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Future Blog Post
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Blog Post number 4
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Blog Post number 1
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portfolio
Field demonstration of remote sensing of plant hydraulics
We measured microwave vegetation optical depth with a tower-mounted radiometer at Harvard Forest, while also measuring plant water potential within the radiometer field of view. The VOD and water potential displayed similar diurnal cycles. 
Portfolio item number 2
Short description of portfolio item number 2 
publications
Tailoring WRF and Noah‐MP to Improve Process Representation of Sierra Nevada Runoff: Diagnostic Evaluation and Applications
Published in Journal of Advances in Modeling Earth Systems, 2020
SMAP Detects Soil Moisture under Temperate Forest Canopies
Published in Geophysical Research Letters, 2020
L-band vegetation optical depth as an indicator of plant water potential in a temperate deciduous forest stand
Published in Biogeosciences, 2021
Global ecosystem-scale plant hydraulic traits retrieved using model-data fusion
Published in Hydrology and Earth System Science, 2021
Interannual variations of vegetation optical depth are due to both water stress and biomass changes
Published in Geophysical Research Letters, 2021
Detecting forest response to droughts with global observations of vegetation water content.
Published in Global Change Biology, 2021
Bias correction of hydrologic projections strongly impacts inferred climate vulnerabilities in institutionally complex water systems
Published in Journal of Water Resources Planning and Management, 2022
Constraining Plant Hydraulics With Microwave Radiometry in a Land Surface Model: Impacts of Temporal Resolution
Published in Water Resources Research, 2023
Investigating diurnal and seasonal cycles of Vegetation Optical Depth retrieved from GNSS signals in a broadleaf forest
Published in Geophysical Research Letters, 2024
Ecosystem Water-Saving Timescale Varies Spatially With Typical Drydown Length
Published in AGU Advances, 2024
research
Field demonstration of remote sensing of plant hydraulics
It’s difficult to validate the use of satellite observations of vegetation optical depth (VOD) to remotely sense vegetation water content, because of the large pixel size of microwave satellite data relative to ground truth data. We circumvented this scaling issue by using a microwave radiometer on top of a 90-foot tower looking down directly at a patch of forest, so its field of view was small enough to be homogeneous. VOD and leaf water potential displayed similarly shaped diurnal and seasonal patterns. This work was conducted at Harvard Forest with many collaborators including Alexandre Roy (U. Quebec Trois-Rivieres), Andreas Colliander (JPL), and Leander Anderegg (UCSB). Diurnal cycles of leaf water potential and VOD
Model-data fusion to map plant hydraulic traits
Plant hydraulic traits are being included in current land surface models, but the values of those traits at different places are largely unknown. With Alexandra Konings (Stanford) and Yanlan Liu (Ohio State), I combined a simple plant hydraulic model with observations from the AMSR series of satellites to estimate values of several plant traits around the world. We found that the spatial distribution of trait values does not correspond well to commonly used classifications of plant functional types. 
Map of model-data-fusion-based estimates of Medlyn’s g1, a parameter related to stomatal opening
Regional climate and reservoir systems
To understand future climate impacts on water storage and hydropower in California’s large system of reservoirs, it is necessary to downscale climate predictions from global models. Working with Tamlin Pavelsky at UNC-Chapel Hill, I studied how to best configure a commonly used regional climate model and land surface model (WRF and Noah-MP) to produce realistic outputs of runoff into California reservoirs. An interactive visualization of this work is available here. I have also worked with engineering researchers (Jon Herman at UC Davis) to study how biases in downscaled runoff are amplified when the model runoff is used in models of reservoir operations.
Inferring the times scale of plant water use planning
Stomatal optimality models are popular tools for predicting how plants adjust their water use in response to drought, but they typically make the unrealistic simplifying assumption that plants optimize a reward function at each instant independently. By developing a new model that integrates water use and photosynthesis over time, we can quantify the time horizon over which plants in different ecosystems conserve water. This work was a collaboration with Xue Feng’s lab at the University of Minnesota. 
Examples of stomatal optimality model predictions at two FLUXNET sites, with different values of the water saving time scale parameter (tau)
talks
Predicting lake depth from topography to map global lake volume
Published:
Ensemble simulation of Sierra Nevada snowmelt runoff using a regional climate modeling approach
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Towards microwave remote sensing of plant hydraulic behavior: Insights from a field campaign
Published:
Constraining Plant Hydraulics with Microwave Radiometry in a Land Surface Model: Impacts of Temporal Resolution and Wavelength of Observations.
Published:
teaching
Remote Sensing of Hydrology
Graduate course, Stanford University, Department of Earth System Science, 2020
I was the Teaching Assistant for “Remote Sensing of Hydrology” in Spring 2020, with Prof. Alexandra Konings as the instructor.
The Water Course
Undergraduate course, Stanford University, Department of Geophysics, 2022
I was one of two Teaching Assistants for “The Water Course” in Winter 2022, with Prof. Rosemary Knight as the instructor. This project-based course introduces students to current issues in water resources within the Central Valley of California.