Stanford University
SESUR student group

Projects from Summer 2020

Projects from Summer 2020

Investigating the complex social-ecological system of invertebrate fisheries in Palau

SESUR
Category(s): Food and Agriculture, Human Dimensions and Sustainability, Ocean
Department: Emmett Interdisciplinary Program in Environment & Resources

Faculty: William Durham, Fiorenza Micheli
Grad Student: Caroline Ferguson

Women represent nearly half of all seafood workers worldwide, yet their contributions to the sector have been largely ignored by scholars and policy-makers. In Palau, women dominate invertebrate fisheries including sea cucumbers, a culturally important resource in Palau and among the most valuable seafood commodities in the world. Sea cucumbers are dried and sold in China for astronomical profits, but fishers are rarely paid fair prices and the industrial scale of fishing demolishes local sea cucumber populations. Our study in many-faceted: (1) understand how gender and other identities shape fishers' access to and benefits from sea cucumber fisheries; (2) participatory monitoring of sea cucumber populations; (3) community-based aquaculture of sea cucumbers; (4) understand the cultural role of invertebrate fisheries in Palau; and (5) support fisherwomen's cooperatives through market surveys. We have gathered qualitative (interviews, focus groups) and quantitative (questionnaires, ecological surveys, market surveys) data to understand the complex social-ecological system of invertebrate fisheries in Palau.

We are seeking a student with interest in the social and natural sciences to support this project. Experience with statistics and/or NVivo is preferred but not required. The student will work closely with graduate student Caroline Ferguson on campus and/or remotely.

Skills/Interest/Background: Statistics

Heavy metal, rainfall and groundwater: What are the connections?

SESUR, SURGE
Category(s): Climate Change, Freshwater, Natural Hazards
Department: Earth System Science
Faculty: Scott Fendorf

Postdoc: Maya Engel

Heavy metals are frequently detected in groundwaters worldwide. Whether they occur naturally or are a result of anthropogenic activity, heavy metal persistence in soil and water systems poses a major threat to the quality and safety of the groundwaters we rely on for drinking and irrigation purposes. Our changing climate may further exacerbate metal threats to our water systems. Of particular interest are climate change driven fluctuations in rainfall patterns that will result in extreme rainfall events and prolonged droughts. These will significantly influence groundwater table levels and soil redox conditions that largely control heavy metal partitioning and transport.

Our project will examine the impact of variations in magnitude and frequency of oscillating water tables on the availability of heavy metals in soils and near-surface sediments. Specifically, we will investigate the impact of 1) increase in extreme rainfall events and 2) decrease in overall rainfall on the mobility of heavy metals in a simulated alluvial aquifer.

As a summer intern, you will first assist in setting up the column systems that imitate an alluvial aquifer. Thereafter, you will on a weekly basis sample, prepare and analyze aqueous samples from the columns. During the internship you will learn basic lab skills as well as how to use a suite of analytic instruments that will be repeated every week (allowing you to master the skills and become confident with the work).

This is a great opportunity for students interested in basic environmental/analytical chemistry.

Skills/Interest/Background: Chemistry, Lab Work

Measuring the rotation rate of Jupiter's moon Europa

SESUR, SURGE
Category(s): Dynamic Earth, Planetary Science
Department: Geophysics

Postdoc: Gregor Steinbruegge

Jupiter's moon Europa is a fascinating icy world with a subsurface water ocean. During the past decades many spacecraft flew by in the proximity of the moon, while cruising inside the Jovian System or while on their way to targets further out in the Solar System. Nonetheless, not much is known about this distant world. Since Europa is tidally locked to Jupiter, the moon is in a 1:1 spin-orbit resonance, however the rotation rate can be affected by multiple perturbations leading to physical librations or a long-term drift of the ice shell.

The aim of this project is to study images taken by multiple spacecraft over the past decades and to identify local or regional geologic features (craters, ridges, bands) that are visible in multiple images. These points can then be marked as geodetic control points. Since the images were taken multiple years or decades apart from each other, comparing the position of these control points allows to determine the long-term average rotation rate of the satellite.

We are looking for a student motivated to spend the summer studying images from this distant world and with the patience and dedication to identify individual features that can be used for the rotation measurement. Any previous experience with the NASA Ames Stereo pipeline, basic knowledge in image processing, or basic programming skills are desirable but not required. The student can acquire all necessary skills during the program.

Skills/Interest/Background: Engineering, Geology, Physics

Radar System Development for High Altitude Ice Sheet Sounding

SESUR, SURGE
Category(s): Climate Change, Dynamic Earth, Natural Hazards
Department: Geophysics

Grad Student: Riley Culberg

Future mass loss from Antarctica and Greenland has the potential to contribute significantly to global sea level rise, but constraining those contributions requires understanding the conditions and dynamic processes occurring inside or underneath ice more than 2 miles thick. Airborne ice penetrating radar is a powerful geophysical tool for observing the subsurface conditions of ice sheets over large regions, but given the vast size of these continents, data coverage is still very sparse. A satellite-based radar sounder, similar to those instruments operating at Mars, would offer the unprecedented spatial and temporal coverage needed to address many questions in glaciology. However, some theoretical radar models predict that successful imaging of the terrestrial subsurface from space will be extremely difficult due to large spreading losses and frequency-dependent interference from surface or near-surface clutter. Our group is developing a miniature radar system on a high-altitude weather balloon in order to directly test how imaging capability may scale with system center frequency and altitude. Ultimately, this system will help us assess the feasibility of satellite-based radar sounding and inform the system design choices for future high-altitude instruments.

We are looking for a motivated student to develop C++ code to control radar system operations such as transmission, reception, and data storage on our software defined radio platform. The student will also participate in lab and ground tests of the system and perform some basic processing of test data in MATLAB. Previous experience with C++, Linux terminal commands, MATLAB, and basic digital signal processing is desired. We recommend that prospective students have taken CS106B/X and EE102A/B or have equivalent experience.

Skills/Interest/Background: Computer Programming, Engineering, Physics, Scientific Programming

Linking food, water and energy in the rapidly urbanizing city of Pune in India

SESUR, SURGE
Category(s): Climate Change, Dynamic Earth, Energy, Food and Agriculture, Freshwater, Human Dimensions and Sustainability
Department: Earth System Science

Postdoc: Anjuli Jain Figueroa

Description: What is the nexus? As cities experience rapid economic growth, they often demand more resources like food, water and energy.  Urbanization transforms the neighboring lands, often displacing agricultural lands, and thus make it necessary to produce more food on less land to satisfy the growing and changing urban needs. What's more, how we use our land dictates when and how much water reaches rivers and aquifers. However, the amount of water and when it's available influence how much a city or crop can grow. These type of linked feedback loops are the core of the food-water-energy nexus and are crucial to understanding open questions like: How will the water cycle respond to changes in land use? How will cities adapt to changes in water availability?

This project brings together the natural and human systems by creating a model to understand the links in the food-water-energy nexus. The interdisciplinary research project also simulates scenarios and interventions like climate change and adaptation strategies to help inform decision makers on viable sustainable development pathways. The project is regionally focused on one of the fastest growing Metro regions in India, Pune, with a population exceeding 7 million and 3.5% annual population growth rate.

We seek 1 or 2 motivated students interested in the urban food-water-energy nexus.  The student(s) should be motivated, organized and interested in interdisciplinary teamwork as we work with a large team spread across the world. The student(s) will work together with our team to 1) identify nexus links, 2) analyze data 3) run the model and 4) visualize results. Previous experience in data analysis, GIS, statistics and/or some computer programming (i.e., Python) is preferred but not required.

Work will be computer-based. The project is appropriate for freshmen through seniors as the student can acquire all necessary skills during the program. The project can be an 8-week, or a 10-week project and can be tailored to the students research interests.

Skills/Interest/Background: Computer Programming, Machine Learning, Mathematics, Scientific Programming, Statistics

Mapping historical radar observations from Antarctica

SESUR, SURGE
Category(s): Climate Change
Department: Geophysics

Grad Student: Mickey MacKie

With the ability to cover large areas and penetrate through 4 km of ice, radar is a valuable tool for investigating glaciers. Most radar surveys have been flown within the last 20 years so temporal comparisons of subglacial conditions have not been made. However, between 1967 and 1979, 400,000 km of airborne radar surveys were taken of Antarctica, making this the largest survey of Antarctica ever flown. These data were originally recorded on 35mm film. They were recently digitized, which enables us to compare this survey to modern radar campaigns in order to study how the ice sheet has changed over the last 50 years. However, the data was collected before the advent of GPS and was positioned using an inertial navigation system, so the locations could be off by several kilometers. We need to improve the positioning in order to compare these data to modern data.

We are seeking a student who is interested in improving the positioning of radar data from high-interest regions. This will entail searching through field notebooks to gather information on the flight paths and using aerospace navigation techniques to better approximate the aircraft position. This project will require basic programming skills and attention to detail.

Skills/Interest/Background: Computer Programming, Machine Learning, Scientific Programming

Investigating protective health decision-making in response to wildfire smoke in California

SESUR, SURGE
Category(s): Climate Change, Human Dimensions and Sustainability, Natural Hazards
Department: Earth System Science, Department: Emmett Interdisciplinary Program in Environment & Resources

Grad Student: Francisca Santana

Wildfires are expected to be more frequent and intense in the future due to climate change. In addition to the direct impacts from wildfires, exposure to wildfire smoke poses severe health risks. The risks are especially acute for vulnerable groups such as children, the elderly, and individuals with chronic health issues. Despite these increasing risks, the pathways involved in individual response to wildfire smoke are not well-understood. What are the decision-making processes and protective health trajectories related to exposure to wildfire smoke? Are the differences in these individual processes and trajectories due to social vulnerability, social support, and chronic exposure to poor air quality? This project seeks to address these motivating questions by using approaches and methods from decision science, social psychology, and sociology. Through interviews, an app-based survey, and individualized air quality monitoring, we hope to generate theories and test hypotheses related to how individuals make decisions in response to wildfire smoke events.

We are looking for a student to work with our interdisciplinary team to support the ongoing management and analysis of a pilot of the app-based survey and the development of the second phase of the survey to be deployed in the fall of 2020. Previous experience with (or the desire to learn) survey research, Qualtrics, and/or statistical analysis in R/Stata would be helpful. The work will primarily occur on campus, but there may be opportunities to travel to field sites in northern California (e.g. Sacramento and Fresno). The student will meet regularly throughout the summer with lead mentors and will have opportunities to collaborate with other post-docs and graduate student lab members of the Wong-Parodi Lab. This is an excellent opportunity for a student interested in human behavior and the public health impacts of climate change.

Skills/Interest/Background: Scientific Programming, Statistics

Understanding nitrous oxide production in the tropical Pacific Ocean with isotope measurements

SESUR, SURGE
Category(s): Climate Change, Dynamic Earth, Ocean
Department: Earth System Science

Grad Student:  Colette L. Kelly

When we talk about greenhouse gases, we usually think carbon dioxide — but of course there are other greenhouse gases, such as methane and nitrous oxide. In particular, each molecule of nitrous oxide emitted to the atmosphere has the greenhouse gas potential of 265 molecules of carbon dioxide; if carbon dioxide were the currency of climate change, then nitrous oxide would be the $300 bill. But the mechanisms and rates by which nitrous oxide is produced in the ocean remain poorly constrained, especially in regions of the ocean with little to no oxygen.

In this project, we will use a novel technique to better understand how ammonia-oxidizing archaea — one of the most abundant organisms in the ocean — generate nitrous oxide in one of these oxygen deficient zones (the eastern tropical North Pacific). Previous work in our lab indicates that, where oxygen is low but non-zero, archaea may be responsible for three quarters of total nitrous oxide production in this part of the ocean. This summer project will expand upon this work by applying site-specific isotopes to traditional rate measurements, to provide insight into the potential pathways by which archaea produce nitrous oxide from different substrates.

This project involves chemical analyses, data analysis, and modeling. The student will analyze experimental samples on an isotope ratio mass spectrometer and learn how to analyze and interpret data for the site-specific isotopes of nitrous oxide. Time permitting, the student will also have the opportunity to learn or develop computational skills in Matlab and/or Python. Lab experience in chemistry or biology at the 300 level (or institution’s equivalent) is preferred; no programming experience is required.

Skills/Interest/Background: Biology, Chemistry, Computer Programming, Scientific Programming, Statistics

Who makes nitrite in the Primary Nitrite Maximum? Investigating nitrite cycling in the upper ocean using stable isotopes.

SESUR, SURGE
Category(s): Ocean
Department: Earth System Science

Grad Student: Nicole Travis

There is long-standing debate about whether nitrifying microbes or photosynthesizing phytoplankton produce the nitrite that is found in many regions of the surface ocean. Our goal will be to provide an isotopic tool for identifying phytoplankton-based nitrite, and begin to determine environmental conditions where phytoplankton may dominate nitrite cycling. In order to do this we need to understand what 'isotopic fingerprint' is left behind when a specific microbial group transforms nitrogen during their cellular growth or energy acquisition. In this project we will characterize the 'isotopic fingerprint' of nitrite that is produced or consumed by phytoplankton using laboratory cultures. We will calculate isotope effects for these nitrogen transformations and begin to apply our findings to a dataset of oceanic nitrite isotope values.

Student should have an interest in oceanography/chemistry and basic laboratory experience (eg. using pipettes and microbalances, handling glassware, taking excellent lab notes). Skills you will learn include: phytoplankton culturing and sterile techniques, making growth media, measuring dissolved nitrogen species (NO3, NO2, NH4), preparing samples for isotope measurement on an isotope ratio mass spectrometer, calculating isotope effects and interpreting marine nitrogen isotope data. We are a marine biogeochemistry group, and will touch on a lot of topics to understand ocean nitrogen cycling. Don't worry, we will teach you what an isotope effect is! 
Skills/Interest/Background: Biology, Chemistry, Lab Work

Erosion and stream formation in desert landscapes

SESUR, SURGE
Category(s): Dynamic Earth, Evolution of Earth
Department: Geological Sciences
Faculty: George Hilley

Grad Student: Aaron Steelquist

Understanding the fundamentals of how landscapes form and evolve over time is central to our ability to interpret Earth’s history as well as predict the effects of a changing climate on land surface. The processes of erosion which create small river channels in soil-rich landscapes have thus far failed to fully explain how drainages form on both Earth and Mars. This project seeks to answer some of the challenges in understanding our planet by using a geologically unique area of southeastern Utah to test how gullies form in bedrock landscapes.

We have used a quadcopter drone to collect thousands of images of emerging drainages on Raplee Ridge monocline. Using modern image-processing tools we can create detailed 3D maps of the drainages which can reveal details about the initiation and expansion of these gullies over time. We are seeking a motivated student to work with our group making measurements on these 3D maps to better characterize the channel morphometries, distribution of blocks within channels, and potential for accumulating water in high-rain periods. This project has significant flexibility in focus and type of measurement depending on student interest and experience. Familiarity with programming (particularly in Python) may be useful for a more extensive project, however programming skills are not required. Background knowledge in basic geology is desirable. Students will work directly with PhD candidate Aaron Steelquist.

Skills/Interest/Background: Computer Programming, Field Work, Geology

Do bacterial symbionts contribute to sterol biosynthesis in marine invertebrates?

SESUR, SURGE
Category(s): Evolution of Life
Department: Earth System Science

Grad Student: Malory Brown

In this project, students will use molecular biology techniques to determine if bacteria associated with sea sponges and/or corals have the genetic capacity to produce geologically relevant sterols. Sterols are lipid molecules that can be preserved in the geologic record for billions of years as sterane biomarkers. Certain unusual steranes found in the rock record are used as biomarkers for ancient sea sponges and may represent the first evidence for animal life on Earth. However, a robust biomarker interpretation relies on a complete understanding of its biosynthesis in modern organisms, yet key enzymes necessary for the biosynthesis of these sponge steranes have not been identified. Further, microbial symbionts can constitute up to 50% of sponge biomass, and studies have shown that bacterial symbionts likely contribute to lipid biosynthesis in the sponge holobiont.

We have identified several gene candidates that may be responsible for the production of these unusual biomarker sterols in sponge metagenomes, suggesting bacterial symbionts may be involved in their biosynthesis. Interestingly, we found additional gene candidates in coral metagenomes suggesting bacteria may also help corals produce sterols related to those found in sponges.  To test these hypotheses, students will express these gene candidates in an E. coli host to determine their functionality.

This project will teach students how to grow bacteria in the lab, how we can determine the function of genes from uncultured microbes, how to extract lipids from cells, and how to utilize gas chromatography-mass spectrometry to detect sterols. This is an excellent opportunity for STEM students from diverse fields to experience the interdisciplinary nature of geobiology research. Prior experience in Earth science is not required.  Coursework in microbiology and organic chemistry would be helpful but are not necessary.

Skills/Interest/Background: Biology, Chemistry, Lab Work

Microscale investigation of bubble dynamics in porous media

SESUR
Category(s): Climate Change, Energy
Department: Energy Resources Engineering

Grad Student: Negar Nazari

Migration of gas bubbles in porous media has important applications for geologic carbon sequestration, petroleum industry, and environmental applications. Ebullition of methane and carbon dioxide gas bubbles from sediments affects biogeochemical processes and increases the emission of greenhouse gases into the atmosphere. Pressure drop and thin film fluid dynamics are crucial parameters in understanding such bubble motion in porous media. This project studies the motion and the fluid dynamics of long bubbles using microfluidic devices (micromodels) and aims to develop a quantitative model to explain bubble movement. The study makes significant use of image processing and statistical analysis of the experimental data.

This research project involves extensive data collection, processing, and analysis. Students will conduct microscale experiments in the lab, collect the data, and use statistical techniques and image processing to analyze the results and develop significant conclusions from the collected datasets. 
We are looking for a researcher with background in engineering, physics/earth science, statistics and computer science. Strong quantitative skills and experience with programming languages such as Python or MATLAB are advantageous. Students will be mentored in experimental work, and there will be an opportunity to submit results to a major conference and/or a peer-reviewed journal.

Skills/Interest/Background: Computer Programming, Engineering, Machine Learning, Mathematics, Physics, Scientific Programming, Statistics

Structural and electronic response of swift heavy ion irradiated iron oxides

SESUR, SURGE
Category(s): Evolution of Earth
Department: Geological Sciences

Faculty: Rodney Ewing & Wendy Mao
Grad Student: Sovanndara Hok

Iron oxides are an abundant class of chemical species that exist in the Earth and other planetary bodies, and they play a significant role in planetary accretion and evolution. Iron oxides in these environments experience a wide range of extreme conditions which can alter their atomic and electronic structure. Swift heavy ion irradiation is a technique that involves bombarding a material with energetic particles in order to simulate the extreme environments in the planetary interiors and interstellar space.

Hematite (α-Fe2O3), magnetite (Fe3O4) and wüstite (FeO) were chosen in order to investigate a the range of oxidation states and crystal structures. These iron oxides were irradiated with 680 MeV Au26+ ions at eight ion fluences ranging from 1011 to 1013 ions/cm2 at the GSI accelerator facility in Darmstadt, Germany. In this project, students will use Raman spectroscopy, X-ray diffraction and X-ray absorption techniques to interrogate the irradiated iron oxides. These measurements will reveal changes in atomic structure, oxidation state and local bonding environment in the irradiated iron oxides. The goal of this project is to study (1) how the swift heavy ions facilitate defects and deformation in the iron oxides and (2) how the initial atomic and electronic structure of the iron oxides promotes or inhibits transformations. Given the abundance of iron oxides in the universe, understanding the response of iron oxides under swift heavy ion irradiation can potentially inform and improve astrophysical and geophysical models.

We are looking for engaged and enthusiastic students who will take initiative in learning new analytical techniques and applying this knowledge to their research. The project will involve collecting, analyzing and interpreting Raman and X-ray measurements with the support of the mentor. No prior geology or geophysics knowledge is required but background or training in material science is desirable. 

Skills/Interest/Background: Chemistry, Lab Work, Physics

Understanding the role of oxygen and temperature change on the marine biota in the Northeast Pacific

SESUR
Category(s): Climate Change, Evolution of Life, Ocean
Department: Geological Sciences
Faculty: Erik Sperling

Due to anthropogenic increases in CO2 the world is becoming warmer and oceans are also becoming more acidic and less oxygenated. Yet because warming and de-oxygenation occur heterogeneously in the ocean, it can be difficult to predict how this will affect the marine biota along an ocean margin. Recently, an ecophysiological model (the Metabolic Index; Deutsch et al., 2015 Science and Penn et al., 2018 Science) has been developed that can predict aerobic habitat loss for a given species across its entire range based on laboratory measurements. This project represents the first time this promising energetics-based approach will be used to predict future organismal habitability against high-resolution oxygen/temperature predictions for the U.S. west coast. These predictions of future habitable range will have high utility in conservation and fisheries planning.

Two possible SESUR projects are offered. The first investigates important crustacean fisheries (Dungeness crab and spot prawn) in the San Juan Islands, WA, area. The second investigates how multiple species of sea urchins (which are one of the key ecological links in kelp forest ecosystems) will be affected by warming/de-oxygenation along the northeastern Pacific ocean margin. Over the course of the project, the student will learn marine invertebrate biology and physiology, global change biology, and how environmental change in the geological past has affected life on Earth.

Experimental protocol: The experiments themselves will involve closed-cell respirometry experiments. These techniques are relatively straightforward but do require careful attention to detail and at times long hours. These experiments will be conducted on organisms of different body size at different temperatures, and the summary results used to map aerobic habitable range for each species in the year 2100.

Location: Both research projects will be off-campus. The first possible SESUR project focused on crustacean fisheries will take place at Friday Harbor Marine Lab (San Juan Islands, Washington State). The sea urchin project will take place at Bamfield Marine Lab (west coast Vancouver Island, Canada) and Quadra Island (Inside Passage, British Columbia). Both are beautiful and vibrant marine stations, renowned centers of marine science research, and a great place to spend the summer! This work will be in collaboration with members of the Sperling Lab including Andy Marquez, Dan Mills, and Murray Duncan.

Requirements: These techniques can be taught relatively quickly. The project does not require any specific background knowledge or skills and is open to all levels of experience, although previous experience with R or marine biology/oceanography would be useful. Because some of the work will be conducted independently by the SESUR student at the marine stations, we want to ensure the student is competent with the protocols prior to the summer. The ideal candidate will either have previous experience in the Sperling Lab or be available in spring term at least for ~5 hours every other week for a paid research assistant position to gain experience with the protocol.

Skills/Interest/Background: Biology, Field Work, Lab Work, Statistics

Analyzing seismic data recorded by optical fibers in urban environments

SESUR, SURGE
Category(s): Dynamic Earth, Natural Hazards
Department: Geophysics
Faculty: Biondo Biondi

Post Doc: Ariel Lellouch

Seismic sensing using fiber-optics cables is an emerging technology, already proving useful for downhole seismic monitoring and infrastructure monitoring. This sensing technology may also be applied using conventional telecommunication fibers, which are shallowly buried and usually have poorer coupling to the ground. While data quality is often lower, the prevalence, low cost, and extensive coverage of telecommunication fibers make them an ideal sensor for urban environments. They can be used for earthquake early warning, traffic monitoring, subsurface parameter estimation, and more.

Such a system, consisting of a 4.8 km telecommunication fiber and an optical interrogator, has been in place under Stanford Engineering Quad. It has been continuously recording data for the last three years. In addition, temporary deployment of a 40-km long fiber array has been conducted with a state-of-the-art interrogator during the previous summer. The first 10 km of that array are now continuously recorded. In this project, students will work on analyzing seismic data from temporary and continuous recordings. Depending on their background and preferences, it may revolve around machine learning applications, signal processing, earthquake seismology, or geophysical studies of the subsurface.

Prof. Biondo Biondi will supervise the project; Dr. Ariel Lellouch, a post-doctoral researcher in Geophysics, will provide mentorship. Candidates from a geophysical, seismological, or CS background are welcomed, and their contribution to the project will be adjusted accordingly to their experience.

Skills/Interest/Background: Computer Programming, Engineering, Machine Learning, Mathematics, Physics, Scientific Programming, Statistics

A Great Stromatolite Reef: correlating ~2 billion year old stromatolites reefs in Hudson Bay, subarctic Canada

SESUR
Category(s): Dynamic Earth, Evolution of Earth, Evolution of Life, Ocean
Department: Geological Sciences
Faculty: Erik Sperling

Grad Student: Malcolm Hodgskiss

Was there a reef 2 billion years ago that was 20% of the size of the Great Barrier Reef? In Hudson Bay, Orosirian-aged (2.05 - 1.8 billion years old) carbonate rocks record the occurrence of a number of stromatolite reefs that formed broad ridges with several metes of relief. These have been observed in the Belcher Islands (east-central Hudson Bay), the Nastapoka Arc (eastern Hudson Bay), and Long Island, Nunavut (southeastern Hudson Bay). This project will involve measuring stratigraphic sections, stromatolite ridge orientations, and describing stromatolite morphologies, in addition to collecting carbonate samples for carbon isotope analyses. Combined with existing published and unpublished data, this will aim to test if these three reef complexes can be correlated. If so, that would suggest the occurrence of an Orosirian stromatolite reef almost 500 km long and 220 km wide, with an aerial extent of 70,000 square kilometers. Such a reef would be 20% the size of the Great Barrier Reef, and would make it the second largest reef system in the modern Earth. Ultimately, the detailed field and laboratory studies that will be carried out in this research project will help test for what may be the largest stromatolite reef in Earth history.

This project will consist mostly of 8 weeks fieldwork, working and camping in very remote conditions, with only one other person. Day to day work will consist of assisting measuring stratigraphic sections, collecting rock samples, and geological mapping, and therefore will involve long hikes and strenuous activity. Experience working with sedimentary rocks, or taking the course 'Sediments: The Book of Earth's History' are highly desirable, as is a basic understanding of geochemistry (although not essential). Extensive camping experience, especially in remote conditions, is an asset. The student must take Stanford's two day Wilderness First Aid course.

Skills/Interest/Background: Chemistry, Field Work, Geology, Lab Work

Microbiology and Geochemistry of Deep-Sea Methane Seeps

SESUR
Category(s): Energy, Ocean
Department: Earth System Science
Faculty: Anne Dekas

Grad Students: Amanda Semler and Nicolette Meyer
Post Doc: Dr. Alma Parada


Methane seeps are island-like ecosystems on the deep-sea floor where methane – a greenhouse gas – is emitted into the water column from underlying rocks and sediments. In these environments, a group of microorganisms called the anaerobic methanotrophic (ANME) archaea consume around 80% of the seeping methane, thereby mitigating natural methane emissions. However, it remains unknown which physico-chemical variables drive their activity and distribution in sediments. Nor is it known how ANMEs will be impacted by incipient ocean warming.

Using a combination of geochemical, molecular, and microscopic techniques, the student will evaluate the effect of one physico-chemical variable – methane concentration – on the activity, relative abundance, and morphology of several ANME subgroups. The student will work with cold seep sediments already collected in Monterey Canyon using a robotic submersible and incubated in the laboratory under a range of methane headspace concentrations. This will be a co-mentorship, with the opportunity to learn diverse techniques (including DNA extraction, PCR, and fluorescence microscopy) from two graduate students (A.S. and N.M.) and one postdoc (A.P.) in the Dekas Lab. Prior research experience is not necessary, but an enthusiasm to perform lab work, high attention to detail, and interest in earth science, geochemistry, and/or microbiology is required.

Skills/Interest/Background: Biology, Chemistry, Geology, Lab Work

Understanding changes in the North American monsoon rainfall and circulation in CMIP6 projections

SESUR, SURGE
Category(s): Climate Change, Dynamic Earth, Freshwater, Natural Hazards
Department: Earth System Science

Faculty: Morgan O'Neill;  Aditi Sheshadri
Staff: Salvatore Pascale

The North American monsoon is a circulation system that brings abundant summer rains to vast areas of the North American Southwest. Understanding the details of the impact of global warming on the North American monsoon is of key importance for regional water resources and for the well-being of a great number of inhabitants of Mexico and the USA.  Unfortunately, current projections of future changes in the North American monsoon remain very uncertain, inhibiting adaptation planning and providing major challenges for the detection and attribution of observed precipitation trends.

Mean sea surface temperature warming has been identified as the primary driver of precipitation reduction in the monsoon region. However, climate models, sea surface temperature biases and different warming patterns of sea surface temperature can substantially impact the monsoon response, leading to large intermodal differences. For this project, the student will first evaluate the CMIP6 (Coupled Model Intercomparison Project phase 6 experiments) ability to simulate realistically the North American monsoon and relate errors to model biases in surface temperature. As a second step, the student will analyze a new set of experiments included in the forthcoming set of CMIP6 (CFMIP3), designed to isolate the influence of different aspects of CO2 forcing on regional climate change and showing the balance between the effects of uniform SST warming, patterned SST warming, direct CO2 radiative absorption, the plant physiological response to CO2, and sea-ice changes.
 
This project is for a student enthusiastic to learn more about the impact of climate change on the Earth's hydroclimate, and it requires a basic knowledge of analysis software (e.g. Matlab, IDL, Python, etc.) for making scientific calculations and analyzing large netcdf data sets (e.g., global climate model output or reanalysis data). Good written and oral communication skills are required. Through the project, the intern will learn the fundamental science of climate change, and how to analyze large data sets and synthesize results.

Skills/Interest/Background: Computer Programming, Mathematics, Physics, Scientific Programming, Statistics

Understanding and mitigating climate risk to vulnerable urban communities in the San Francisco Bay Area

SESUR
Category(s): Climate Change, Human Dimensions and Sustainability, Natural Hazards
Department: Geophysics
Faculty: Jenny Sukale

Lecturer: Derek Ouyang

Climate change presents a profound challenge to exposure-based risk mitigation in coastal communities like the San Francisco Bay Area because it entails unprecedented and likely increasing uncertainty, including the emergence of new, previously unknown types of risks. We need to integrate geoscience expertise with a better understanding of the vulnerability of affected communities, the primary factor determining why a natural phenomenon turns disastrous. A whole family of alternative approaches to risk mitigation that increase resilience, defined as the capacity of a community to absorb, adapt, and recover from shocks, could emerge out of this improved understanding, which then require pathways of adoption in key local and regional institutions.

The mission of the Stanford Future Bay Initiative is to contribute to a vibrant future for the Bay Area by producing actionable knowledge that enables an equitable approach to climate adaptation. Through a year-long service-learning class entitled 'Shaping the Future of the Bay Area,' students, researchers, and community partners have co-produced insights on the impacts of sea level rise in San Mateo County that have generated both summer research projects under faculty advisor Prof. Jenny Suckale as well as embedded summer service opportunities with local community-based organizations like Acterra and Siena Youth Center, under the mentorship of nonprofit staff and Stanford Lecturer Derek Ouyang. Students have supported the development of research papers on socioeconomic equity and commute disruption that are currently under publication review, a door-to-door survey that was administered to over 300 residents in East Palo Alto, and a 30-week afterschool youth curriculum currently being used in North Fair Oaks.

Through SESUR, we are looking for 1-2 undergraduates to participate in the Stanford Future Bay Initiative's summer research projects, which will continue to engage partners in local communities like East Palo Alto and North Fair Oaks in understanding climate risk and developing the Stanford Urban Risk Framework (SURF), which estimates direct and indirect socioeconomic losses from hazards such as flooding and heat waves. Specific details are to be determined by the student practicum work that takes place in Winter (GEOPHYS 118Y) and Spring (GEOPHYS 118Z). While this opportunity will focus on publication-oriented activities like literature review and data analysis, interaction with local partners will be a key foundation to our process, and students may collaborate with and assist peers who are focused on direct community service projects. We are looking for students with experience in R or Python, some background in geospatial analysis, and an enthusiasm for community engagement. Participation in GEOPHYS 118Y/Z or another initiative offering, CEE 136 (Winter), are not required but highly recommended.

Skills/Interest/Background: Computer Programming, Engineering, Field Work, Statistics

Soil Carbon Protection in Agricultural and Natural Soils

SESUR
Category(s): Climate Change, Food and Agriculture
Department: Earth System Science
Faculty: Scott Fendorf

Grad Student: Emily Lacroix

Soils are the largest dynamic reservoir of carbon (C) on Earth, storing more C than the atmosphere and vegetation combined. Microbial conversion of soil C into carbon dioxide (CO2) accounts for 1/4 of annual global CO2 emissions. Recently, oxygen depletion in soils, leading to zones termed anoxic microsites, has been identified as an important soil C protection mechanism, slowing the rate of CO2 production in upland soils. While anoxic microsites likely exert an important control on soil CO2 efflux, surprisingly little is known about their prevalence in soil, and how they change with soil properties and management practices.

Our project will examine the role anoxic microsites play in various agricultural soils and nearby 'natural' soils in storing carbon. Moreover, the results of this work will inform land management strategies to maximize soil C storage in cropland soils and ultimately help mitigate global climate change.

As a summer researcher, you will work to collect soil samples, analyze soil properties, and conduct incubations and water extraction experiments to measure greenhouse gas production and oxygen content. In order to capture a range of soil and climatic factors, field sampling will likely involve spend time driving across the United States (with Emily Lacroix) to collect and analyze soils and gas efflux from both agricultural and natural systems. We expect the position to involve long car trips and many hours working outside. Lab work includes elemental analysis by combustion, density fractionation, texture analyses, and more. A driver's license and lab or field experience would be nice but are not required. Most importantly, the student must have a sense of humor and willingness to learn!

Skills/Interest/Background: Biology, Chemistry, Field Work, Lab Work

Scaling investments in wastewater-to-fertilizer technologies

SESUR, SURGE
Category(s): Food and Agriculture, Human Dimensions and Sustainability
Department: Earth System Science
Faculty: David Lobell

 

Globally, 80% of wastewater is discharged to the environment without treatment, emitting nutrients, organic contaminants, and microorganisms. Nutrients such as nitrogen and phosphorus can induce eutrophication, or algal blooms that alter aquatic ecosystems by consuming dissolved oxygen and producing cyanotoxins that also threaten human health. As energy, food, and water become more scarce, it becomes untenable to use energy to produce potable water, only to combine it with excreta and discharge the mixture to the environment. In the case of nitrogen, energy-intensive fertilizer production (Haber Bosch process, N2 ‚Üí  NH3) and removal from wastewater (NH3 ‚Üí N2) are reverse processes. Recent technologies developed at stanford offer a low-cost way to recover nitrogen from wastewater and use it as fertilizer. Typically the end products are very high in sulfur, which can improve the effectiveness of fertilizer in some soils, but not in others. In this project, the student will focus on mapping demand for N and S for crop production at subnational scales in Africa. The student will use existing geospatial datasets on soil properties, fertilizer trial results, population density, and cropping area to estimate the local demand for N and S fertilizers. This information will then feed into broader analyses to prioritize the best locations to trial the new technologies and accelerate the transition from laboratory findings to real-world solutions.

A background in GIS and basic programming skills (R or python) is required.

Skills/Interest/Background: Computer Programming

Crop yield impacts of wet springs

SESUR, SURGE
Category(s): Climate Change, Food and Agriculture
Department: Earth System Science
Faculty: David Lobell

Post Doc: Jill Deines

Climate change is expected to increase heavy spring rainfalls in agricultural regions, as seen in the midwestern U.S. in 2019. Wet springs can hurt crop yields by causing ponding on fields, thus delaying planting and/or reducing plant emergence. However, the latter factor is not well understood and likely occurs to varying degrees in every year, given local variations in topography and soil texture. The goal of this project will be to improve understanding of the importance of very wet springs to crop productivity in the Corn Belt of the US.

We hypothesize that a significant and growing fraction of the yield gap in the Corn Belt is attributable to poor emergence caused by high early season rainfall and field ponding. The student will combine two new sources of high-resolution geospatial data derived from satellite data - one on the presence of standing water during the spring, and another on maize and soybean yields at field-level resolution.

A background in GIS and basic programming skills (python, R, Javascript, etc) are strongly desired. Student will gain knowledge about climate impacts on agriculture and build skills in spatial data analysis and satellite applications in agricultural monitoring.

Skills/Interest/Background: Computer Programming, Statistics

Deep transfer learning for crop field segmentation

SESUR, SURGE
Category(s): Food and Agriculture
Department: Earth System Science
Faculty: David Lobell

Grad Student: Sherrie Wang

The size and spatial distribution of agricultural fields are basic characteristics of rural landscapes, yet remain poorly mapped in smallholder systems in the developing world. High resolution satellite imagery and recent advances in computer vision offer opportunities for automated segmentation of field boundaries, but we often lack ground truth labels on which to train data-hungry deep learning models.

In this project, the student will help develop a model to segment crop fields, with a focus on few-shot learning and model generalization in smallholder systems. Datasets will include high resolution satellite imagery and field boundaries from the US, Europe, and Ghana.

A background in computer programming and deep learning frameworks (e.g. PyTorch, TensorFlow) is required, as the student will experiment with different neural network architectures and transfer learning techniques. Knowledge of satellite datasets, crowdsourcing, and agriculture is a plus but not required.

Skills/Interest/Background: Computer Programming, Machine Learning

How is the use of California forests changing?

SESUR, SURGE
Category(s): Climate Change, Food and Agriculture, Human Dimensions and Sustainability, Natural Hazards
Department: Earth System Science
Faculty: Chris Field
Post Doc: Christa Anderson
 
Over the past 20 years, timber harvest in California has declined continuously. At the same time, due to climate change, forest fire risk and mortality due to pests have also increased. Better understanding the way that forest management has changed is needed to understand the future of forest health. How has timber harvest changed in the past 20 years in California, in addition to declining overall harvest? Does a decline in timber harvest provide information about future forest health or climate change risks? This project will use GIS and documentary evidence to examine changes in California timber harvest. 
 
The ideal student is interested in ecology, forests, and mapping. They are highly organized and interested in the opportunity to develop an independent research project. Most of the research will be desk-based, but field visits to forests are expected.
Skills/Interest/Background: Computer Programming, Scientific Programming, Statistics

Environmental Justice in Urban Tree Cover

SESUR, SURGE
Category(s): Climate Change, Human Dimensions and Sustainability
Department: Earth System Science
Faculty: Chris Field

Post Doc: Christa Anderson

Research in environmental justice has documented many was in which disadvantaged communities often face poor air quality and higher exposure to environmental hazards. Another aspect of environmental justice is whether disadvantaged communities have lower tree cover and access to open spaces compared to non-disadvantaged communities. Some studies have assessed whether disadvantaged communities have lower greenness than other communities, but research has not often been conducted looking at fine scale tree cover-trees lining city streets. 

New high-resolution urban tree cover data for California's Bay Area paired with the state's database on other socio-economic and environmental factors allow for a more detailed analysis to assess whether there is a relationship between disadvantaged communities and urban tree cover in California's Bay Area. Do poor communities have fewer trees or fewer open spaces? Do communities that face high levels of pollution have fewer trees? Do communities with high incidence of asthma have fewer trees? 

The ideal student is interested in environmental justice, has some experience in R, and has interest or experience with remote sensing and statistics. Most of the work will be based at Stanford, but field visits to relevant sites are expected. For inspiration on this research, see a recent article on disparities in tree cover in Los Angeles: Why Shade is a Mark of Privilege in Los Angeles.

Skills/Interest/Background: Computer Programming, Machine Learning, Scientific Programming, Statistics

Understanding Sustainable Supply Chain Practices in the Information and Communications Technology Sector

SESUR, SURGE
Category(s): Climate Change, Energy, Human Dimensions and Sustainability
Department: Emmett Interdisciplinary Program in Environment & Resources
Faculty: Adam Brandt

Grad Student: Lin Shi

Leading information and communications technology (ICT) companies such as Apple and Google have taken initiatives towards sustainably manage their supply chains. Yet ICT companies have different approaches to define and implement sourcing sustainability. What motivates ICT companies to take actions and communicate their supply chain sustainability? What approaches are they taking? This project will help understand how ICT companies manage and disclose their supply chain sustainability through analyzing documents from the companies and 3rd parties.

We are looking to collaborate with 2 undergraduate students to conduct text analysis over the summer to address the questions outlined above. Each student will play an active role in the research process, including data collection and analysis. We look for candidates who are analytical and comfortable to learn new skills. Prior experience with content analysis, machine-based text analysis, and familiarity with written Chinese (helpful for understanding major actors in the ICT space) is a plus.
If you are interested, please send a paragraph of interest (3-5 lines) and CV to Lin Shi at linshi@stanford.edu.

Skills/Interest/Background: Computer Programming, Machine Learning

The geologic history of water and life in the Amazon Basin

SESUR
Category(s): Climate Change, Evolution of Life, Freshwater
Department: Geological Sciences

Grad Student: Tyler Kukla
Post Doc:  Katharina Methner 

The Amazon Basin discharges more than 20% of our planet’s annual freshwater and provides more than 10% of the oxygen we breathe. While this region is critical for the climate system today, relatively little is known about how it came to be so important. Was the Amazon always a hub of primary productivity and biodiversity? How resilient is the rainforest to past climate change?

This project will reconstruct the history of Amazon climate from geologically recent soils that span much of the continent plus older rocks collected from a large, international drilling project. These samples hold chemical signatures that provide information about the climate and environment in which they formed, offering some of the first-ever insights to the long-term evolution of the Amazon Basin. With existing numerical models, these data will allow us to quantitatively reconstruct the evolution of the water cycle and atmospheric circulation and probe the implications for the global climate system.

We are seeking an enthusiastic student to tackle this problem with geochemical lab analyses and computer simulations. The balance between lab and computer modeling will depend on the student’s interests plus the degree of prior math and coding experience. While prior experience may shape the project trajectory, no prior experience is required; the student will acquire all necessary skills during the program.

Skills/Interest/Background: Chemistry, Computer Programming, Lab Work, Mathematics, Physics

Investigations in Optimizing a Forced Air Static Composting System

SESUR, SURGE
Category(s): Food and Agriculture
Department: Emmett Interdisciplinary Program in Environment & Resources
Faculty: Scott Fendorf

Grad Student: Rachel Reed
Staff: Patrick Archie

Purchasing compost can be a large burden to many small scale farms whose profits are shrinking every year. Ideally, farms would be able to make their own compost but doing so can be incredibly labor intensive. Small scale farming relies heavily on manual labor and they are unable to dedicate the time needed to build their own compost piles. This year we have begun working on a forced air static composting system that pumps air through channels underneath the compost pile in cycles that is meant to mimic a farmer going in and "flipping" the pile to reintroduce oxygen to the system.
 
We are looking for a motivated student who is interested in sustainable agriculture. The main focus of the summer will be on optimizing the forced air composting system that was built on the farm this year. We want to be able to determine an optimal cycling system for the compost (how frequently the air should circulate, at what airflow velocity, and at what penetration) so that we can upload the results to an Open Source Farming Page. You should expect to get dirty everyday, be self sufficient, and have a deep passion for sustainable agriculture. Work will include doing background research to determine previous work done in this field and building upon that work. You should also be comfortable using hand tools and shovels. Work on the composting system will be interspersed with hands on learning on the farm in agriculture practices. Preference will be given to freshman.
Skills/Interest/Background: Biology, Engineering, Field Work

Using Psychology and Neuroscience to Study Environmental Attitudes and Behaviors

SESUR
Category(s): Climate Change, Energy, Human Dimensions and Sustainability
Department: Emmett Interdisciplinary Program in Environment & Resources
Faculty: Brian Knuston

Research Associate: Dr. Nik Sawe

We use a combination of methods from behavioral economics, neuroscience, and psychology to study opportunities to improve pro-environmental policy support and behavior change. We have several projects that students can get involved in.

1)     Using neuroimaging (FMRI) to study the effect of different behavioral economics interventions to incentivize pro-environmental behaviors, to understand how these incentives are processed and which brain regions are most predictive of behavior change. Students would help in designing surveys, creating the programs that MRI subjects interact with, scanning the subjects in MRI, analyzing data, and keeping track of subject participation in follow-up surveys. Knowledge of Python, MATLAB, R, and/or the Qualtrics survey platform are helpful but not required.

2)     Studying the efficacy of new terminology as replacements for existing terms commonly used in the media related to climate change, renewables, and sustainability, with the aim of finding less partisan, more compelling alternatives. Testing will be in both nation-wide surveys and through interviews and focus groups. Students will assist with survey design in Qualtrics and interviewing of participants, as well as data analysis. Prior experience with interviewing, surveys, NLP techniques, or R is beneficial but not required.

3)     Analyzing the effects of nature imagery in both the US and India, to understand which images are most compelling for eliciting intentions to engage in pro-environmental behaviors, as well as which are the most able to improve mood and other outcomes in individuals who suffer from depression and anxiety, in an effort to study the potential mental health benefits of nature imagery. Students will assist with data analysis. Prior experience with R or other statistical analysis software is required.
Skills/Interest/Background: Biology, Computer Programming, Lab Work, Machine Learning, Statistics

Evaluating the fate of hurricane outflow air in a global simulation

SURGE
Category(s): Climate Change, Dynamic Earth
Department: Earth System Science

Hurricanes are likely to be impacted by global warming, and some experts believe that the signal has already risen above the noise for the most powerful storms. However some of the mechanisms that make the worst hurricanes, like the process of rapid intensification, remain poorly understood and forecast. These gaps in physical understanding of how hurricanes work make predicting the consequences of climate change more difficult.

This project will make use of the NASA GEOS-5 Nature Run, a global 2-year simulation with dozens of realistic, interacting hurricanes. The student will study the largest part of the hurricane: the outflow, which is the exhaust air dumped into the top of the atmosphere from the eyewall. The outflow direction and impact are severely under-studied and the GEOS-5 Nature Run is an excellent tool for better understanding how hurricane outflow interacts and evolves. The subsidence rate, which is the speed at which outflow air aloft downwells back into the boundary layer, will be quantified and may yield surprising answers.

A good candidate for this project will have some familiarity with python and be comfortable with learning new tools; including reading netcdf files and using the parcel tracking software LAGRANTO. The student can expect to gain an improved appreciation for atmospheric thermodynamics and concepts like inertial stability, and do cutting-edge research at the forefront  of hurricane science. Good oral and written communication skills are required.

Skills/Interest/Background: Computer Programming, Mathematics, Physics, Scientific Programming

Healthy soil, healthy crop: remote investigation of soil-yield relationships in Mexico

SESUR, SURGE
Category(s): Food and Agriculture
Department: Earth System Science
Faculty: David Lobell

Grad Student: Jake Campolo

Soils are an integral component of agricultural production, supplying essential nutrients, maintaining water availability, and fostering biological communities which benefit crops. However, soil fertility decline is an ongoing concern in smallholder agricultural systems. Infertile or degraded soils lead to reductions in both yields and fertilizer effectiveness, ultimately lowering profitability and jeopardizing farmer income and food security. This project aims to (1) quantify relationships between soil characteristics, fertilizer responsiveness, and crop yields at regional scales, and (2) assess the impact of new soil and fertilizer management recommendations on improving yields. We will investigate these questions using over 200,000 fields in Mexico surveyed over 8 years, digital soil maps produced from large soil sample datasets, and high-resolution crop yield maps from satellite- and simulation-based estimations.

We are looking for a student to assist in synthesizing survey location data and soil data with high-resolution satellite imagery using geospatial techniques. Later work will focus on using machine learning with the resulting dataset to predict crop types, plant and harvest dates, and yield estimates, as well as statistical analyses to test the role of soils and the effects of management recommendations. Opportunities exist for the student to explore their own scientific questions that arise from working with the available data. Experience with data mining, visualization, and statistics in R or Python is recommended, as well as an understanding of or previous experience with geospatial applications such as GIS or Google Earth Engine. Students will gain technical experience in remote sensing and big data analysis, as well as knowledge of smallholder agricultural systems and the science behind achieving global food security.

Skills/Interest/Background: Computer Programming, Machine Learning, Scientific Programming, Statistics

Using Data Assimilation to Improve Carbon Cycle Model Predictions

SESUR
Category(s): Climate Change
Department: Earth System Science

Graduate Student: Caroline Famiglietti
Post Doc: Gregory Quetin

The terrestrial carbon cycle—how much carbon plants absorb through photosynthesis, how much plants and soils respire, and other fluxes—is an essential part of life on Earth. Modeling the carbon cycle globally is crucial for making predictions of the fate of the biosphere under climate change, but is also extremely difficult due to its complexity and variability. One way to improve carbon cycle models is by using observed data to constrain the parameters that control flows of carbon between different pools, thereby updating the model to best reflect reality. Because soil and vegetation behavior is so diverse across locations, determining the best values for these parameters in global models can be challenging without explicit constraints from data. This method of optimally combining theory with observations is called data assimilation. 

Data assimilation is especially effective for global carbon cycle models because of recent developments in the quantity and quality of relevant satellite remote sensing data available for ingestion. Our research group uses one such data assimilation system—the CARbon Data MOdel fraMework (CARDAMOM)—in different ways to better understand what these observations can tell us about vegetation and soil behavior. CARDAMOM is a flexible framework that can be customized by the user.

We seek a motivated student interested in applied scientific programming and data analysis. The student will use CARDAMOM to test how carbon cycle prediction accuracy is affected by using manually-selected (non-optimized, a method commonly used for other models) versus optimized model parameters. While a background in earth sciences is not required, the student should have some prior experience with C and/or Python, and ideally also with working at the command line.

Skills/Interest/Background: Computer Programming, Mathematics, Scientific Programming

Understanding drivers of large wildfire in western USA

SESUR
Category(s): Climate Change, Dynamic Earth
Department: Earth System Science

Grad Student: Krishna Rao

Large wildfires pose a significant threat to humanity. In the state of California alone, wildfires caused more than $3.5 Bn worth of damage to property and claimed 103 lives in 2018. Our current understanding of wildfire danger is predominantly shaped by wildfire's link to climate drivers (e.g. temperature, precipitation, etc.). However, we have limited insight into the role of fuel moisture (wetness of vegetation) and other factors in governing wildfire size.

This project aims to investigate the specific climate, fuel, human, and other associated drivers of the 10 largest wildfires in western USA since 2015. Understanding the specific events leading up to wildfires can improve our wildfire danger assessments.  In particular, we are interested in using case studies to determine the influence of 'fuel moisture' - how dry vegetation is, on the eventual fire outcomes.

We seek an enthusiastic student who will work with Krishna Rao and Alexandra Konings to investigate the drivers behind large wildfires. The student will be responsible for visualizing and quantifying climate, fuel, and geographic variables before the start of wildfires and identify common patterns between them. This could be done by visualizing time series of the various drivers or by mapping the variables during the weeks leading up to the fire. The student should have prior experience with working with geospatial data, either using a scripting programming language (such as Python, Matlab, or R) or on a geographic information system (GIS) platform. 

Skills/Interest/Background: Computer Programming, Engineering, Machine Learning, Scientific Programming, Statistics

Estimating root-zone soil moisture from radar remote sensing using neural networks

SESUR
Category(s): Dynamic Earth, Food and Agriculture
Department: Earth System Science

Grad Student: Krishna Rao

Root-zone soil moisture (RZSM) has a major influence on plant physiological processes, because it directly controls the amount of water available for photosynthesis and growth. By controlling how much water plants transpire, it can also influence weather. In spite of its importance, we lack large-scale estimates of RZSM - in situ measurements cover only small areas, and physical models are highly uncertain. RZSM estimates have not previously been available from satellite from remote sensing. Determining RZSM at large spatial scales could, among others, directly benefit drought-driven tree mortality prediction, precision agriculture, and explaining landscape-scale heterogeneity in vegetation type. 

This project aims to develop large-scale estimates of RZSM using space-borne microwave radars. Instead of using a process-driven method to estimate RZSM from radar backscatter (which require a large number of in-situ parameters that are impractical to gather at landscape-scale), we will use a data-driven approach and empirically train a neural network (deep learning). 

We seek an enthusiastic student to develop the neural network. Background in Earth sciences is not required. The student should have prior experience with programming in Python, and some experience (or desire to learn) the TensorFlow deep learning library. 

Skills/Interest/Background: Computer Programming, Machine Learning, Scientific Programming

Impact of climatic and soil stressors on rice production in South and Southeast Asia

SESUR, SURGE
Category(s): Climate Change, Food and Agriculture
Department: Earth System Science
Faculty: Scott Fendorf

Grad Students: Tianmei Wang and Aria Hamann

Rice is a staple for more than half of the world’s population. Soils used for rice cultivation within South and Southeast Asia are derived from Himalayan sediments that have naturally occurring arsenic. Moreover, irrigation with arsenic containing groundwater is increasing the soil concentrations of arsenic. Arsenic poses a chronic threat to human health when consumed, and it also retards growth of rice plants, threatening rice yield and grain quality. Our previous studies revealed that climatic stressors coupled with soil arsenic substantially decrease rice yield and jeopardize grain quality for the Californian rice. We are now expanding our studies to represent global rice production, examining different soil types and rice varieties, with a specific emphasis on rice production in Asia where 95% of global rice is grown.

The goal of this project is to assess to what extent elevated temperature and atmospheric CO2 (parameters of climate change) combined with soil arsenic affect rice yields and grain quality within South and Southeast Asia. We will use soils from Bangladesh and greenhouse conditions emulating current and future climates. We will conduct highly-controlled greenhouse experiments with different soil arsenic concentrations and climatic conditions projected to occur over the rest of this century. The geochemistry of soil porewater and physiological changes of rice plant will be analyzed to understand the fate of arsenic in the soil-rice continuum.

We’re looking for a highly motivated student to maintain greenhouse pot experiment in fully climate-controlled chambers, collect and analyze porewater samples, and assess changes in rice physiology throughout the growth period. Previous laboratory experience in biogeochemistry or environmental science would be helpful.  A willingness to work in warm, humid conditions along with conducting detailed laboratory analyses is needed.

Skills/Interest/Background: Biology, Field Work, Lab Work

Predicting soil arsenic levels within rice paddies using remote sensing

SESUR, SURGE
Category(s): Climate Change, Food and Agriculture
Department: Earth System Science
Faculty: Scott Fendorf

Grad Student: Tianmei Wang
Postdoc: Dr. Samuel Araya

Rice is a staple for more than 50% of the world population. Thus, it is crucial to accurately estimate rice productivity in the future to feed the growing population. However, rice paddies in South and Southeast Asia contains naturally occurring arsenic which is a health concern for consumption and a threat for sustaining crop yields. Moreover, up-to-date mapping of soil chemistry in South and Southeast Asia is seldom available. The goal of this project is to (1) detect plant traits leading to decreased yields and degradation of grain quality resulting from arsenic and (2) predict arsenic concentrations within rice plants/grain and in soil.

We’re looking for a highly motivated student to develop machine learning algorithm to monitor health conditions of rice plants and predict heavy metal(loid)s contamination in rice paddies from satellite and drone imagery. The student will have the opportunity to participate in field campaigns together with the mentors, including flying drones and collecting plant tissue and soil samples from rice paddy fields. Prior programming experience in python, R, or another language is required. Experience with machine learning and computer vision is highly desired.

Skills/Interest/Background: Computer Programming, Field Work, Machine Learning, Scientific Programming

Examining environmental exposure disparities in California

SESUR, SURGE
Category(s): Energy, Human Dimensions and Sustainability
Department: Emmett Interdisciplinary Program in Environment & Resources

Grad Student: David Gonzalez
 
Racial/ethnic minority and low-income populations are disproportionately exposed to environmental hazards and have disproportionately low access to environmental benefits. Most prior studies that have investigated exposure disparities look at one point in time, limiting capacity to examine the processes that produce exposure disparities. The objectives of this study are to describe racial/ethnic and socioeconomic disparities in the siting of industrial sites and to explore hypotheses that produce exposure disparities.

This work will involve applying methods from quantitative social sciences using longitudinal environmental and demographic data. With guidance from research mentors, the student will prepare and analyze data and assist with visualizing and interpreting results. Depending on the student's interests, we may be able to pursue field work for a closely related study in central California. We are particularly interested in students seeking to build data analysis skills in R or similar statistical packages (you don't need to have extensive experience, but familiarity with a statistical package is helpful).

Skills/Interest/Background: Scientific Programming, Statistics

Can satellite observations of soil moisture be used to forecast maize growth in East Africa?

SESUR, SURGE
Category(s): Food and Agriculture, Human Dimensions and Sustainability
Department: Earth System Science
Faculty: David Lobell

Postdoc: Dr. Andreas Schlueter

Recent advances in satellite observations of soil moisture provide unprecedented opportunities for monitoring and forecasting of tropical crops. The new datasets can help to better understand how vegetation responds to daily changes in soil moisture, which is an important information for famine early warning systems. The prospective student will work on new ways to forecast the response of maize, which is the dominant staple crop in East Africa, from satellite observations of soil moisture. He/she will download and preprocess the satellite data, calculate lagged correlation of the timeseries of soil moisture and vegetation indices within maize pixels, and test simple linear models to predict maize growth from soil moisture observations. If time and prior experience permits, the student can collaborate with an existing project in Computer Science and, furthermore, develop simple non-linear models using machine learning methods. The student should have good coding skills (preferably Python). Prior experience with remote sensing and machine learning is a plus.

Skills/Interest/Background: Computer Programming, Statistics

Investigating traffic congestion and emissions to make equitable policy

SESUR, SURGE
Category(s): Energy, Human Dimensions and Sustainability
Department: Energy Resources Engineering

 

Sonoma county and Santa Cruz county in California are two examples of counties that face increasing traffic congestion and pollution because of increased commuter traffic to the Bay Area. It is not straightforward to design policies that can help mitigate these problems without leading to inequities. For example, existing electric vehicle incentive programs typically mostly benefit the wealthier populations. Incentives to move closer to work usually do not work well for lower income families for whom the cost of living closer to work remains much too high. Also, truly decommissioning internal combustion (ICE) vehicles is not easy either. Most studies consider an ICE vehicle decommissioned if a person replaces an ICE vehicle with an electric vehicle. However, these ICE vehicles are usually sold and stay on the road. They may in fact then be driven more and less well-maintained as the income level of the new owners typically is lower, and their commute distances may be longer. So, for communities like Sonoma and Santa Cruz this is a head-scratcher. What to do?

The DIVE research group on campus (DIVE stands for Decommissioning Internal-combustion-engine Vehicles) hopes to shed some light on this. We are a group of enthusiastic students and faculty who are very interested in sustainability and equity issues. We work together with local experts.

The big question we posed above requires many smaller subproblems to be solved. One subproblem we are working on now is a computer simulator that can realistically simulate traffic situations. We are building one particularly for CA highway 17, the biggest traffic corridor between the Bay Area and Santa Cruz. We will use this simulator to predict the outcomes of various traffic scenarios, which will help to inform our decision making.

We are open to students with all kinds of backgrounds. Are you good at data collection?  Can you write code? Would you be interested in studying policies and policy outcomes in other regions/counties to get ideas? Would you like to develop traffic models?  Would you like to learn more about ICE vehicle decommissioning? Are you interested in demography? We can use students with any of these interests and more.

Skills/Interest/Background: Computer Programming, Engineering, Machine Learning, Mathematics, Physics, Scientific Programming, Statistics

Statistic Learning of Fluid Properties

SESUR
Category(s): Energy
Department: Energy Resources Engineering

Grad student: Livia Fulchignoni

Computer simulations of fluid flow are used in many fields of science and engineering. Such computational models require different inputs, including a fluid's thermophysical properties. Identification of these properties is a challenging task when the fluid is a mixture of multiple fluids, such as liquids and gases. Recent discovery and design of complex fluids with atypical fluid compositions further complicate this task. Traditional correlations may not be valid for a particular composition, and some meta-fluids may lack adequate equations of state. This project involves statistical analysis of experimental data and/or physics-based modeling.

A student working on this project will gain a general knowledge about modeling of thermophysical properties of complex fluids and learn how to parlay this knowledge into development of new constitutive laws for a given fluid from the theory and measurements. The research consists of data discovery from relevant literature and public databases, coding thermodynamic equations, data and error analyses, and coding to automate these tasks. Programming experience (preferably MATLAB or Python) is required. A background in statistics is desired but not necessary. The student will be mentored in solving an engineering problem, quantitative and qualitative data analysis, and organization. The results of this study are likely to lead to a publication in a peer-reviewed journal.

Skills/Interest/Background: Engineering, Machine Learning, Mathematics, Physics, Scientific Programming, Statistics

Analyzing the impact of collaborative freshwater governance regimes on environmental outcomes

SESUR, SURGE
Category(s): Freshwater, Human Dimensions and Sustainability
Department: Emmett Interdisciplinary Program in Environment & Resources
Faculty: Bruce Cain

Graduate Student: Gemma Smith

What do we know about the linkages between human institutions and their impact on the physical environment? Often, we struggle as researchers to make the explicit connection between natural resource governance institutions and the environmental outcomes associated with these structures. This project seeks to bridge this link through the study of collaborative governance – a governance approach which seeks to formally include governmental and non-governmental stakeholders in decision-making over freshwater resources – in US watersheds, and the environmental outcomes in these watersheds over time. We have compiled and are expanding a panel dataset of US watersheds and their governance models to study this relationship.

We are looking for a motivated student with a background in environmental/ earth science, statistics, computer science, or the social sciences with an interest in working with both quantitative and qualitative data. The student will be trained in qualitative coding techniques to identify and classify governance regimes, which may also include the opportunity to conduct phone surveys and interviews. Students with a background and interest in machine learning will be encouraged to consider and develop machine learning approaches to qualitative data classification. The student will work with mentors to use both the quantitative and qualitative data and develop and run appropriate statistical analyses, with the opportunity to present or create a short, written project on their analysis at the end of the summer. This is a great opportunity for students interested in coupled human-natural systems and interdisciplinary research, who want to gain skills in a range of data and methodological approaches. 

Skills/Interest/Background: Computer Programming, Machine Learning, Scientific Programming, Statistics

Imaging the San Andreas Fault in three dimensions with gravity and seismic data

SESUR, SURGE
Category(s): Dynamic Earth, Evolution of Earth, Natural Hazards
Department: Geophysics

Do you want to understand how the Earth behaves beneath our feet? And explore how 100 million years of plate-tectonic evolution have created the complex three-dimensional pattern of faults beneath Los Angeles and southernmost California? Do you enjoy working with and visualizing large datasets? The geophysics community has developed multiple competing seismic-wavespeed models with resolutions of 1 to 10 km horizontally and vertically, depending on the depth of interest. These different models lead to different predictions of subsurface geology, for example including the dip of the San Andreas Fault that strongly affects the predicted seismic-energy radiation pattern in the next “Big One” in southern California. You will learn about and download these models (e.g. agupubs.onlinelibrary.wiley.com/doi/10.1002/2015GC005970). Seismic wavespeed is a sufficiently good proxy for rock density that one test for the validity of these models is whether they predict the observed gravity field.

You will learn to use commercial 3d gravity modeling software ( www.geosoft.com/products/gm-sys ), then convert the wave speed models into density models (e.g. pubs.geoscienceworld.org/ssa/bssa/article/95/6/2081/146858 ) and re-format or sub-sample (filter) appropriately for use with this software. Your goal is to assess which wave speed model most accurately predicts the observed gravity field (e.g. web.gps.caltech.edu/~clay/gravity/gravity.html ) in particular over areas such as the southern San Andreas fault and Salton Trough. Can we test between specific geologic hypotheses (how steep is the San Andreas fault? how much magma is in the lithosphere beneath the Salton Trough?). The ideal student would be motivated to carry this through to a presentation at a professional meeting.

Skills/Interest/Background: Computer Programming, Geology, Physics, Scientific Programming

Remotely sensing arsenic within rice using historic land use changes in Arkansas

SESUR, SURGE
Category(s): Food and Agriculture, Human Dimensions and Sustainability
Department: Earth System Science

Mentors: Prof. Scott Fendorf, Prof. David Lobell, and Dr. Samuel Araya

Rice is the major staple crop for feeding the global population. A naturally and anthropogenically derived soil contaminant, arsenic, threatens rice production in many regions of the world. Here, we seek to examine Arkansas rice fields that were historically used for cotton production and received extensive application of arsenical pesticides, leading to a build-up of soil arsenic.  We will compare them against rice fields that were not under previous cotton production. Using the differences in historic land use, we will deduce differences in aggregated rice canopy reflectance that are correlated with arsenic contamination. This project, identifying a spectral signature for arsenic compromised rice in Arkansas, would validate the possibility of applying powerful remote sensing and AI tools to understand how rice is threatened by arsenic at regional and global scales.

We are looking for an enthusiastic student with computer programming skills (preferably Python). Previous experience with satellite imagery and machine learning is desirable, but not required.

Skills/Interest/Background: Computer Programming, Machine Learning

Walking the Razor’s Edge: Exploring hope and concern in response to climate change

SESUR
Category(s): Climate Change, Human Dimensions and Sustainability
Department: Emmett Interdisciplinary Program in Environment & Resources
Faculty: Nicole Ardoin

Climate change is a complex, wicked problem that impacts not only the biophysical world, but also how humans relate to one another and the world around them. In our lab, we seek to generate new understandings of the human response, at the individual and collective scales, to climate change. We are particularly interested in how affective (emotional) responses, such as fear, concern, grief, and hope, are influenced by peoples’ direct experiences in iconic places and megaflora, such as coastal redwood forests, or with intensive experiences, such as nature-based tours. Ultimately, we seek to understand how such distinctive experiences, which influence and impact people on individual and collective levels, influence human behaviors in the short and longer term.

FOR THE SUMMER: We seek a student to collaborate with our team on our ongoing field work related to the the Summen Project, an inter-institutional study of climate change impacts on California’s coastal redwood forests. Through the SESUR program this summer, the student will primarily be working with us on ethnographic and interview-based elements of the study to complement survey-based work that we have undertaken over the past two years. Tasks, activities, and projects in which the student may engage include, but are not limited to: conducting literature in support of analysis, application, and publications of research findings; assist with data collection through semi-structured interviews with park rangers, park visitors and nearby residents; assist with ethnographic data collection through spending time on site in the parks along with members of our research team; and provide organizational support for community listening sessions and/or design thinking workshops around the theme of “Walking the Razor’s Edge”. This is an excellent opportunity for students interested in the intersection of climate change, sense of place, and human behavior as well as in gaining exposure to ethnographic field methods and community engaged learning.

Skills/Interest/Background:

Ice Penetrating Radar: Science and Engineering to Explore Ice Sheets and Icy Moons

SESUR, SURGE
Category(s): Climate Change, Evolution of Earth
Department: Geophysics

The Stanford Radio Glaciology research group focuses on the subglacial and englacial conditions of rapidly changing ice sheets and the use of ice penetrating radar to study them and their potential contribution to the rate of sea level rise. In general, we work on the fundamental problem of observing, understanding, and predicting the interaction of ice and water in Earth and planetary systems Radio echo sounding is a uniquely powerful geophysical technique for studying the interior of ice sheets, glaciers, and icy planetary bodies. It can provide broad coverage and deep penetration as well as interpretable ice thickness, basal topography, and englacial radio stratigraphy. Our group develops techniques that model and exploit information in the along-track radar echo character to detect and characterize subglacial water, englacial layers, bedforms, and grounding zones. In addition to their utility as tools for observing the natural world, our group is interested in radio geophysical instruments as objects of study themselves. We actively collaborate on the development of flexible airborne and ground-based ice penetrating radar for geophysical glaciology, which allow radar parameters, surveys, and platforms to be finely tuned for specific targets, areas, or processes. We also collaborate on the development of satellite-borne radars, for which power, mass, and data are so limited that they require truly optimized designs. Student projects are available in support of both ice penetrating radar instrument development and data analysis.
Summer only.

Skills/Interest/Background: Engineering, Physics, Scientific Programming

Studying the subsurface response to fluid extraction and injection at wells via the integration of InSAR-measured surface deformation and well data

SESUR, SURGE
Category(s): Dynamic Earth, Human Dimensions and Sustainability
Department: Geophysics


Humans continually alter subsurface behavior by extracting and injecting fluids at wells (e.g. groundwater and oil/gas pumping; CO2 storage and wastewater disposal). These activities can have important consequences that affect local environments and communities, such as induced earthquakes, permanent compaction of aquifers, or deformation that can damage nearby infrastructure. To mitigate these effects, it is vital to monitor and understand the subsurface response to well activity, allowing us to model and predict future behavior. 

Our group is assessing the use of interferometric synthetic aperture radar (InSAR) for measuring (from SPACE!) and using the evolution of surface deformation to infer the subsurface response to various types of well activity. In this project, there is space for students with a large variety of backgrounds and interests, including signal processing, geomechanical modeling, or integration and analysis of large data sets. Though the project will be related to the application of InSAR to induced seismicity and/or subsurface fluid flow, it has the flexibility to be student-driven. Depending on research interests, it may be beneficial to have programming experience (e.g. Matlab or Python), though the only requirement is an enthusiasm to learn more about radar remote sensing and its applications!  Summer only. 

Skills/Interest/Background: Engineering, Geology, Physics, Scientific Programming

Human Dimensions of using Marine Environmental DNA to Monitor Ocean Biodiversity

SESUR
Category(s): Human Dimensions and Sustainability, Ocean
Department: Emmett Interdisciplinary Program in Environment & Resources
Faculty: Alexandria Boehm , Faculty: Meghan Shea


Scientists and conservationists are increasingly using environmental DNA (eDNA; the bits of DNA that organisms leave behind in the environment) as a way to measure biodiversity, including in ocean environments. eDNA has the potential to make monitoring marine ecosystems much easier and cheaper, but there are still many big scientific questions about what eDNA samples actually represent. In order to advance the use of marine eDNA, we need a lot of scientific work, but we also need a better understanding of how scientists, policymakers, and the public think about the promises and pitfalls of the technology. Looking at how eDNA is discussed in the media provides one window into how people perceive eDNA.

This collaborative project will involve systematically identifying media articles about marine eDNA, categorizing and recording the kind of information included in the articles, and analyzing that information to better understand how journalists depict the potential uses of marine eDNA. Research collaborators will learn more about the science of marine eDNA, gain experience with qualitative/quantitative content analysis, and if interested, contribute to drafting a manuscript on the project findings. There are no required qualifications for this position, but you would be an especially good fit if at least one of the qualifications below applies to you:

  • Excitement to learn more (and read a ton of articles) about marine environmental DNA
  • Experience conducting a large literature review
  • Interest in environmental communication
  • Experience with statistical analysis

If you are interested in the human dimensions of marine environmental DNA but not the particular project outlined, there may be opportunities to design or contribute to other related projects; please don’t hesitate to reach out.

Spring preferred.  Maybe Summer.  Meghan may be on a research cruise for a good chunk of the summer (COVID-dependent), in which case would likely not be able to mentor a student over the summer. 

Skills/Interest/Background: Biology, Engineering, Statistics

Fe-bearing colloids in anoxic sediments: How do they form? Are they mobile? And do they transport metal contaminants?

SESUR, SURGE
Category(s): Freshwater, Natural Hazards
Department: Earth System Science
Faculty: Scott Fendorf, Faculty: Maya Engel


Fe-bearing colloids are commonly found within anoxic sediments of gravel bed floodplains. These colloids may bind metal contaminants and organic compounds, influencing their reactivity and mobility. Changes in biogeochemical conditions, such as shifts in the groundwater table level and consequently in the flux of oxygenated water, may further impact the fate of the colloids. To date, the mechanisms driving Fe-bearing colloid generation are largely unknown. Thus, our main objective is to advance our understanding of Fe-bearing colloid formation, chemistry and transport.

Our project will examine the formation and behaviour of Fe-bearing colloids at oxic-anoxic interfaces. Specifically, we will investigate the mechanism of colloid generation, how varying biogeochemical conditions influence colloid stability and mobility, and whether metal contaminants incorporate into the colloids.

As a summer intern, you will assist in setting up systems in the lab to monitor the colloidal activity. You will help sample, prepare, and analyse both aqueous and solid samples of the colloids. During the internship you will learn basic lab skills as well as how to use a suite of analytic instruments that will be repeated several times (allowing you to master the skills and become confident with the work). The work will be conducted both at the Department of Earth System Science and at SLAC National Laboratory with postdoc Maya Engel and staff scientist Kristin Boye. This is a great opportunity for students interested in basic environmental/analytical chemistry.

Skills/Interest/Background: Chemistry, Lab Work

That Sinking Feeling: Modelling Land Subsidence in California's Central Valley

SURGE
Category(s): Climate Change, Dynamic Earth, Food and Agriculture, Freshwater, Human Dimensions and Sustainability, Natural Hazards
Department: Geophysics
Faculty: Rosemary Knight, Faculty: Matthew Lees


On less than one percent of the total farmland in the United States, California’s Central Valley produces eight percent of the nation's agricultural output by value. However, the Valley has an arid climate, and a significant portion of the water required for irrigation comes from groundwater pumping. The scale of this groundwater pumping has caused severe environmental issues in the Valley; one of the most acute of these is the sinking of the land surface, known as land subsidence. In places, subsidence rates have exceeded 20 cm/year, causing millions of dollars of damage to infrastructure such as canals and railways. Mitigating this subsidence is a critical issue in the Valley.

We are looking for a student to run computer simulations of land subsidence. The simulations will explore some of the details behind how and why subsidence is occurring, and how it might continue into the future. This project will be best suited to a student with background in physics and mathematics. Although training will be provided to run the computer simulations, coding experience (in any language) is desirable as the project is computational in nature. The student will gain valuable skills working with data and running physical models, as well as an insight into how physical modelling can be used to provide critical insight into environmental issues.

Skills/Interest/Background: Computer Programming, Engineering, Geology, Mathematics, Physics, Scientific Programming

Understanding satellite measurements of vegetation water content for improved wildfire prediction and monitoring of vegetation health

SURGE
Category(s): Climate Change, Natural Hazards
Department: Earth System Science
Faculty: Alexandra Konings, Faculty: Nathan Dadap, Faculty: Krishna Rao


Large wildfires and forests die-offs are occurring with increasing frequency and severity, highlighting the negative consequences of a changing climate. While much work has been done to understand and predict how droughts cause these events, there has been relatively little research about the impact of hydrologic conditions at the land surface. Our recent findings suggest that vegetation water content plays a significant role in mediating these events. Unlike many other relevant variables, vegetation water content can also be measured directly with (microwave-frequency) satellites. Given the unparalleled spatial and temporal coverage afforded by satellite measurement, these datasets have enormous potential for monitoring fire risk and forest health. However, many questions remain regarding how to interpret these measurements and their accuracy.

In this project, the student will have an exciting opportunity to compare datasets related to vegetation water content derived from various satellite products, with the end goal of contributing an improved understanding of the information that these datasets can provide as well as their limitations. If progress allows, this research could be presented at research conferences and/or through journal publication. The ideal candidate will have experience manipulating data using a programming language such as Python, R or Matlab. Background knowledge regarding wildfires or ecohydrology are a plus, though not required. We seek candidates with a strong interest in learning analytical skills applicable to data analysis in earth science.

Skills/Interest/Background: Machine Learning, Scientific Programming, Statistics

Exploring Community Action to Address Sustainability Issues

SESUR, SURGE
Category(s): Climate Change, Human Dimensions and Sustainability
Department: Emmett Interdisciplinary Program in Environment & Resources
Faculty: Nicole Ardoin, Faculty: Anna Lee, Faculty: Mele Wheaton, Faculty: Alison Bowers


Issues such as climate change and the COVID-19 pandemic emphasize the complexity and interconnectedness of the global community, particularly the ways in which collective action is required for effective solutions to such issues. In this project, we seek to understand more deeply how to foster as well as measure collective environmental literacy in support of community action to address sustainability issues. Currently, we are developing approaches and tools to measure collective environmental literacy, with an emphasis on instruments that can be administered remotely. We will begin pilot testing such approaches and measures in the winter and spring quarters 2020-21, with data collection and analysis continuing  into the summer.
 
This is a great opportunity for students interested in ideas related to environmental social science, collective and community-scale action, motivations for and  barriers to civic engagement, and larger-scale sustainability issues. Students will have a chance to develop skills in survey development and administration, conducting interviews, analyzing survey and interview data, developing literature reviews, and academic writing. We seek students from a range of backgrounds and experiences, including especially those with a background and/or some experience in the social sciences background. We also welcome those from more of a natural science or engineering background, but who have interest in the social sciences as well as those just beginning to explore the environmental and sustainability sciences.

Our lab is highly collaborative and fast-paced; respectful, kind, and constructive in interactions; and seeks team members with a diversity of perspectives, interests, and life experiences. We also seek people who are independent, demonstrate a commitment to quality and rigor, and are curious and energetic in their pursuit of environmental social science.
Spring and Summer

Skills/Interest/Background: Computer Programming, Field Work, Statistics

Linking models and data to understand the terrestrial carbon cycle

SESUR, SURGE
Category(s): Climate Change
Department: Earth System Science


The carbon cycle is an essential part of life on Earth. With global models of the carbon cycle, we can make predictions of the fate of the terrestrial biosphere under climate change, which both help to predict what ecosystems might look like in the future, and even can help us improve our estimates of changing temperatures and rainfall. However, in practice, predictions of the future carbon cycle are often highly uncertain due to the complexity and variability of the Earth system. One way to improve carbon cycle models is by using observed data to constrain the model parameters that control flows of carbon between different pools, thereby updating the model to best reflect reality. This method of optimally combining theory with observations is called model–data fusion. Model–data fusion is especially effective for global carbon cycle models because of recent developments in the quantity and quality of relevant satellite remote sensing data available for ingestion. Our research group uses one such model–data fusion system—the CARbon Data MOdel fraMework (CARDAMOM)—in different ways to better understand what these observations can tell us about model processes, parameters, and predictions. CARDAMOM is a flexible framework that can be customized by the user.

We seek a motivated student interested in applied scientific programming and data analysis. Advised by Caroline Famiglietti and Alexandra Konings, the student will use CARDAMOM to help understand and improve carbon cycle prediction accuracy. A range of available projects are available depending on the student’s interests, from model development to data analysis. While a background in earth sciences is not required, the student should have some prior experience with C and/or Python, as well as with working at the command line.
Spring or Summer

Skills/Interest/Background: Biology, Computer Programming, Machine Learning, Scientific Programming

Integrated kinetic energy of a mature tropical cyclone in a variety of climates

SESUR
Category(s): Climate Change
Department: Earth System Science


A tropical cyclone is a general scientific term describing hurricanes and typhoons.  The intensity of the surface winds of a tropical cyclone is highly relevant to its impacts when it makes landfall. How does this intensity vary with climate change? The integrated kinetic energy of a tropical cyclone acts as a measure of how intense the surface wind field is, and so understanding how this quantity may vary with different climate variables is important to predicting how the intensity of tropical cyclones may behave in future climates. In this work, we will use integrated kinetic energy as a tool to investigate how various climate variables affect tropical cyclone intensity. 

This project will involve quantifying how the integrated kinetic energy of simulated, idealized tropical cyclones is affected by changes in a variety of climate variables, such as sea surface temperature and atmospheric moisture content. With guidance from research mentors, the student will design different setups of a simple model to produce a suite of simulated tropical cyclones under different climates. The student will additionally calculate the integrated kinetic energy of these tropical cyclones to tackle the leading research question, and will interpret the physics behind the results.  

We are seeking an enthusiastic student with an interest in the physics of atmospheric flows to tackle this project involving computational analysis and working with numerical models. Knowledge of calculus and basic programming (in any language) is required. No atmospheric science experience is necessary! However, I will likely send you a bit of background reading on atmospheric science to do before the project starts. 
Summer only.

Skills/Interest/Background: Computer Programming, Mathematics, Physics, Scientific Programming

Computer Simulations of Earthquakes and Volcanoes

SESUR, SURGE
Category(s): Dynamic Earth, Natural Hazards
Department: Geophysics
Faculty: Eric Dunham, Faculty: Lauren Abrahams , Faculty: Kate Coppess


Earthquakes and volcanoes pose immediate hazards to human society. Our research group develops computer codes to model a wide range of physical processes associated with these hazards. Summer interns have multiple opportunities to apply their computing skills (programming and scripting) and/or the theory of mechanics to explore earthquakes or volcanoes. Students who enjoy programming can assist with code development or running simulations to test a hypothesis; familiarity with UNIX and prior programming experience in MATLAB, Python, C++, or another language are required. A strong background in calculus-based mechanics is also necessary because we solve solid and fluid mechanics problems (but prior experience with solid and fluid mechanics is not required). Previous experience with earth science is not required.
Spring or Summer

Skills/Interest/Background: Physics, Scientific Programming

Investigating the Impact of Soil Quality on Children's Health and Nutrition in Developing Countries

SESUR, SURGE
Category(s): Food and Agriculture, Human Dimensions and Sustainability
Department: Earth System Science


Soil quality and properties are important factors determining crop health and farm output. These agricultural outcomes are key influencers of household income, food security, and quality of nutritional intake, all of which can determine children's health and growth in low-income countries. Also, specific soil properties, particularly micronutrient content such as zinc and selenium, can directly impact human health and play an essential role in children's development. Current knowledge of the magnitude and extent of soil characteristics' impact in determining children's health outcomes is still limited. We know even less about the potential role of developmental interventions (such as subsidized food, supplemental school meal programs, and others) in mitigating the adverse impacts of low soil quality on children's health. We seek to answer these questions in the proposed study by combining soil test data from India's national Soil Health Card program with remote sensing data on agricultural outcomes and large-scale survey data on children's health and nutrition.

We are looking for a student to assist in data collection, assembly, and analysis, leading to a research report. The specific tasks will include extracting and cleaning the soil tests data from the Soil Health Card program portal, using the cleaned data to create soil health maps for key soil characteristics at varying spatial scales, constructing remote-sensing based measures of crop growth and yield, geospatial matching of soil and yield data with information on children' s health outcome in order to undertake further analysis. Previous experience with (or desire to learn) web-scraping and GIS using R or Python will be helpful. This work can be done remotely.
Spring or Summer

Skills/Interest/Background: Computer Programming, Machine Learning, Scientific Programming

Biogeographic patterns of marine animal temperature-dependent hypoxia traits

SESUR
Category(s): Climate Change, Ocean
Department: Geological Sciences
Faculty: Erik Sperling, Faculty: Andy Marquez


As oceans warm, marine organisms’ basal metabolic rates rise, increasing their demand for oxygen, while at the same time environmental oxygen supply is limited or decreasing. These synergistic effects of ocean warming and deoxygenation are driving changes in distributions, phenologies, and interactions of marine organisms as they seek environments where their physiological requirements can be maintained. The effects of temperature and oxygen availability on an organism’s aerobic metabolic capacity is captured within a single metric known as the Metabolic Index (𝚽), with species-specific parameters Ao and Eo representing the overall hypoxia tolerance and temperature sensitivity of hypoxia tolerance respectively. The goal of this project is to compile a database of joint Ao/𝚽crit and Eo values for various marine organisms using biogeographic data to study how temperature-dependent hypoxia traits vary with body size and across latitudinal gradients. We hypothesize that hypoxia tolerance decreases with body size for some organisms (Rhynchonellata and Crinoidea) but not others (Bivalvia, Gastropoda, Cephalopoda) as suggested by fossil record analyses. Additionally, we hypothesize that organisms at the tropics will have lower Ao/𝚽crit values than marine organisms at higher latitudes. Thus, this database will help us to understand which organisms are most vulnerable to temperature-dependent hypoxia and the geographic selectivity of extinction of modern climate change.

We are looking for an undergraduate student to work on this database during the summer as well as potentially travel to the Friday Harbor Labs in the San Juan Islands, WA for two months to help conduct hands-on respirometry experiments. If travel/fieldwork is not allowed by summer 2021, the project can be completed remotely. Previous research experience is not required, but computational skills will be helpful.
Summer only.

Skills/Interest/Background: Biology, Geology, Lab Work

Using satellites to measure impacts of climate extremes in US agriculture

SESUR, SURGE
Category(s): Climate Change, Food and Agriculture, Human Dimensions and Sustainability
Department: Earth System Science
Faculty: David Lobell, Faculty: Jill Deines


Climate change is expected to increase extreme climate events, including heavy spring rainfalls and summer droughts in agricultural regions. The impacts these events have on crop yields are not well understood but important for agricultural adaptation and food security. The goal of this project is to better understand linkages between climate extremes and crop productivity in the Corn Belt of the US.

The student will use geospatial data derived from satellites to analyze climate impacts at field-level resolution, making this project well suited to both in-person and remote work. Experience with GIS and basic programming skills (python, R, Javascript, etc) and willingness to learn new techniques are helpful. The student will gain knowledge about climate impacts on agriculture and build skills in spatial data analysis and satellite applications in agricultural monitoring.
Spring or Summer.

Skills/Interest/Background: Machine Learning, Scientific Programming, Statistics

Conservation agriculture: what works and where?

SESUR, SURGE
Category(s): Climate Change, Food and Agriculture, Human Dimensions and Sustainability
Department: Earth System Science
Faculty: David Lobell, Faculty: Jill Deines

Conservation agriculture aims to improve agricultural sustainability by incorporating methods that improve soil health, crop resilience, nutrient management, and carbon sequestration. This movement towards regenerative agriculture is gaining momentum but often lacks quantitative metrics to evaluate the impact across the landscape. These metrics are needed to guide best practices and incentivize action along the food supply chain. This project aims to use satellite-derived data products to evaluate practices such as cover cropping and reduced tillage.

The student will use big data techniques to compare map datasets of management practices, quantify effects on yield and environmental outcomes, and/or fuse data from local studies to support large scale analyses. Experience with GIS and basic programming skills (python, R, etc.) and willingness to learn new techniques are helpful. The student will gain knowledge about conservation agriculture and build skills in data science, causal inference, and agricultural monitoring. This project is computer-based so well suited to both in-person and remote work.
Spring or Summer

Skills/Interest/Background: Machine Learning, Scientific Programming, Statistics

Fluid migration and thin film stabilization in porous media

SESUR
Category(s): Energy
Department: Energy Resources Engineering

Fluid transport through porous media is important to energy and environmental applications including geological carbon storage, enhanced oil recovery, soil remediation, and energy storage. Multiphase flow and the movement of bubbles of gas dispersed in aqueous fluids are of special interest.  Microscale analysis of the motion and the fluid dynamics of long bubbles provides crucial information to understand the mechanisms of fluid transport and interaction. Factors of interest to understand bubble motion include the length of the bubble, the properties of the gas/liquid interface, pressure drop, and thin film fluid dynamics. Accordingly, this project aims to develop quantitative understanding to explain bubble movement in porous media and seeks to study the effect of additives such as nanoparticles to stabilize the thin liquid films that coat channel walls and surround  bubbles in microchannels.  Our analysis will be microscale and might include experiments using microfluidic devices that permit direct observation of bubble dynamics, computational fluid dynamics calculations, or a combination of experiments and computations. 

This research project ideally involves extensive data collection, processing, and analysis. It also requires fulfilling some basic programming tasks. Students will conduct microscale experiments in the lab, collect the data, and use statistical techniques and image processing to analyze the results and develop significant conclusions from the collected datasets.

We are looking for a researcher with a background in engineering, physics/earth science, statistics and computer science. Strong quantitative skills and experience with programming languages such as Python, C, or MATLAB are advantageous. Students will be mentored closely throughout the entire project, and there will be an opportunity to submit results to a major conference and/or a peer-reviewed journal.

Skills/Interest/Background: Computer Programming, Engineering, Lab Work, Mathematics, Physics, Statistics

Using Psychology, Behavioral Economics, and Linguistics to Study Environmental Attitudes and Behaviors

SESUR
Category(s): Human Dimensions and Sustainability
Department: Emmett Interdisciplinary Program in Environment & Resources
Faculty: Nik Sawe, Faculty: Brian Knutson

We use a combination of methods from behavioral economics, neuroscience, and psychology to study opportunities to improve pro-environmental policy support and behavior change. We have several projects that students can get involved in:

1)     Studying the efficacy of new terminology as replacements for existing terms commonly used in the media related to climate change, renewables, and sustainability, with the aim of finding less partisan, more compelling alternatives. Testing will be in both nation-wide surveys and through interviews and focus groups. Students will assist with survey design in Qualtrics and interviewing of participants, as well as data analysis. Prior experience with interviewing, surveys, NLP techniques, or R is beneficial but not required.

2)     Analyzing the effects of nature imagery in both the US and India, to understand which images are most compelling for eliciting intentions to engage in pro-environmental behaviors, as well as which are the most able to improve mood and other outcomes in individuals who suffer from depression and anxiety, in an effort to study the potential mental health benefits of nature imagery. Students will assist with data analysis. Prior experience with R or other statistical analysis software is required.

Spring or Summer

Skills/Interest/Background: Computer Programming, Machine Learning, Scientific Programming, Statistics

Assessing the determinant traits of extinction and origination selectivity of marine animals with respect to body size

SESUR, SURGE
Category(s): Evolution of Life
Department: Geological Sciences
Faculty: Jon Payne, Faculty: Pedro Monarrez

Animal body size has played an important role in extinction patterns in the fossil record. A widely recognized pattern has been the preferential extinction of larger animals, particularly in terrestrial vertebrates. Recent studies assessing extinction selectivity in marine animal groups, however, suggest that overall, smaller organisms preferentially go extinct during background intervals and mass extinctions. But among some marine classes, larger-bodied animals preferentially go extinct during mass extinctions, suggesting heterogeneous responses to mass extinctions among classes. When origination patterns are also assessed, similar patterns result, with selectivity alternating between background intervals and mass extinction recoveries, but not uniformly across classes. Since body size correlates with other organismal traits, such as ecology and physiology, it’s possible that these groups of traits are responsible for the evolutionary dynamics with respect to body size that are observed.

We seek a student to determine which body size correlative traits are more important in determining extinction and origination selectivity for a marine group of animals in the fossil record. The student will assess extinction and origination selectivity among a suite of organismal traits by collecting additional body size data from the published literature and using our existing database. These data will be used in statistical models that estimate extinction and origination probability. Previous experience working with R and statistics will be helpful, but not required. Opportunities to work beyond the summer might also be available. This project is a great opportunity for students seeking to learn about drivers of evolutionary dynamics in the fossil record.
Spring or Summer

Skills/Interest/Background: Scientific Programming, Statistics

Reconstructing Boundary Layer Structure of Tropical Cyclones using Satellite Observations

SESUR, SURGE
Category(s): Natural Hazards
Department: Earth System Science
Faculty: Morgan O'Neill, Faculty: Ipshita Dey

Tropical cyclones are one of the world's most lethal geophysical hazards and their global impact has dramatically increased in the last few decades. Improved forecasting of the most intense tropical cyclone events, especially within the context of climate change has clear implications for future risk assessment and mitigation strategies. Physical processes determining the dynamic and thermodynamic structure of a Tropical Cyclone Boundary Layer (TCBL) are quite different from anywhere else in the atmospheric boundary layer due to the substantial contribution of latent heating and frictional forces. These processes are closely related to storm development and intensification. However, our current understanding of TCBL is limited by the number of observations in this region. Our goal is to characterize the structure of TCBL using advanced microwave-based satellite observations. Microwave remote sensing offer a unique opportunity to observe the atmosphere beneath the cloud base within 1-2km from the surface. We would ideally like to observe, identify and track features unique to TCBL and explain their significance in the intensification of tropical cyclones.

Through this project, the student will develop a good understanding of the observational methods used for retrieving atmospheric variables, statistical tools used for identifying and characterizing the dynamics and thermodynamics of atmosphere as well as assist in the calibration and validation of satellite data. We seek highly motivated students with a mathematical and computational background - preferably with experience in MATLAB/Python/R. Background knowledge in atmospheric physics would be useful but not required.
Summer only.

Skills/Interest/Background: Computer Programming, Mathematics, Physics, Scientific Programming, Statistics

Soil Carbon Protection in Agricultural Soils

SESUR, SURGE
Category(s): Climate Change, Food and Agriculture
Department: Earth System Science
Faculty: Scott Fendorf, Faculty: Emily Lacroix


THIS PROJECT HAS IDENTIFIED A SESUR STUDENT ALREADY.Soils are the largest dynamic reservoir of carbon (C), storing more C than the atmosphere and vegetation combined. Microbial conversion of this C into carbon dioxide (CO2) accounts for ¼ of global CO2 emissions. Recently, anoxic microsites, small zones of oxygen depletion, have been identified as an important soil C protection mechanism, slowing the rate of CO2 production in upland soils. While anoxic microsites likely exert an important control on soil CO2 efflux, surprisingly little is known about the prevalence of anoxic microsites in different types of soil. We want to know whether the contribution of anoxic microsites to soil C protection varies based on soil type and/or management practices. The results of this work should inform land management strategies to maximize soil C storage in cropland soils and ultimately help mitigate global climate change.

As a researcher, you will spend time in analyzing soil samples from various agricultural sites. Potential lab work includes elemental analysis by combustion, density fractionation, soil extractions, texture analyses, and more. This work will help you learn basic laboratory skills as well as how to use and interpret the results of several analytical instruments. There is a small chance for field work, but that is still unknown. We are looking for a student who has strong attention to detail and a willingness to learn. No prior lab experience necessary. If we cannot be in-person, remote work will include literature synthesis and writing, a key skill for any discipline.
Spring or Summer

Skills/Interest/Background: Biology, Chemistry, Lab Work

Using quartz to determine the temperature and stress conditions of mylonitization

SURGE
Category(s): Dynamic Earth, Evolution of Earth
Department: Geological Sciences
Faculty: Marty Grove, Faculty: Nikki Seymour


Ductile deformation in the Earth's crust - where rocks flow like taffy rather than breaking suddenly - is recorded by minerals such as quartz. Original quartz grains are deformed and recrystallize into new grains when they are subjected to heat and strain during mountain-building episodes. The size of the new quartz grains and the way they are oriented within the rock record important information about the temperature and stress during the deformation process. This information is particularly important in regions that record multiple episodes of ductile deformation associated with different tectonic events. This project focuses on a polydeformed rock unit: the Orocopia Schist located in the Plomosa Mountains of west-central Arizona.

The student will collect electron backscatter maps of existing rock samples and use grain size and grain orientation analyses to determine the maximum stress and temperatures recorded by quartz in the Orocopia Schist. This information will be integrated with mineral chemistry, microstructural petrography, and geochronology to determine which of two likely tectonic events resulted in the recrystallization of the quartz grains. The student will gain knowledge about operating a scanning electron microscope and build skills in data collection, data analysis through Matlab, and data interpretation. Experience with scanning electron microscopes, basic programming skills (Matlab), and willingness to learn new techniques are helpful but not required. The main requirement is enthusiasm and willingness to try new techniques! This project is partially lab-based (use of the SEM) and partially computer-based (use of MTEX Matlab code) and is suited to a combination of in-person and remote work.

Skills/Interest/Background: Geology, Lab Work, Scientific Programming

Equitable Transportation Decarbonization

SESUR, SURGE
Category(s): Climate Change, Energy, Human Dimensions and Sustainability
Department: Energy Resources Engineering
Faculty: Margot Gerritsen, Faculty: Nora Hennessy

Equitable Transportation Decarbonization

Mentors: Prof. Margot Gerritsen (ERE), Nora Hennessy (ERE PhD Candidate), and Nadim Saad (CME PhD Candidate)

The transportation sector is responsible for 41% of greenhouse gas emissions in California, and 70% of those come from light-duty passenger vehicles. The Decommissioning Internal Combustion Engine (ICE) Vehicles (DIVE) group studies how to remove conventional gasoline vehicles from the road and replace them with zero emission vehicles (ZEVs). While our main goal is to reduce greenhouse gas emissions, an equally important goal is to promote equity in the transportation sector. In addition to producing greenhouse gas emissions, conventional vehicles emit air pollutants that damage human health, and often disproportionately impact low income and vulnerable communities. Removing conventional vehicles from the road would reduce these damages. On the other hand, vehicles are essential for many people and provide access to work. While the benefits of replacing ICE vehicles with ZEVs are well known, the process will likely be costly, and may place additional burdens on low income families. Our group seeks to understand these dynamics and promote equitable solutions to this challenge.

We are seeking students from all backgrounds who are interested in climate change and issues of environmental justice and equity. Potential projects include analyzing the relationship between transportation emissions, health impacts, and COVID-19; designing equitable policies to remove ICE vehicles from the road without negatively impacting vulnerable communities; or another related project the student is interested in. We will work with the student to develop a specific project to match their skills and interests. All work will be able to be done remotely.
Spring or Summer

Skills/Interest/Background: Computer Programming, Engineering, Scientific Programming, Statistics

Risks‌ ‌and‌ ‌opportunities‌ ‌for‌ ‌on-farm‌ ‌recharge‌ ‌to‌ ‌achieve‌ ‌groundwater‌ ‌sustainability‌

SESUR, SURGE
Category(s): Climate Change, Food and Agriculture, Freshwater, Human Dimensions and Sustainability
Department: Emmett Interdisciplinary Program in Environment & Resources
Faculty: Scott Fendorf, Faculty: Randall Holmes


Sustainable management of groundwater resources is critical for achieving global water and food security. California, like many of the world’s groundwater dependent regions, is tasked with addressing declining groundwater levels and climate change’s impacts to the state’s hydrologic system. We use California as a case study for evaluating a promising, distributed solution for restoring groundwater levels in the era of climate change: the flooding of agricultural fields for groundwater recharge, or on-farm recharge. During this project, we will evaluate how the physical science and engineering of balancing groundwater quality and quantity aligns with socio-behavioral challenges, including stakeholder perceptions of risks and opportunities for increasing groundwater storage using on-farm recharge. This research will aid in building an agent-based model, facilitated by Water in the West's Dr. Courtney Hammond-Wagner, to explore the types of incentives, governance structures, monitoring systems, infrastructure, and physical conditions that enable on-farm recharge to support groundwater sustainability. The research will serve as the foundation for piloting an online “On-farm recharge Incentives Dashboard” that will allow water management agencies to explore policies and programs for on-farm recharge in their respective groundwater basins.

Undergraduates working on this project will gain fundamental skills understanding the geochemistry of water quality in California agriculture, as well as coding interviews for content using socio-behavioral constructs, basic data analysis, and insights on how interdisciplinary teams collaborate to incorporate science and engineering research (such as geochemistry and water infrastructure) into policy. We’ll provide the crash course on California water politics! Summer work may lead into opportunities the following academic year. No experience necessary.   

Skills/Interest/Background: Chemistry, Engineering, Field Work, Geology, Scientific Programming, Statistics

Modeling of gasoline particulate filter regeneration in the presence of a catalytic coating

SESUR, SURGE
Category(s): Energy
Department: Energy Resources Engineering
Faculty: Simona Onori, Faculty: Gabriele Pozzato


Gasoline Direct-Injection (GDI) engine technology improves vehicle fuel economy toward future targets while, simultaneously decreases CO2 emissions. The main drawback of this technology is the increased emission of particulates (when compared to their indirect injection-based technology counterpart). Thus, aftertreatment devices such as Gasoline Particulate Filters (GPF) are today considered the most promising and practically adoptable solution to limit PM/PN out of GDI exhaust. In a nutshell, the particulate filter traps soot particles resulting from fuel combustion and prevents their release into the atmosphere. Soot oxidation (also known as regeneration) is required at regular intervals to clean the filter, maintaining a consistent soot trapping efficiency, and avoiding the formation of soot plugs in the GPF channel. To enhance the oxidation process, a catalytic coating is usually introduced in the filter.

Starting from a multiphysics model accounting for mass, momentum, and energy transport developed in our laboratory, the candidate should formulate the chemical reactions associated with the regeneration process in case a catalytic coating is present in the filter. Also, relying on experimental data, the identification of the parameters characterizing the reaction kinetics should be performed with the help of non-linear optimization tools. Knowledge of Matlab&Simulink and of COMSOL Multiphysics (or similar) is required for the model implementation and simulation activities.

Skills/Interest/Background: Engineering, Physics, Scientific Programming

Exergy-based modeling of a fuel cell hybrid electric vehicle

SESUR, SURGE
Category(s): Energy
Department: Energy Resources Engineering
Faculty: Simona Onori, Faculty: Gabriele Pozzato


In the last years, the quest for energy efficiency has led the research community to devote time and effort towards the development of new powertrain solutions for the next generation of vehicles. In this context, to evolve the standard internal combustion engine technology, hybrid electric vehicles are of interest. As a matter of fact, the introduction of multiple power sources allows for the improvement of the fuel economy and for the reduction of pollutants emissions. To benefit from hybrid architectures it is of paramount importance to introduce effective management strategies, which allow for the optimization and balancing of the power sources. In this context, the modeling of the powertrain irreversibilities relying on the second law of thermodynamics (or, in other words, on exergy and entropy) is gaining interest. This particular approach allows to explicitly formulate the loss terms for all the powertrain constituents (transmission, battery, electric motor, internal combustion engine, etc.). This allows to understand the major source of inefficiency and also to develop optimization strategies to minimize these losses.

The ideal candidate should be passionate and interested in the thermodynamics modeling of systems. The project will allow to learn fundamental concepts in the context of the second law of thermodynamics analysis of systems. In particular, the candidate will develop and simulate an exergy-based model for fuel cells, to be introduced in the hybrid vehicle modeling framework we have developed in our laboratory. Knowledge of Matlab&Simulink is required for the model implementation and simulation activities.

Skills/Interest/Background: Engineering, Physics, Scientific Programming

Exploring the interaction of wind-driven internal waves with the Florida Current

SURGE
Category(s): Dynamic Earth, Ocean
Department: Earth System Science
Faculty: Leif Thomas


Winds blowing over the ocean generate waves, not just the familiar ones that ride along the surface, but another type which travel down into the deep sea. These so-called internal waves can transmit a large amount of wind energy downwards and thus are thought to play an important role in sustaining the deep branch of the ocean circulation and in driving mixing. This study will explore the physics of wind-driven internal waves in the Strait of Florida where preliminary calculations suggest that the interaction of the waves with the strong Florida Current is quite effective at enhancing downward energy transport and mixing. The project will involve running and analyzing high-resolution numerical simulations to understand the underlying dynamics of this interaction more fully and to assess its impacts on the mixing and transport of biogeochemical tracers.

We seek a motivated student interested in using scientific programming (in particular Python and Fortran) to study the physics of the ocean. While experience in oceanography is not required, a background in math and/or physics, and programming is necessary.

Skills/Interest/Background: Mathematics, Physics, Scientific Programming

Investigating the Morphology of Impact Craters on the Moon using Unsupervised Machine Learning

SURGE
Category(s): Natural Hazards, Planetary Science
Department: Geological Sciences
Faculty: Mathieu Lapotre, Faculty: Lior Rubanenko


Impact craters, the most prevalent topographic feature on the surface of the Moon, form when meteorites impact the surface at supersonic speeds. The integrated effect of impacts over time churns the rocky surface of the Moon into a thin, powdery layer of debris called the lunar regolith. Exposed regolith darkens over time due to physical and chemical interactions with the harsh environment of space. When fresh craters form, they overturn this dark regolith and expose bright soils. As a result, fresh craters are characterized by bright walls and ejecta blankets, but also by rays; linear features that extent up to tens of crater radii away from the crater rim. Studying fresh impact craters on the Moon could help us constrain the size distribution of impactors in the Earth-Moon system, explore the recent cratering rate of the Moon, and characterize lunar surface processes that have occurred in the past millions of years.

Due to their unique, easily distinguishable surface features, fresh craters are typically identified manually by human analysts in images. However, manually characterizing a large number of craters is difficult and, in the extreme case, unfeasible. In this project, we seek to employ machine learning to automatically characterize the maturity of all simple craters (whose size is between 1 and ~15 km) on the Moon. Using an Autoencoder, a type of an artificial neural network, we will analyze high-resolution images obtained by the Lunar Reconnaissance Orbiter to identify distinct morphometric features like bright ejecta and rays and derive the maturity of craters.

The project will involve data collection and pre-processing the images, and designing and employing an Autoencoder based on popular architectures. Students with coding background in Python, R, or Matlab are encouraged to apply. Prior experience in machine learning or statistics is not required. At the end of the summer, students will be mentored to compile their results in the form of a scientific conference abstract.

Skills/Interest/Background: Computer Programming, Geology, Machine Learning, Scientific Programming, Statistics

Developing Low-Cost Tools to Measure Natural Greenhouse Gas Emissions

SESUR, SURGE
Category(s): Climate Change, Food and Agriculture
Department: Earth System Science
Faculty: Alison Hoyt, Faculty: Jack Lamb

The natural environment is a significant source of greenhouse gases in the Earth’s atmosphere, but these contributions remain poorly constrained due to measurement limitations. Current techniques require expensive gas analyzers or collecting cumbersome gas samples, either of which creates barriers against comprehensive sampling. Meanwhile, greenhouse gas emissions from the natural environment can vary widely in space and time, requiring a highly resolved measurement scheme. In our group, we have been working to address these challenges by creating easy-to-use, low-cost greenhouse gas emission sensors. In this project, one or more students will assist with the development and production of greenhouse gas sensing tools. This project provides a wide variety of opportunities to get involved, and we will work with students to define a plan for summer research based on their interests and experience. Potential focus areas include electronics, software development, manufacturing, calibration, field testing, and user experience design. Students will take a leading role in developing engineering prototypes into user-ready tools for studying Earth’s climate.

A student project might look like one of these examples: Researching and developing a manufacturing strategy to build hundreds to thousands of units; Developing user interface software to interface with hardware, and process data; Researching unmet needs in greenhouse gas sensing and designing suitable tools; Preliminary field deployment and calibration of prototype sensors; Designing hardware/firmware protocols for a wireless sensor network, or something different depending on interest.

Interested students from any field are encouraged to apply, including but not limited to earth sciences, engineering, and computer science. Experience or interest in climate science, design, manufacturing, electronics, or software development would all be beneficial to the project.

Skills/Interest/Background: Chemistry, Computer Programming, Engineering, Field Work, Lab Work, Scientific Programming, Statistics

Applying machine learning techniques to induced seismicity

SESUR, SURGE
Category(s): Climate Change, Energy, Natural Hazards, Planetary Science
Department: Geophysics
Faculty: Greg Beroza, Faculty: Ryan Schultz

Any activity that changes the distribution of stress in the Earth’s crust has the potential to cause earthquakes.  A prominent research focus in recent years has been earthquakes caused by human activities (i.e., induced seismicity): both because of their ability to cause damage in exceptional cases and their ability to halt operations.  There is still much to understand about the causes, potential impacts, and mitigation strategies for these earthquakes.  In many cases, this problem has been exacerbated by a lack of data.  Fortunately, recent developments in machine learning techniques have been able to revolutionize the catalogue-building process in seismology.  The use of neural networks on large, labelled datasets allows for super-human level development of earthquake catalogues – often increasing the resolvable data by an order of magnitude from traditional approaches.

            In this project, we intend to use recently developed machine learning techniques to build a large catalogue of induced earthquakes.  Data will span almost a decade of continuous ground motion monitoring in Alberta, Canada – a region that has encountered a significant increase in seismic activity due to oil and gas operations.  The catalogue derived from this work will be used to better discern the causative activities, their potential impacts, and possible strategies to mitigate their risks.

We are seeking a self-motivated and enthusiastic student to help us implement the developed techniques on the continuous waveform data in the catalogue building process. We anticipate the student will develop some understanding of induced seismicity, its triggering mechanisms, and techniques for risk management. The student will develop state-of-the-art skills in earthquake catalogue development. A background in geophysics or machine-learning is not required.  Some prior experience with scientific programming, preferably with Python or MATLAB would be very helpful.

Skills/Interest/Background: Computer Programming, Machine Learning, Mathematics, Physics, Scientific Programming, Statistics

Phytoplankton ecology and behavior in the Arctic Ocean

SURGE
Category(s): Climate Change, Ocean
Department: Earth System Science
Faculty: Kevin Arrigo, Faculty: Stephanie Lim

Phytoplankton are the primary carbon fixers in the ocean and serve as the base of the marine food web. The Arrigo lab studies phytoplankton’s role in global biogeochemical cycles, primarily in the polar regions. This project will focus on phytoplankton in the Arctic Ocean, an ecosystem which is warming at twice the global rate and undergoing rapid changes, and seeks to characterize major patterns in phytoplankton ecology and behavior.

This project will use “The Gravity Machine” (https://gravitymachine.org), a vertical sinking column with a tracking microscope, to characterize phytoplankton sinking behaviors under various environmental conditions. Results may help explain the formation of the subsurface chlorophyll maximum, a persistent feature in the Arctic where phytoplankton congregate at a depth of 10s of m below the surface. Many theories explaining the subsurface chlorophyll maximum have been proposed, but they are difficult to test in the field, so we seek to apply new single-cell tracking technology to test these potential mechanisms in the lab. Altogether, a better understanding of phytoplankton behavior may help explain how phytoplankton in the Arctic will respond to climate change.

Interested students from any field are encouraged to apply, including but not limited to earth sciences, ocean sciences, and biology. A student would develop experimental design, laboratory, and data analysis skills.

Skills/Interest/Background: Biology, Lab Work

Memoirs of a Mineral: Unraveling records of magma storage preceding silicic volcanic eruptions

SESUR, SURGE
Category(s): Dynamic Earth, Evolution of Earth, Natural Hazards
Department: Geological Sciences
Faculty: Ayla Pamukcu, Faculty: Sarah Hickernell

Volcanic eruptions pose a significant hazard to society, particularly when they are explosive. Magma viscosity is correlated with magma silica content and explosivity, such that silicic magmas (~>65 wt% SiO2) are particularly prone to producing dangerous, explosive eruptions. Volcanoes that erupt such magmas have generated some of the largest and most explosive eruptions in Earth’s history. Identifying and monitoring potentially eruptible silicic magma bodies in the modern Earth’s crust via geophysical observations (e.g., seismic studies) has proven challenging. Therefore, to understand and predict the future behavior of active silicic magmatic systems, scientists must utilize the rock record left behind by past eruptions. The minerals that grow in magma before an eruption and are preserved in erupted rocks can provide especially rich information about a magmatic system. In particular, the chemical compositions of minerals can reflect important information about magmatic conditions, such as temperature, pressure, and magma composition. Furthermore, minerals grow outward, developing layers like the rings of a tree, such that we can use core-to-rim variations in mineral chemistry of individual crystals to investigate changes in magmatic conditions through time.

Ultimately, the goal of the project is to better understand the behavior of eruptible silicic magmas in the crust. The specific approach will be to assess the conditions of silicic magma storage which led to eruption via mineral geochemistry. The student will acquire data via analytical instrumentation on campus to characterize mineral chemistry from silicic volcanic rocks from Nevada. Following data analysis, the student may apply various petrologic tools, including but not limited to geothermometers and geobarometers, to obtain critical information about silicic volcanic systems and the magmatic storage conditions that lead to eruptions.

We are looking for an enthusiastic student with background knowledge in Earth sciences and chemistry, who has quantitative skills and experience with Excel or other programs/coding languages useful for data manipulation and plotting. Experience with mineralogy and igneous petrology is helpful, but not required. 

Skills/Interest/Background: Chemistry, Computer Programming, Engineering, Geology, Lab Work, Scientific Programming, Statistics

Carbon content of Colombian peatlands

SESUR, SURGE
Category(s): Climate Change, Freshwater
Department: Earth System Science
Faculty: Alison Hoyt, Faculty: Scott Winton

Peatlands are ecosystems with soils composed almost entirely of partly decomposed organic matter. They cover just 3% of the world’s land surface, but store as much carbon as all the world’s trees and terrestrial vegetation. Peatlands in tropical areas are sensitive to disturbance and if they are drained or burned, carbon lost to the atmosphere is irrecoverable because peat takes hundreds or thousands of years to form. Protection of peatlands is, therefore, a global priority for climate change mitigation, but conservation planning is difficult because of a poor understanding of peatland distributions in tropical areas, especially South America. For example, the two published estimates of peat volume in Colombia vary by two orders of magnitude (3 to 327 km3).

Knowledge of tropical peatland distributions and carbon stocks must improve in order to develop natural climate solution strategies involving peatlands. This project focuses on how much carbon is stored in Colombian peatlands.

We are seeking an enthusiastic student to work in the terrestrial carbon laboratory with peat cores recently collected from swamps in the Colombian Amazon. Activities will include, grinding soil in a ball mill, weighing soil subsamples on a microbalance, packing analytical tins and operating lab instruments. Previous experience with soils or in a laboratory setting may be useful but is not required. Students will visualize results using data software. R is preferred, especially if the student has a background or interest in learning coding skills, but Excel is also acceptable if that is what the student prefers.

Skills/Interest/Background: Chemistry, Lab Work, Statistics

Reaction Modeling and Analysis for Carbon Dioxide and Hydrogen Storage

SESUR, SURGE
Category(s): Climate Change, Energy
Department: Energy Resources Engineering
Faculty: Ilenia Battiato, Faculty: Kyle Pietrzyk

Due to approaching decarbonization and net-zero emission targets, considerable attention has been given to advancing methods of carbon dioxide (CO2) and hydrogen (H2) storage. In a theorized clean energy economy, CO2 and H2 are securely stored underground in naturally occurring formations due to various trapping mechanisms involving physical impedances and chemical reactions. However, immense variability in the physical regime, pore-scale geometry, and geophysical reactive chemistry exists in such systems, making them elusive to modeling efforts, but fruitful in opportunities for further model development and analysis.

Activities in this project revolve around model development and analysis for reactive transport in geological CO2 and H2 storage systems. We are interested in the utilization and improvement of our state-of-the-art, multi-scale model development code, Symbolica, for analyzing the complex reactions present in such systems. In particular, we look to investigate 1.) how side reactions in geological storage systems affect reactive timescales and chemical outcomes, and 2.) how large scale, geological temperature gradients affect reactive physics.

We are seeking an enthusiastic student to research the reaction networks, pore-scale geometries, and typical physical parameters relevant in geological CO2 and H2 storage systems. Reactive transport models will then be generated using Symbolica and solved using numerical analysis. Students with experience in Mathematica and python are preferred, and basic knowledge in CO2 or H2 sequestration and reactive systems is desired, but not required. Experience conducting literature reviews and working independently is also a plus.

Skills/Interest/Background: Chemistry, Computer Programming, Engineering, Mathematics, Scientific Programming

Carbon Dynamics of a Mixed Coral-Seagrass Ecosystem in Palau

SESUR
Category(s): Climate Change, Ocean
Department: Earth System Science
Faculty: Rob Dunbar

Even if the anthropogenic CO2 emissions are greatly reduced over the next 20 to 30 years, modeling studies show that we must deploy large-scale and effective carbon dioxide removal (CDR) technologies if we wish to prevent the most dangerous outcomes of future climate change. Blue CDR refers to C uptake by marine systems. Natural coastal systems such as algal and seagrass beds, mangrove forests, estuaries, and coral reefs are already known to process large amounts of marine C, and research on whether management practices can enhance coastal blue CDR is accelerating.
This project involves working with a team of Stanford faculty, staff, and students to conduct in-situ measurements of C system transformations in mixed coral reef/seagrass communities in the Republic of Palau. This work builds on several years of pilot projects and can be linked with participation in the BOSP Stanford Palau class offered during the early summer quarter 2022. We note that at the moment the university has given us a green light to teach the BOSP undergraduate course in Palau next summer and we expect to support a limited number of undergraduate research projects after the course ends. The SESUR student(s) will participate in fieldwork on shallow marine reefs and seagrass beds as well as analytical work in a lab in Palau. If covid conditions require remote work only, Dunbar will aid the student in the development of mixed biophysical/biogeochemical models of these systems – the kinds of models that are eventually used to determine the cost-effectiveness of specific coastal CDR strategies.

Skills/Interest/Background: Biology, Chemistry, Field Work, Lab Work, Physics

Human Dimensions of using Marine Environmental DNA to Monitor Ocean Biodiversity

SESUR
Category(s): Human Dimensions and Sustainability, Ocean
Department: Emmett Interdisciplinary Program in Environment & Resources
Faculty: Nicole Ardoin, Faculty: Meghan Shea

Scientists and conservationists are increasingly using environmental DNA (eDNA; the bits of DNA that organisms leave behind in the environment) as a way to measure biodiversity, including in ocean environments. eDNA has the potential to make monitoring marine ecosystems much easier and cheaper, but there are still many big scientific questions about what eDNA samples actually represent. In order to advance the use of marine eDNA, we need a lot of scientific work, but we also need a better understanding of how scientists, policymakers, and the public think about the promises and pitfalls of the technology.

We are seeking research collaborators to assist with one of several potential social science projects: 1) Scientific Perspectives of Marine eDNA: this project will involve assisting with transcription and coding of interviews conducted with interdisciplinary scientists participating in an oceanographic research cruise that involved eDNA data collection to better understand how different types of scientists think about the value of eDNA. 2) Media Representations of Marine eDNA: this project will involve systematically identifying media articles about marine eDNA, categorizing and recording the kind of information included in the articles, and analyzing that information to better understand how journalists depict the potential uses of marine eDNA. Research collaborators will learn more about the science of marine eDNA, gain experience with qualitative/quantitative content analysis, and if interested, contribute to drafting a manuscript on the project findings.

There are no required qualifications for these projects, but you would be an especially good fit if at least one of the qualifications below applies to you: Excitement to learn more (and read a ton of articles) about marine environmental DNA, experience conducting a large literature review, experience conducting semi-structured interviews of doing qualitative data analysis, interest in environmental communication, experience with statistical analysis. If you are interested in marine environmental DNA but not the particular projects outlined, there may be opportunities to design or contribute to other related projects, including projects involving scientifically collecting and analyzing eDNA samples in the field; please don’t hesitate to reach out to Meghan Shea.

Skills/Interest/Background: Biology, Statistics

BOULDERING: Assessment of meter-size rocks around impact structures on Moon, Mars, and asteroids

SURGE
Category(s): Dynamic Earth, Evolution of Earth, Natural Hazards
Department: Geological Sciences

Unless they were protected by thick atmospheres, the surfaces of planetary bodies in the Solar System have been bombarded by meteors through time, leaving behind an abundance of impact craters. Upon impact, rock fragments are ejected from the crater cavity and deposited elsewhere on the surface, where they may form secondary craters - a process that is poorly understood to date. In principle, the density of impact craters on a given surface allows planetary scientists to age that surface, provided that they can distinguish secondary from primary craters. Secondary craters increase crater density and distort crater statistics, which ultimately biases age estimates. Characterizing the size and velocity distribution of ejected rock fragments is a key unknown towards accurate age determinations.

Our group seeks to close that knowledge gap through quantitative measurements of boulder populations that can be detected on high-resolution spacecraft imagery of various planetary surfaces, such as the Moon’s, using deep learning. Specifically, our goal is to compile size and shape distributions of boulder populations on a variety of planetary bodies to evaluate the influence of impactor and terrain properties on the ejection mechanisms and its influence on the evolution of the surfaces of planetary bodies.

The focus of the project will be to map boulder populations from spacecraft imagery and to conduct size and shape analyses. The selected student(s) will compare their findings with previously published data and discuss whether their results support or contradict previous studies. This project is geared towards students interested in pursuing a graduate curriculum in planetary geosciences. Students will learn how to map rocks on planetary surfaces such as the Moon, Mars, and asteroids and analyze their distributions with the help of a Geographical Information System (GIS) software such as QGIS or ArcGIS. Students will also gain some familiarity with programming to conduct statistical analyses of their results. Interested students should have some basic programming skills (python) and interest in Remote Sensing / Geospatial data / GIS / Planetary Geosciences.

Skills/Interest/Background: Computer Programming, Geology, Machine Learning, Physics, Scientific Programming, Statistics

Tidal Heating of Habitable Red Dwarf Planets

SESUR, SURGE
Category(s): Dynamic Earth, Planetary Science
Department: Geological Sciences
Faculty: Laura Schaefer, Faculty: Matthew Reinhold

Terrestrial planets orbiting in the habitable zone around red dwarf stars have very short orbital periods relative to the Earth and thus are subjected to significant tidal effects.  If the planets have a non-zero eccentricity, one of these effects could be significant internal heating due to tidal dissipation and could equal or even surpass the tidal heating of Jupiter’s moon Io.  The habitability of such worlds is not well understood, and consequently, the tidal heating rates in such planets need further exploration.

This project will explore the tidal heating rates of different varieties of terrestrial planets in or near the habitable zone of red dwarf stars.  By using the virtual planet simulator VPlanet, the heating rates of planets with varying masses and core-mass fractions will be characterized as a function of eccentricity.  One of the biggest unconstrained parameters when predicting tidal dissipation rates is the material response to deformation.  Using the VPlanet software, this response will be tracked and predicted for the varying types of terrestrial planets.  

We are seeking a student who is interested in tackling planetary science problems using computer modeling.  Some familiarity with coding and remote computing will be useful:  The VPlanet software will be run on remote computing with the results most easily being analyzed through a local coding language, such as MATLAB or python.

Skills/Interest/Background: Geology, Physics, Scientific Programming

Remote sensing of smallholder agriculture

SESUR, SURGE
Category(s): Food and Agriculture, Human Dimensions and Sustainability
Department: Earth System Science
Faculty: David Lobell

Hundreds of millions of smallholder farmers around the world face challenges of climate change and inadequate soil nutrition. This project will work towards improving datasets used to study and inform management in these systems, with an emphasis on using new satellite sensors. Among the potential specific topics are: (i) estimating peanut yields and aflatoxin contamination in Malawi using high-resolution optical data (ii) estimating oil palm age in Nigeria using lidar, radar, and optical data, (iii) estimating soil erosion vulnerability in Ethiopian farmland using radar and optical data, (iv) predicting maize response to fertilizers in Kenya and Tanzania, (v) estimating canopy nutrient stress in India using optical data. The specific project will be decided closer to the summer based on student interests and data availability.

This is a good project for students with a strong coding background (especially R or python) and a desire to gain familiarity with remote sensing data and methods.

Skills/Interest/Background: Computer Programming, Machine Learning, Scientific Programming, Statistics

Developing the First Marine Nitrous Oxide Budget for the North American West Coast

SESUR, SURGE
Category(s): Climate Change, Ocean
Department: Earth System Science

Nitrous oxide (N2O) is a powerful greenhouse gas and the most prominent source of ozone depletion, today. In the ocean, N2O is produced and consumed by microbes and can be transported thousands of kilometers by ocean currents before being released into the atmosphere. The North America West Coast (NAWC) is a highly productive coastal upwelling region with dynamic chemical, biological, and physical characteristics, but its role in global N2O cycling is presently unknown. This project will support the first-ever N2O budget for the NAWC. Specifically, this project will help i) identify how N2O is being produced in the NAWC, ii) quantify how much N2O is released to the atmosphere, and iii) investigate how ocean physics plays a role in N2O dynamics in the region.

We are seeking an enthusiastic student interested in global biogeochemical cycling, climate change, and greenhouse gases. Interest in oceanography is a plus, but by no means required. Preferably, the student will have taken general chemistry and have basic coding experience (MATLAB, Python, or R).  The student will get to choose which region along the NAWC – from British Columbia, Canada to San Diego, California – they will focus their project on. Through this project, the student will learn fundamental laboratory techniques, including how to operate a mass spectrometer, as well as perform data analysis and visualization. 

Skills/Interest/Background: Chemistry, Lab Work, Scientific Programming

What works and what doesn't? Assessing Marine Protected Areas as Social-Environmental Systems

SESUR, SURGE
Category(s): Climate Change, Human Dimensions and Sustainability, Ocean
Department: Emmett Interdisciplinary Program in Environment & Resources, Department: Geological Sciences
Faculty: Rob Dunbar, Faculty: Ryan OConnor

The whole world is made up of Social-Environmental Systems of every scale and style. Among the most cherished, intricate, and delicate of these human-nature interactions are marine systems, where for generations humans have found resources, recreation, and cultural significance above and below the waves. Today these kinds of systems are under threat from global climate change, ocean acidification, pollution, overfishing, and so many other challenging stressors. The international community has responded, in part, by protecting portions of the ocean – some large, others small – through marine governance strategies like Marine Protected Areas (MPAs) and Marine Sanctuaries. But how do we know if these governance strategies are effective? This is the question that we are seeking to explain, and we need your help.

A team of Ryan O’Connor, a marine social ecologist and PhD student in E-IPER, and Professor Rob Dunbar, Geological Sciences, is engaged in an ongoing effort seeking to understand how MPAs and Marine Sanctuaries around the globe are assessed, what defines success and failure, and what kind of implications these strategies and definitions have on the development of marine governance. As an integral part of this team, the research intern would develop a deep and valuable awareness of the literature surrounding marine governance, gain expertise in novel and emerging strategies for determining success in MPAs and Marine Sanctuaries, and contribute to the growing knowledge base of how marine governance interacts with a changing climate and a warming ocean. 

Our team is seeking a student with excitement and enthusiasm for marine science and policy and a willingness to dive deep into the subject through literature review, specific case studies around the world, and the possibility for fieldwork assessing existing MPAs. While comfort with scientific literature is helpful, no prior experience in the field is necessary, and all perspectives and backgrounds are welcome to apply. Even if you’re completely new to the Environmental Sciences or to the Ocean and want to learn more, we’re interested in hearing from you! 

Skills/Interest/Background: Biology, Field Work, Lab Work, Statistics

How Do Mass Extinctions Change Ecosystems?

SURGE
Category(s): Climate Change, Evolution of Life, Ocean
Department: Geological Sciences
Faculty: Jon Payne, Faculty: Jood Al Aswad


As a result of climate change, many species are expected to shift their geographic ranges or go extinct over the next century. Those range shifts and extinctions will alter the structure of ecosystems globally. Similar changes have occurred in deep time, and the fossil record can be used to anticipate the pattern and magnitude of such changes.

For this project, we will quantify how the spatial continuity of ecosystems changes across mass extinction events, and test whether that change results from environmental change versus ecological interactions. If necessary, this project can be conducted remotely: most (if not all) of the work will be done using a computer and the R and R Studio software. The student will work with data on the biogeography of marine invertebrate animals before and after the end-Cretaceous extinction event.

We are seeking enthusiastic students who are interested in conducting research in biology, geology, paleontology, or other related fields. No prior coding experience is necessary. The student will develop skills in study design, coding, and data analysis.

Skills/Interest/Background: Biology, Computer Programming, Geology, Scientific Programming, Statistics

Rightsizing direct air capture: Policy and resource needs for an ambitious U.S. carbon removal strategy

SESUR, SURGE
Category(s): Climate Change, Energy, Food and Agriculture, Freshwater, Human Dimensions and Sustainability
Department: Earth System Science, Department: Emmett Interdisciplinary Program in Environment & Resources

Meeting ambitious climate goals, including limiting warming to 1.5°C, increasingly requires permanently removing past emissions from the atmosphere at a large scale. Despite the significant assumed capacity for carbon dioxide removal (CDR) in global models, its technologies and practices remain underdeveloped compared to other climate solutions like renewable energy. As the largest cumulative emitter in history, the U.S. bears the responsibility to develop and deploy a portion of the CDR necessary to meet global climate goals enshrined in the Paris Agreement.

Our project focuses on direct air capture (DAC), a CDR approach that captures CO2 from ambient air. In its recent bipartisan infrastructure deal (the “Infrastructure Investment and Jobs Act” of 2021), Congress set aside over $3.5 billion for U.S. DAC deployment. Advocates hope that any investment of this scale will help lower the costs of DAC and contribute to a burgeoning negative emissions economy. But how much DAC-based CDR should the U.S. aim to achieve? Is Congress’s recent investment enough? And what impacts on energy, water, land, food systems, and the economy would DAC have at scale?

A student researcher would assist in answering these questions using the Global Change Analysis Model (GCAM): an integrated, multi-sector model that integrates human and Earth system dynamics. Together, we would evaluate local, national, and global human and environmental effects of DAC deployment at various scales. Ultimately, the goal is to inform policymakers, industry, and communities of the potential costs and benefits of DAC. We are seeking an enthusiastic student interested in using quantitative methods to help guide ambitious, equitable climate policy. Basic coding experience (Python or R) is preferred but not required.

Skills/Interest/Background: Computer Programming, Engineering, Machine Learning, Scientific Programming, Statistics

Toxic Soil: Assessing Heavy Metal Contamination in Urban Gardens across the San Francisco Bay Area- Using Soil Biogeochemistry to Combat Environmental Injustice

SESUR, SURGE
Category(s): Food and Agriculture, Human Dimensions and Sustainability
Department: Earth System Science
Faculty: Scott Fendorf, Faculty: Alexis Wilson

Urban agriculture can be defined as areas within cities used for growing crops or raising small livestock, either for personal consumption or sale. Various forms of urban agriculture exist including home, community, and school gardens. Urban gardens are important to communities of color in particular because they are spaces for spiritual healing, building and sustaining communities, social and environmental activism, and reconnecting with land and culture. The combination of urban food insecurity and soil contamination becomes an Environmental Justice issue because research indicates that communities of color (Black, Indigenous, Latino/Hispanic, Asian/Pacific Islander) and low-income communities disproportionately face food insecurity and exposure to environmental pollution.

Because these sites exist in urban areas, there is a risk that gardens may be built on sites with contaminated soils or are actively being contaminated by various sources. Heavy metal(loid)s, such as lead (Pb), cadmium (Cd), chromium (Cr), and arsenic (As) are common urban soil contaminants due to anthropogenic activities which raise contaminant concentrations such as past and current industrial activity, hazardous waste, and metal-containing products (e.g. leaded paint). While researchers have broadly characterized overall patterns of soil contamination in urban areas, levels of metal contamination and exposure threats are still lacking for urban agricultural spaces. As such, the overall goal of this research is to use an interdisciplinary approach, using Environmental Justice and Community-Based Participatory Research as foundational frameworks, to advance our understanding of the threat of soil contamination to urban gardens across the San Francisco Bay Area, specifically focusing on marginalized communities. This research is conducted in partnership with community organizations across the Bay Area, and includes field and lab work using quantitative and qualitative techniques.

This project provides a wide variety of opportunities for involvement, and we will work with students to define a plan for summer research based on their interests and experience. The student will either collect and analyze samples from urban gardens or conduct and analyze qualitative interviews of urban gardeners. Potential focus areas include fieldwork (soil and plant sampling), lab work (measuring heavy metal concentrations, soil pH, soil chemistry), qualitative interviews, data analysis (comparing results across sites, creating charts and graphs), and developing/maintaining community partnerships, etc. Cities we may work in include Oakland, Richmond, Palo Alto, and East Palo Alto, and San Jose, California.

A student project might look like one of these examples:
Students may: Choose a subset of urban garden sites and develop a relevant research question around heavy metal concentrations in soil and/or plant samples and other variables such as neighborhood demographics (race, income, gender), environmental conditions (atmospheric pollution), proximity to pollution sources, etc. Activities may include: fieldwork (collecting soil and plant samples at urban gardens), lab work (preparing samples, analyzing for heavy metals , measuring soil characteristics (pH, water content)), geospatial mapping, etc. If remote: activities would focus on data analysis and presentation (graphical representations of the data) rather than field and lab work OR Assist in interviewing urban garden users and managers focused on the topic of their knowledge and perception of the risk of soil contamination and analyzing the resulting data. Activities may include: recruiting participants, interviewing participants (in person or remotely), distributing surveys and analyzing the data, geospatial mapping, etc. If remote: activities can all be done remotely.

Interested students from any field are encouraged to apply, including but not limited to earth sciences, engineering, and social science/humanities. Experience or interest in soil science, environmental justice, community-based research, food and agriculture, and/or climate science would all be beneficial to the project.

Skills/Interest/Background: Chemistry, Field Work, Lab Work

Exploring Motivations and Support Systems of Youth Climate Activists in the Bay Area

SESUR, SURGE
Category(s): Climate Change, Human Dimensions and Sustainability
Department: Emmett Interdisciplinary Program in Environment & Resources
Faculty: Nicole Ardoin, Faculty: Alison Bowers

Successfully addressing climate change issues requires collective effort at multiple scales. The youth climate movement represents one piece of the solution. In this project, we will conduct semi-structured interviews with youth climate activists to examine their motivations and support systems.

We seek to gain insight into the mechanisms required for individual and collective action and to identify the role of emotions in response to climate change. Our research will help inform how organizations and policymakers might more effectively interact with youth climate activists. We seek a student to collaborate with our team to first help with the identification of youth climate activists in the San Francisco Bay Area. Building on pilot interviews conducted in previous summers, we will conduct interviews with identified youth climate leaders and analyze the interview data to identify emergent findings to inform future research and practice.

Other than an interest in climate change, human behavior, and the social sciences, no prior experience is needed. We welcome students new to Earth and environmental sciences and will provide training in qualitative research methods, interviewing, and qualitative data analysis.

Skills/Interest/Background: Field Work, Lab Work

Examining impacts of drought-induced air pollution changes on human health

SESUR, SURGE
Category(s): Climate Change, Human Dimensions and Sustainability, Natural Hazards
Department: Earth System Science
Faculty: Marshall Burke, Faculty: Minghao Qiu

2021 is one of the driest years we have ever seen. Scientists project that many places in the world (e.g., Western US and much of Africa) will experience increasing drought risks. Most prior research that studied the impacts of droughts on human health has studied the “direct” impacts, e.g. through failed crops and poor nutrition. We propose to study the potentially very large indirect effects, including the increased likelihood of wildfires and large dust storms that can dramatically increase air pollution. 

This work will involve working with satellite data measuring climate and air pollution and applying methods from quantitative social sciences. With guidance from research mentors, the student will prepare and analyze data and assist with visualizing and interpreting results. Depending on the student's interests, there is flexibility in the geographical focus of the project (e.g., US or Africa) . We are particularly interested in students seeking to build data analysis skills in R or similar statistical packages (you don't need to have extensive experience, but familiarity with a statistical package is helpful).

Skills/Interest/Background: Computer Programming, Machine Learning, Scientific Programming, Statistics

Wildfire generated toxins and ecosystem recovery

SESUR, SURGE
Category(s): Human Dimensions and Sustainability, Natural Hazards
Department: Earth System Science

Elevated temperatures coupled with extended drought resulting from climate change have increased wildfire risks across California. Thirteen of the twenty most destructive wildfires on record have occurred within the past five years and are expected to increase in frequency and intensity. In addition to acute localized fire hazards, degraded air quality from smoke and dust impose disseminated and prolonged health risks. One large component of wildfire smoke and dust is derived from the combustion of plant material; however, ash compositional differences and their associated exposure risks based on vegetation type and fire severity are poorly understood. Plant ash can contain concentrated toxic metals that originate from soils.  When inhaled as smoke or dust, they can bring about harmful health effects. Additionally, the plant ash and thermally transformed surface soils represent the growth media for rebounding microbial communities. Physical-chemical properties of the plants and soils post-fire can affect bacterial and fungal community composition.

We have two projects where we ask: (1) how does varying fire severity and vegetation type affect ash toxicity? and (2) how does post-wildfire soil and ash influence microbial communities that serve as the basis for ecosystem recovery? 

We seek two summer researchers to work on already established field sites in Sonoma, Napa, and Lake counties across the North Coast Range of California. Both projects include a wide range of soil analyses, such as lab extractions to measure metal content, and microscopic analysis of ash and soil particles. The projects diverge, however, with one (“plant project”) focusing on the plant species (and resulting ash) and the other (“microbial ecology project”) on the soil microbial communities serving as the basis for ecosystem recovery. For the plant project, additional lab-based measurements will include homogenizing and ashing different vegetation types, analyzing ash microscopically, and estimating toxicity by simulated lung fluid extractions. For the microbial ecology project, additional techniques include isolating, culturing, and identifying fungal/bacterial communities in a range of post-wildfire soils using PCR and DNA sequencing. From pure cultures, we will archive representative isolates that can be applied for future recovery initiatives in wildland soils and plants. In both projects, field and lab experience will be gained but not required to apply. Some basic chemistry and biology coursework will be useful but by no means necessary, your mentors will help guide and teach you the skills necessary to continue as an independent researcher.

Skills/Interest/Background: Biology, Chemistry, Field Work, Geology, Lab Work

Understanding satellite measurements of vegetation water content for improved characterization of vegetation drought response

SURGE
Category(s): Climate Change, Natural Hazards
Department: Earth System Science
Faculty: Alexandra G. Konings, Faculty: Meng Zhao, Faculty: Nathan Holtzman

Ecosystems are threatened by more common and more extreme droughts in a warming climate. Drought dries out plant tissues, reducing growth and photosynthesis and eventually causing plants to die. Thus, monitoring changes in vegetation water content (the water content of the plant tissues) has enormous potential for better understanding vegetation response to drought. Traditional measurements of vegetation water content are carried out on individual plants, and they are cost- and labor-intensive. Fortunately, advances in satellite microwave remote sensing techniques provide unparalleled spatial and temporal coverage to measure vegetation water content from space. However, many questions remain regarding how to interpret these measurements and their accuracy.

In this project, the student will have an exciting opportunity to compare satellite microwave observations sensitive to vegetation water content with a series of ground measurements and other remote sensing measurements, with the end goal of improving our understanding of the information that these microwave datasets provide as well as their limitations. If progress allows, this research could be presented at a research conference and/or journal publication. The ideal candidate will have experience manipulating data using a programming language such as Python, R, or Matlab. Background knowledge regarding remote sensing or ecohydrology is a plus, though not required. We seek candidates with a strong interest in learning analytical skills applicable to data analysis in earth science. 

Skills/Interest/Background: Biology, Scientific Programming

Geological storage of CO2 and caprock sealing integrity

SESUR, SURGE
Category(s): Energy
Department: Energy Resources Engineering
Faculty: Tony Kovscek, Faculty: Arash Kamali-Asl

One approach to combating climate change is sequestering large volumes of CO2 that would otherwise be released to the atmosphere. This approach could be implemented over relatively short timescales. For this purpose, earth scientists use geological formations that have a very large storage capacity for CO2. Saline aquifers and depleted oil/gas reservoirs are among the top choices as storage formations because they have physical properties suited for injection of large volumes of CO2. It is most important that there is a good caprock (i.e., low permeability rock) above the target formation so that the injected CO2 cannot migrate upward and is trapped in place. These caprocks may have multiple cracks or faults in them that facilitate the gradual migration of CO2 away from the storage formation. On the other hand, when the CO2 is injected into a storage formation made of sandstone, it reacts with the minerals of the rock (mainly composed of quartz) as well as the in-situ water. Therefore, a CO2-saturated brine is produced that could be under- or super-saturated with respect to silica. The focus of this work is on interactions between the produced brine and shaly caprock. We hypothesize that the flow of this brine through the shale cracks or fractures could alter the overall permeability of the caprock, and hence, control the amount of CO2 that could be injected.

We are looking for a motivated, hands-on undergraduate student to conduct and analyze flow-through experiments in our rock mechanics laboratory. The major task for this project is to collect high-quality laboratory data of caprock corrosion on a few samples with different mineralogical contents. You will learn how to analyze a fracture surface as well as well as elemental composition of the inlet and outlet fluids. Data analysis is a significant part of the study. No prior experience is needed for advancing the project, just be passionate about what you do.

Skills/Interest/Background: Chemistry, Engineering, Lab Work

Computational modeling of volcanic eruptions, earthquakes, and other natural hazards

SESUR, SURGE
Category(s): Dynamic Earth, Natural Hazards
Department: Geophysics
Faculty: Eric Dunham

Volcanoes, earthquakes, and tsunamis pose immediate hazards to human society. Our research group develops computational codes to model a wide range of physical processes associated with these hazards. Summer interns have opportunities to apply the theory of mechanics to understand these natural hazards. Students who enjoy mathematical analysis can help to derive equations and solve analytical problems; experience with differential equations, continuum mechanics, and Fourier transform would be useful. Students who enjoy programming can assist with code development; prior programming experience in MATLAB, Python, C++, or another language is required. A strong background in mechanics and/or programming is a must for all applicants since all projects involve computer simulations of solid and fluid mechanics problems. Previous experience with earth science is not required.

Skills/Interest/Background: Computer Programming, Engineering, Mathematics, Physics, Scientific Programming

Nitrogen fixation in the canopy: Exploring cryptic nitrogen sources in the boreal forest

SESUR
Category(s):
Department: Earth System Science
Faculty: Rob Jackson, Faculty: Peter Pellitier


The boreal forest ecosystem is the largest contiguous ecosystem on Earth. However, climate change, in particular warming and increasing carbon dioxide concentrations, threatens the persistence and health of these forests. Nutrients such as nitrogen strongly limit the growth of these forest ecosystems. Understanding how boreal forests obtain nitrogen is critical to predicting how the forest will continue to offset anthropogenic emissions. Symbiotic nitrogen fixation represents a major input of nitrogen into the forest system, yet has very rarely been studied in the canopy where endophytic fungi and bacteria inhabit tree needles.

We are looking for a motivated student to develop and lead molecular techniques to extract DNA from spruce needles, in order to quantify the microbial communities that dwell within. Using this purified DNA pool, the student will quantify the abundance of genes involved in nitrogen fixation. The student will also utilize bioinformatic pipelines in R to process the sequence data. This project is set provide new insights into a cryptic nitrogen cycling pathway. The student can be expected to gain a wide array of skills in molecular biology including PCR, quantitative PCR, gel electrophoresis, bioinformatics and statistics. This project is best suited to a student with prior molecular biology experience or coursework.

Skills/Interest/Background: Biology, Computer Programming, Lab Work, Scientific Programming, Statistics

Stinson Beach Microbiome: diversity and function of bacteria and archaea in a subterranean estuary

SESUR, SURGE
Category(s): Climate Change, Freshwater, Ocean
Department: Earth System Science

At the land-sea interface, sandy beaches are among the most vulnerable ecosystems to the effects of global climate change. Sea level is projected to rise between 0.5 and 1 meter by 2100, critically endangering their very existence. Sandy beaches provide essential ecosystem services, including habitat for sensitive biota, protection of land from storm events and flooding, and water filtration and purification. Forced by tides and waves, large volumes of seawater are flushed through the beach daily. In addition, meteoric groundwater can percolate through the beach and discharge to the sea. In the subsurface of the beach is a subterranean estuary with steep chemical (salinity, oxygen, nutrient) and physical (temperature, moisture content) gradients. The beach can be viewed as a “biogeochemical reactor” where microorganisms mediate the transformation of nutrients, trace metals, and carbon within the subterranean estuary. 

Our research group is interested in characterizing the chemical and physical drivers of microbial diversity and metabolism within the subterranean estuary at Stinson Beach in Marin County, CA. As a student working on this project, you will hopefully have the opportunity to assist with field research and community outreach at Stinson Beach. You will gain fundamental skills in sample collection, laboratory techniques (e.g. DNA extraction and sequencing), and data analysis (e.g. bioinformatics and multivariate statistics). Based on your interests and experience, we will help you define an exciting and manageable summer project. Prior laboratory experience is helpful, but not required. If you have a general interest and enthusiasm for environmental science, microbial ecology, biogeochemistry, or molecular biology, please consider applying.

Skills/Interest/Background: Biology, Chemistry, Field Work, Lab Work, Scientific Programming, Statistics

Planned relocation as climate adaptation: Are destination sites safer than origin sites?

SESUR, SURGE
Category(s): Climate Change, Human Dimensions and Sustainability, Natural Hazards
Department: Earth System Science, Department: Emmett Interdisciplinary Program in Environment & Resources
Faculty: Chris Field, Faculty: Erica Bower

Facing mean and extreme sea-level rise and other coastal hazards, communities across the world are considering options to reduce their exposure, as well as their vulnerability. The planned relocation of whole communities to less exposed areas is gaining salience as a strategy for both disaster risk reduction and climate change adaptation. While planned relocations may have multiple objectives, and occur in the context of multiple drivers, the primary goal is generally assumed to be a reduction of hazard exposure. Yet little empirical research assesses how effective planned relocations are at reducing exposure, and on what time horizon.

Through spatial analysis of comparing coastal planned relocation cases, this project assesses the distance and change in elevation between origin and destination sites and compares this with global sea-level rise projections over time. This project also considers why destination sites are selected, and the role of risk assessments in informing this process, including exploring who initiates and implements assessments, the hazard types, and timelines considered, types of knowledge integrated, and level of community engagement.

We are looking for a student who is both quantitative and qualitatively oriented, and who has experience, or is willing to gain experience, with spatial analysis.

Skills/Interest/Background: Computer Programming

Imaging the San Andreas Fault in three dimensions with gravity and seismic data

SESUR, SURGE
Category(s): Dynamic Earth, Evolution of Earth, Natural Hazards
Department: Geophysics

Do you want to understand how the Earth behaves beneath our feet? And explore how 100 million years of plate-tectonic evolution have created the complex three-dimensional pattern of faults beneath Los Angeles and southernmost California? Do you enjoy working with and visualizing large datasets? The geophysics community has developed multiple competing seismic-wavespeed models with resolutions of 1 to 10 km horizontally and vertically, depending on the depth of interest. These different models lead to different predictions of subsurface geology, for example including the dip of the San Andreas Fault that strongly affects the predicted seismic-energy radiation pattern in the next “Big One” in southern California. You will learn about and download these models (e.g. agupubs.onlinelibrary.wiley.com/doi/10.1002/2015GC005970). Seismic wavespeed is a sufficiently good proxy for rock density that one test for the validity of these models is whether they predict the observed gravity field.

You will learn to use commercial 3d gravity modeling software ( www.geosoft.com/products/gm-sys ), then convert the wave speed models into density models (e.g. pubs.geoscienceworld.org/ssa/bssa/article/95/6/2081/146858 ) and re-format or sub-sample (filter) appropriately for use with this software. Your goal is to assess which wave speed model most accurately predicts the observed gravity field (e.g. web.gps.caltech.edu/~clay/gravity/gravity.html ) in particular over areas such as the southern San Andreas fault and Salton Trough. Can we test between specific geologic hypotheses (how steep is the San Andreas fault? how much magma is in the lithosphere beneath the Salton Trough?). The ideal student would be motivated to carry this through to a presentation at a professional meeting.

Skills/Interest/Background: Computer Programming, Engineering, Field Work, Statistics

Forecasting urban water quality using machine learning

SESUR, SURGE
Category(s): Energy, Food and Agriculture, Freshwater, Human Dimensions and Sustainability
Department: Earth System Science
Faculty: Kate Maher, Faculty: Zach Perzan


Nitrogen is one of the most ubiquitous, costly, and challenging environmental pollutants of the 21st century. At the same time, fertilizer production will struggle to meet the needs of a growing population. A new interdisciplinary research team at Stanford aims to tackle these two challenges by converting waterborne nitrogen pollutants into high-purity fertilizer.

Collaborators in the Dept. of Chemical Engineering have developed a new device for exactly this purpose; our research group is focused on forecasting water quality flowing into the device, so it can change its operation according to the forecast and optimize nitrogen removal. Using real-world water quality data from our industry partners (the water utilities of New York City and San Francisco), this project aims to forecast short-term nitrate concentrations based on current conditions. Some of the specific questions include: what water quality variables are most closely correlated with nitrate and nitrite? Does nitrate respond to any environmental variables (precipitation, temperature)? What statistical techniques (autoregression, exponential smoothing, long short-term memory network) are most suited for nitrate forecasting? In this project, the research student will develop strong data science and coding skills.

The project is open to students in any science background (including those outside earth or environmental sciences), though previous coding experience (Python, R, or Matlab) is preferred.

Skills/Interest/Background: Chemistry, Computer Programming, Machine Learning, Scientific Programming, Statistics

Mitigating Greenhouse Gas Leaks from Subsurface Seals

SESUR, SURGE
Category(s): Climate Change, Energy
Department: Geophysics
Faculty: Tiziana Vanorio, Faculty: Jihui Ding

Rising global concerns around the central role of greenhouse gases (e.g., carbon dioxide and methane) in climate change have impelled governments, scientists, and engineers to find ways of
reducing emissions. It is approximated that annual CH4 emissions from abandoned oil and gas wells are presently underestimated by 20% in the U.S. and 150% in Canada. Leaky wells result from property degradation of materials at interfaces, which tends to be major sources of leaking greenhouse gases. Unfortunately, the costs for a company to plug leaky wells are high and many of the available methods are unreliable. 
Thus, there is a clear need to pursue a renewed and sustainable effort aimed at well remediation and the long-term prevention of greenhouse gas leaks. This means we need to seek a win-win solution by achieving both mitigating greenhouse gas leaks and CCUS (carbon capture, utilization, and storge). It has become clear that CCUS costs should be discussed in the context of a broader energy system transformation to offset the costs of carbon removal with the practical benefits of climate change mitigation and CO2 reuse – whether the reuse is for energy production or material manufacturing.

At Rocks and Geomaterials Lab, we study rocks and material resources to marshal environmental solutions for fluid injection and storage in the Earth’s underground, energy sources, and manufacturing processes. In this project, we seek a highly motivated student to produce and characterize rock-cement composite samples representative of subsurface seals of interest. Because a major portion of greenhouse gas leaks come from cement-rock debonding, it is critical to create representative cement-rock composites for laboratory testing.

This project involves preparing samples, measuring sample porosity and permeability, and imaging microstructures of key samples. The student will be introduced to a wide range of laboratory techniques and closely mentored throughout the project to develop experimental and analytical skills. Prior lab experience is preferred but not required.

Skills/Interest/Background: Chemistry, Engineering, Lab Work, Physics

Development of an Interactive Website Interface for Site Selection Criteria of a Geological Carbon Storage

SESUR
Category(s): Energy, Human Dimensions and Sustainability
Department: Energy Resources Engineering
Faculty: Tony Kovscek, Faculty: Catherine Callas

Carbon capture and sequestration (CCS) will play a significant role in mitigating carbon emissions. One challenge is that CCS requires a long-term sequestration site capable of storing CO2 for the foreseeable future and having adequate capacity and injectivity. Many factors influence the suitability of a geological storage site ranging from the geology and geomechanical environment to injectivity and capacity parameters to the economics and social and political framework. All these factors need to be considered in the evaluation criteria. Developing a consistent methodology to screen potential geological storage sites is integral to the large-scale deployment of CCS technologies to ensure safe, secure, and economic sequestration.

This research project is to develop an interactive webpage that users can use to screen potential carbon storage sites. The site should be able to take user-uploaded data and score and rank potential sites based on developed site selection criteria. Potential outputs are heatmaps, maps of potential sites, chance of success charts, spider diagrams, and tables with the ranked sites.

We are looking for a researcher with a background in computer science. Experience with programming languages and website development is advantageous. Students will be mentored closely throughout the entire project.

Skills/Interest/Background: Computer Programming, Engineering
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