Abstract
Wildfire is globally important to climate change and is projected to increase in severity with it. Thus, improving our predictability and understanding of its spatial patterns and impacts on terrestrial vegetation dynamics are greatly needed, as well as our ability to quantify the tradeoffs between wildfire mitigation practices and greenhouse gas emissions. Our capabilities in wildfire modeling span across explicit wildfire modeling with a machine learning approach, modeling vegetation dynamics across multiple ecosystems and climates, and modeling impacts of climate-relevant policy scenarios on wildfire emissions and the terrestrial carbon balance (machine learning model & E3SM, ecosys, and CALAND, respectively). Here we present our recent research activities in these various sub-disciplines of wildfire science to demonstrate a selection of the breadth of research at Berkeley Lab.
Biography
Zelalem A. Mekonnen: https://eesa.lbl.gov/profiles/zelalem-mekonnen/
My primary research interest is terrestrial ecosystem modeling at site, regional and global scales.
I am particularly interested in studying soil-plant-atmosphere interactions to understand ecosystem responses to changes in environmental conditions. My current project include examining the underlying ecosystem processes that control changes in plant functional types and associated feedbacks to the climate system, studying the effects of warming, plant and microbial water stress, and disturbance (e.g. fire) on nutrient cycling and land-atmosphere carbon and energy exchanges under past and future climates.
Qing Zhu: https://eesa.lbl.gov/profiles/qing-zhu/
I am a Research Scientist working on global carbon (C), nitrogen (N), and phosphorus (P) cycles. I have been participating DOE-ACME, Climate-Biogeochemistry feedback SFA, NGEE-Arctic, and NGEE-Tropics projects, being responsible for developing and evaluating new global C-N-P elemental cycles and interaction modules. I also work on benchmark metrics to better inform the model development.
Maegen B. Simmonds: https://eesa.lbl.gov/profiles/maegen-b-simmonds/
Maegen Simmonds is a soil and ecosystem biogeochemist, modeler, and data scientist who works to ensure the long-term sustainability of ecosystems by informing best management practices, decision-making, and policy. Her research focuses on terrestrial ecosystem processes, including land-atmosphere exchange of greenhouse gases (CO2, CH4, N2O). Predicting the interactive effects of climate, wildfire, soil and plant properties, and land management have been central themes in her work. Currently her research focuses on developing the California Natural and Working Lands Carbon and Greenhouse Gas Model (CALAND), which simulates the net changes in California’s land carbon stocks under various suites of land use and land management interventions interacting with environmental changes (urbanization, wildfire, climate change). The primary goal of this project is to inform the State of California’s land management goals and greenhouse gas reduction targets in California’s 2030 Natural and Working Lands Climate Change Implementation Plan. She will expand on this work through a recently awarded multi-institutional grant from the California Strategic Growth Council. Her role in this project involves developing models to scale up experimental field trial data on changes in soil carbon in agricultural systems due to additions of biochar, compost, and rock across temporal and spatial scales in California.
Dr. Simmonds studied sustainable development (B.A.) at The Pennsylvania State University before receiving her doctorate in soils and biogeochemistry from the University of California, Davis, in 2014. She spent two years at Stanford University as a postdoctoral scholar, where she designed an experiment inspired by her curiosity in mechanisms of carbon stabilization in soil organic matter, particularly the role of oxygen limitation in soil microbial respiration. Specifically, she tested the temperature sensitivity of anaerobic microbial respiration and its dependence on crop residue inputs in a greenhouse soil incubation experiment. For her dissertation she examined the potential for precision management in rice cropping systems through on-farm field studies and using machine learning techniques to explain the underlying causes of spatiotemporal variability of rice yield. She also conducted field experiments and used process-based modeling to explore crop, soil, and water management effects on methane and nitrous oxide emissions from rice soils. Later her model calibration and validation work directly supported the quantification of statewide methane emissions from rice cultivation in the California Air Resources Board greenhouse gas inventory. In addition to research, she has a passion for teaching and developing curriculum and software for a range of subjects including general soil science, soil nutrient management, climate change mitigation, experimental design, and statistics.