EESA Research Projects Summer 2023

Students will learn how to detect and measure greenhouse gases, and to identify their sources, using state-of-the-art and custom-built gas analyzing equipment.  Students will learn about how local sources of greenhouse gas emissions contribute to climate feedbacks in the Earth’s atmosphere and how scientists quantify these sources and their effects.  Project topics will touch on physics, biology, instrumentation, and data analysis and visualization. 

Desired Education Level: Community college, Undergraduate student

Relevant Internship Programs: CCI, BLUR, SULI

Mentor: Andrew Moyes (abmoyes@lbl.gov) 

Water quantity and quality in mountainous watersheds and Arctic permafrost systems are heavily impacted by the spatial and temporal variability in snowpack dynamics and how they are modulated by landscape properties. Improving our understanding of how landscape properties impact surface/subsurface hydrology is critical to improve the assessment of how ecosystems respond to climate change. The candidate will help evaluate the impact of solar radiation and snowpack dynamics on surface and subsurface hydrology across two watersheds (in Colorado and Alaska) using a dataset obtained with a dense network of temperature sensors that provide unprecedented spatial resolution. 

Desired Education Level: Community college, Undergraduate, Masters, PhD student

Relevant Internship Programs: CCI, BLUR, SULI

Mentor: Baptiste Dafflon (bdafflon@lbl.gov) 

Disturbances, land-use change and their effects on the carbon cycle, are critical features of the Earth’s carbon budget and therefore on global climate change. It is now imperative that accurately represent different types of land-use change in global land models is essential for model predictions. This project will gather, synthesis, and analysis large observational datasets such as plant age distribution, mortality, plant community composition, and plant traits related to water stress in response to disturbances such as wildfire and drought dieback. These observational datasets will then be used to validate the model outputs or predictions.  This student project can focus on gathering observational datasets from a specific region, biome, or global plant trait datasets.  

Desired Education Level: Community college, Undergraduate, Masters, PhD student

Relevant Internship Programs: BLUR, SULI, DOE Science Graduate Student Research (SCGSR)

Mentors: Jennifer Holm (jaholm@lbl.gov) 

Models that simulate and predict future feedbacks between vegetation and climate are essential for understanding the global carbon budget and informing policy on reaching net zero. This project will help to improve the representation of global plant distributions in the vegetation model FATES by tuning model parameters related to plant leaf deciduousness. The intern will use literature and plant trait databases to generate parameter values to test, and will compare model simulation results against satellite data of global plant distributions in order to assess parameter values. 

   This project is a great opportunity for someone interested in climate science and forest ecology  to learn about land surface modeling and to develop skills in python and jupyter notebooks.  

Desired Education Level: Community college, Undergraduate, Masters, PhD student

Relevant Internship Programs: CCI, BLUR, SULI, GEM, DOE Science Graduate Student Research (SCGSR)

Mentors: Jessica Needham (jfneedham@lbl.gov)  

Gridded data products are used in place of direct observations for understanding physical processes because they are complete and spatially and temporally continuous. However, when the goal is to characterize extreme wind speeds, inference that relies on gridded products may be misleading due to the fact that traditional gridded products over-smooth large measurements. This unexpected bias in gridded products is potentially catastrophic, since extreme winds play a large role in the onset and spread of wildfires across the western United States. The goal of this project is to produce a gridded data set of wind extremes over California (for starters) and the (eventually) entire western United States using statistical (Gaussian processes) and machine learning (physics-informed neural network) methods that retain the observed statistics of extreme wind speeds while resolving these statistics at a high spatial resolution. The intern will gain experience working with large spatio-temporal data, statistical programming, extreme value analysis, and uncertainty quantification.

Desired Education Level: PhD student

Relevant Internship Programs: BLUR, SULI, DOE Science Graduate Student Research (SCGSR)

Mentors: Joshua North (jsnorth@lbl.gov)   

Tree respiration is predicted to be 10X higher than anthropogenic CO2 emisisons globally, but limited field data exist on tree respiration rates and the influence of environmental and biological variables. Due to the lack of commercial systems for stem respiration, at the UC Berkeley greenhouse real-time volatile metabolomics laboratory, we are developing a new Real-Time stem CO2 efflux and O2 influx system and automate it to take continuous readings from up to 6 different stem enclosures sequentially to collect diurnal information on stem respiration. Thus, the project is to develop a new system to collect real-time oxygen and carbon dioxide flux measurements, to define the respiratory quotient (flux of CO2/O2) continuously from tree stems for the first time.  

Desired Education Level: Visiting Faculty, Community college student, Undergraduate student, Masters student, PhD student

Relevant Internship Programs: VFP, CCI, BLUR, SULI, DOE Science Graduate Student Research (SCGSR)

Mentors: Kolby Jardine (kjjardine@lbl.gov)    

Plants emit methanol into the atmosphere at high rates as a function of growth rates and cell wall expansion with methanol production and emissions directly involved in tissue morphogenesis including leaf growth and development via changes in cell wall elasticity. Recently, we discovered that drought stress causes methanol emissions to dramatically decline while diurnal studies under well watered soils suggest reduced leaf water availability in the afternoon relative to the predawn suppresses growth processes and methanol emissions. Thus methanol emissions may provide a new chemical signal for diurnal growth processes difficult to assess by physical means. To test these hypotheses, the visiting researcher will collect the first dataset on 1) Leaf expansion rates, 2) Methanol emissions, 3) Leaf water potential, and 4) Bulk cell wall elasticity determined by P-V curves. This will be done on experimental plants in the UC Berkeley greenhouse with variable soil moisture additions as well as field plants under high temperature atmospheric drought in the summer, but well watered soils.  

Desired Education Level: Visiting Faculty, Masters student, PhD student

Relevant Internship Programs: VFP, BLUR, SULI, DOE Science Graduate Student Research (SCGSR)

Mentors: Kolby Jardine (kjjardine@lbl.gov)    

As climate changes and more people live and work near wilderness areas, most forests are experiencing higher levels of disturbances. These disturbances change the structure and species composition of forests, and can also impact the way these forests exchange heat, water and carbon with the atmosphere. In this internship, you will use multiple remote sensing data sets collected in California to investigate how fires change the forest canopy height and openness, and to study how long it takes for burned forests to recover similar surface temperature and evaporation observed in forests that did not burn. You will learn how to explore multiple remote sensing data sets (airborne lidar, GEDI and ECOSTRESS), and how to use object-oriented programming (R) and geographic information system (QGIS) to analyse and visualise data.  

Desired Education Level: Community college student, Undergraduate student

Relevant Internship Programs: CCI, BLUR, SULI

Mentors: Marcos Longo (mlongo@lbl.gov)      

Understanding how rapidly water flows through the soil is crucial to infer transport of contamination, groundwater recharge and plant water availability. This project aims to derive where and when newly infiltrating water (i.e., snowmelt or rainfall) bypasses previously stored water (= preferential flow). We will use high-frequency soil moisture time series to identify the occurrences of preferential flow.  

Desired Education Level: Visiting Faculty, Undergraduate student, Masters student, PhD student

Relevant Internship Programs: VFP, BLUR, SULI, DOE Science Graduate Student Research (SCGSR)

Mentors: Matthias Sprenger (msprenger@lbl.gov)  

Approximately 70% of microbes that grow in terrestrial subsurfaces cannot be cultured in the lab due to restrictions on carbon sources, and many of them only grow on solid phase substrates. Therefore, enrichment strategies using solid particles amended with diverse mineral and complex carbon sources can effectively facilitate the cultivation of subsurface microbial communities. In this study, mineral-coated sand particles and necromass (i.e. non-living microbial biomass) will be used to enrich and isolate diverse microbes from subsurface sediment samples collected from Oak Ridge, Tennessee. During this project, we will inoculate sediment-derived microbes with different mineral-coated sand particles and necromass media, isolate microbes from different experimental settings, and determine their taxonomy. The mineral-coated sand is expected to have more diverse isolates than pure sand, and the sand with different minerals is expected to have distinctive isolates. 

Desired Education Level: Community college student, Undergraduate student

Relevant Internship Programs: CCI, BLUR, SULI

Mentors:  Romy Chakraborty (rchakraborty@lbl.gov) and Mingfei Chen (mingfeichen@lbl.gov)     

Methane (CH4) has been the second most important contributor to post-industrial global warming after carbon dioxide (CO2). However, the complex nature of wetland CH4 processes makes it challenging to accurately model and predict CH4 emissions. This proposed work will leverage on cutting-edge machine learning models to improve the wetland methane predictability.

Desired Education Level: High school student

Relevant Internship Programs: Experiences in Research program

Mentors: Qing Zhu (qzhu@lbl.gov)    

The purpose is study is to determine the changes of Amazon rainforest using remote sensing data to determine where are the most dynamics forests, and to determine the type of disturbances associated with those changes. Google Earth Engine will be the prefered platform to develop this study.

Desired Education Level: Visiting Faculty member, Undergraduate student, Masters student, PhD student

Relevant Internship Programs: VFP, BLUR, SULI, DOE Science Graduate Student Research (SCGSR)

Mentors: Robinson Negron-Juarez (robinson.inj@lbl.gov) 

In recent years, increases in temperatures, extreme rainfall, and droughts have resulted in catastrophic impacts around the globe. Physical science tells us that human-caused increases in greenhouse gas concentrations are to some extent responsible, however, it is important to understand the extent to which observed changes can be attributed to human activities as opposed to the natural variability of the climate system. This project aims to use statistical and machine learning frameworks to quantify the causal relationship between greenhouse gas concentrations and observed changes in temperature and rainfall distributions. The end result will be a visualization tool to help the general public understand how human activities are impacting their local climate. The intern will gain experience with statistical modeling, analysis of spatio-temporal data, causal inference, and data visualization.

Desired Education Level: Masters student, PhD student

Relevant Internship Programs: BLUR, SULI, DOE Science Graduate Student Research (SCGSR)

Mentors: Samuel Baugh (samuelbaugh@lbl.gov)  

Recent technological advances have enabled low-power, low-cost, short-range radar sensors with 5G connectivity. Such sensors are usually designed for automotive applications, but we are building a solution to monitor environmental parameters in mountainous watersheds. Research tasks for this project include low-power electrical design, as well as the development of AI enabled radar processing algorithms for precipitation, snowpack, and vegetation observations.

Desired Education Level: Visiting Faculty, Undergraduate student, Masters student, PhD student 

Relevant Internship Programs: VFP, BLUR, SULI, DOE Science Graduate Student Research (SCGSR)

Mentors: Stijn Wielandt (stijnwielandt@lbl.gov)   

Soil quality, or what is increasingly referred to as “soil health”, plays a critical role in the ability of soil to act as carbon sink and reduce climate change. Despite knowledge and evidence of the importance of soil health, agricultural studies remain largely focused on regulating irrigation practices, and frequently ignore that soils play an equally important role in carbon management. In addition, irrigation technologies rarely consider long-term impacts of irrigation on carbon emissions (e.g., impacts of salt build-up or erosion on decreased carbon storage). To address this research gap, this project will determine the short- and long-term effects of major cropping systems and irrigation methods on underlying, fundamental ecosystem and biogeochemical processes controlling soil carbon emissions. The primary focus of this research will be to determine the conditions and treatments (e.g., rock amendments, biochar) which will lead to negative carbon emissions from representative agricultural soils. 

Desired Education Level: Visiting Faculty, Undergraduate student, Masters student, PhD student

Relevant Internship Programs: VFP, BLUR, SULI, MLEF, DOE Science Graduate Student Research (SCGSR), UC Global Food Initiative, Cal Energy Corps

Mentor: Bhavna Arora (barora@lbl.gov) 

Geological repository for high level radioactive waste is a multi-barrier system. The interaction between two barrier materials: bentonite and cement, has critical effect on the long term safety of the repository. The goal of this summer internship project is to use numerical models to simulate the long-term interaction between bentonite and cements. The student will learn the geochemistry of bentonite and cement, the numerical code that simulate geochemical reactions, and conduct simulations that is part of an international collaboration project. The model results will be document in reports and presentation for an international meeting in the fall of 2023. 

Desired Education Level: Visiting Faculty, Undergraduate student, Masters student, Ph.D. student

Relevant Internship Programs: VFP, Ingenuity program

Mentor: Liange Zheng (lzheng@lbl.gov) 

Shearing of fractures is an important process in Earth systems. Shearing of fractures may lead to permeability increase within the fractures—which can be desirable for subsurface energy recovery. However, the permeability increases or even induced seismicity caused by shearing of fractures needs to be managed in a nuclear waste repository or in other subsurface storage systems. In this project, we look for skilled students to carry out 3D numerical simulations of shearing of discrete fractures with different types of geometry using 3DEC, PFC or other software. Detailed technical goals will be set based on the background and interest of the successful candidate.

Desired Education Level: Ph.D. student

Relevant Internship Programs: Ingenuity program

Mentors: Mengsu Hu (mengsuhu@lbl.gov) and Tsubasa Sasaki (TsubasaSasaki@lbl.gov) 

Gas flow through the Earth is critically important in geological processes, such as volcanism, as well in subsurface engineering activities, such as nuclear waste disposal, carbon sequestration and hydrogen storage. Subsurface gas migration involves complex processes, including multiphase fluid flow with gas breakthrough, phase change expansion, pressure buildup, as well as potential gas fracturing with rapid gas release that could potentially be catastrophic. In this project, we look for a skilled student to conduct numerical simulations of subsurface gas flow and fracturing in low permeability geological media, such a clay host rocks, faults and fractures. A detailed program will be designed together with the candidate depending on the candidate’s background and desire.  

Desired Education Level: Ph.D. student

Relevant Internship Programs: Ingenuity program

Mentors: Jonny Rutqvist (jrutqvist@lbl.gov)

Water participates in nearly all near-surface processes and has a controlling effect on many of them, including the binding and transport of charged species. The properties of water at interfaces remain poorly constrained because water has not been directly seen with the molecular detail needed to predict its many possible interfacial structures. Recent simulations suggest that exotic phases of water exist at interfaces, some of which conduct charge better than the best battery materials. Here, we explore the interfacial structure of water in 3D with atomic resolution using cryo-electron tomography and build atomic models of interfacial water structure that test recent proposals. The project will build expertise in computer vision and machine learning techniques for 3D image quantification leading to a new understanding of one of the most important substances in our lives.

Desired Education Level: Visiting Faculty, Community college student, Undergraduate student, Masters or PhD student 

Relevant Internship Programs: VFP, Ingenuity program

Mentor: Michael Whittaker  (mwhittaker@lbl.gov)

Small earthquakes are not public concern but contain detailed information of subsurface stress condition. Recent development of machine learning technologies for seismology can provide crucial improvement of the detectability of small earthquakes from seismic data. In this project, we can work together to use and modify existing machine learning tools for detection. The student can learn general earthquake and elastodynamic physics, programming skill for seismological machine learning packages.

Desired Education Level: Undergraduate student, Masters or PhD student 

Relevant Internship Programs: BLUR, SULI, MLEF, DOE Science Graduate Student Research (SCGSR)

Mentor: Nori Nakata (nnakata@lbl.gov)  

Enhanced weathering is an approach being investigated to remove carbon dioxide from the atmosphere by speeding up the natural process of rock weathering, thereby combating climate change. Interns will participate in an enhanced weathering experiment with rock additions to soil being conducted in large pots at Lawrence Berkeley National Lab. Students will assist with water, soil, and gas sampling and analysis, gaining hands-on experience in environmental geochemistry research, learning a range of analytical techniques, and working closely with earth scientists. 

Desired Education Level: Community College student, Undergraduate student

Relevant Internship Programs: CCI, BLUR, SULI

Mentor: Patricia Fox (pmfox@lbl.gov)  

Carbonate minerals nucleation in natural environments is often a complex and multistep process challenging to follow experimentally. Here, we used molecular modeling to reveal the mechanism of the first stages in carbonate mineral nucleation, including the spontaneous formation of ion pairs and pre-nucleation clusters. In the next step, the nucleation energetic and kinetic characteristics obtained from simulation efforts will be used to parametrize the larges scale models: mineral nucleation and growth, kinetic rate laws, and coarse-grained stochastic or continuum models. Participation in this project will allow you to learn the basics of molecular modeling, gain practical skills in setting up and running molecular simulations, and analyze simulation data using open-source simulation codes. You will also be exposed to programming in Python and high-performance computing.

Desired Education Level: Visiting Faculty, Community College student, Undergraduate student, Masters or PhD student

Relevant Internship Programs: VFP, BLUR, SULI, GEM, MLEF, Ingenuity program

Mentor: Piotr Zarzycki (ppzarzycki@lbl.gov) 

Geophysical monitoring of thermo-hydro-mechanical (THM) processes of disposed heat generating nuclear waste because the THM processes impact both the short- and long-term fate of nuclear waste. Geophysical monitoring, such as electrical resistivity and distributed sensing, is a critical tool in understanding such THM processes with high spatial and temporal scales. The intern will work with the PI and the project team to conduct geophysical monitoring of these importance processes. Through this opportunity, the intern will gain an excellent understanding of the key processes impacting to disposal of nuclear waste, and how geophysical tools can be used to understand these processes.

Desired Education Level: Visiting Faculty, Community College student, Undergraduate student, Masters or PhD student

Relevant Internship Programs: VFP, Ingenuity program

Mentor: Yuxin Wu  (ywu3@lbl.gov)  

The applicant will be involved in the analyses of fault geological, mechanical and hydraulic data related to experiments of fault reactivation conducted in the USA (San Andreas Fault) or in Switzerland. Depending on the project's progress, the applicant may be involved either in field work, signal preprocessing or numerical modeling. All types of skills are welcome from geologists, geophysicists, hydrogeologists, rock mechanics and modeling. Please contact Yves Guglielmi for a better design of the project in relation with your skills.

Desired Education Level: Visiting Faculty, Masters or PhD student

Relevant Internship Programs: VFP, BLUR, SULI, MLEF, DOE Science Graduate Student Research (SCGSR)

Mentor: Yves Guglielmi (yguglielmi@lbl.gov)