical datasets from a highly instrumented site (the DOE Rifle Subsurface Biogeochemistry Field Observatory in Rifle, Colorado).
LBNL-ESD and the U.S. Army Core of Engineers—Cold Regions Research and Engineering Laboratory (USACE—CRREL) are collaborating to explore the use of distributed fiber-optic sensors to monitor the state of permafrost underlying transportation infrastructure, such as roads, runways, and rail lines.