SESUR applicants - Stanford Students
If you are interested in learning more or getting involved in one of these projects, you should contact the faculty member and other mentors directly. This list is not comprehensive however, and many other projects are possible. Please visit this page often for project updates. Also, feel free to explore all our faculty research areas and contact anyone whose research interests you. For your reference, you can also view the project archives at the bottom of this page for an overview of previous year's submitted projects.
SURGE applicants - Non-Stanford Students
If you are interested in getting involved in one of these projects, please indicate so on your application. This list is not comprehensive however, and many other projects are possible. Feel free to browse the list of faculty research interests and indicate, on your application, anyone whose research interests you.
last updated on 1/28/2020
Investigating the complex social-ecological system of invertebrate fisheries in Palau
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.
Heavy metal, rainfall and groundwater: What are the connections?
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.
Measuring the rotation rate of Jupiter's moon Europa
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.
Radar System Development for High Altitude Ice Sheet Sounding
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.
Linking food, water and energy in the rapidly urbanizing city of Pune in India
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.
Mapping historical radar observations from Antarctica
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.
Investigating protective health decision-making in response to wildfire smoke in California
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.
Understanding nitrous oxide production in the tropical Pacific Ocean with isotope measurements
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.
Who makes nitrite in the Primary Nitrite Maximum? Investigating nitrite cycling in the upper ocean using stable isotopes.
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.
Erosion and stream formation in desert landscapes
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.
Do bacterial symbionts contribute to sterol biosynthesis in marine invertebrates?
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.
Microscale investigation of bubble dynamics in porous media
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.
Structural and electronic response of swift heavy ion irradiated iron oxides
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.
Understanding the role of oxygen and temperature change on the marine biota in the Northeast Pacific
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.
Analyzing seismic data recorded by optical fibers in urban environments
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.
A Great Stromatolite Reef: correlating ~2 billion year old stromatolites reefs in Hudson Bay, subarctic Canada
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.
Microbiology and Geochemistry of Deep-Sea Methane Seeps
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.
Understanding changes in the North American monsoon rainfall and circulation in CMIP6 projections
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.
Understanding and mitigating climate risk to vulnerable urban communities in the San Francisco Bay Area
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.
Soil Carbon Protection in Agricultural and Natural Soils
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!
Scaling investments in wastewater-to-fertilizer technologies
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.
Crop yield impacts of wet springs
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.
Deep transfer learning for crop field segmentation
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.
How is the use of California forests changing?
Environmental Justice in Urban Tree Cover
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.
Understanding Sustainable Supply Chain Practices in the Information and Communications Technology Sector
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 email@example.com.
The geologic history of water and life in the Amazon Basin
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.
Investigations in Optimizing a Forced Air Static Composting System
Using Psychology and Neuroscience to Study Environmental Attitudes and Behaviors
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.
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.
Evaluating the fate of hurricane outflow air in a global simulation
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.
Healthy soil, healthy crop: remote investigation of soil-yield relationships in Mexico
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.
Using Data Assimilation to Improve Carbon Cycle Model Predictions
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.
Understanding drivers of large wildfire in western USA
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.
Estimating root-zone soil moisture from radar remote sensing using neural networks
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.
Impact of climatic and soil stressors on rice production in South and Southeast Asia
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.
Predicting soil arsenic levels within rice paddies using remote sensing
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.
Examining environmental exposure disparities in California
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).
Can satellite observations of soil moisture be used to forecast maize growth in East Africa?
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.
Investigating traffic congestion and emissions to make equitable policy
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.
Statistic Learning of Fluid Properties
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.
Analyzing the impact of collaborative freshwater governance regimes on environmental outcomes
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.
Imaging the San Andreas Fault in three dimensions with gravity and seismic data
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.
Remotely sensing arsenic within rice using historic land use changes in Arkansas
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.
Walking the Razor’s Edge: Exploring hope and concern in response to climate change
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.