Alexandra Konings
Stanford University, CA·Earth & Environmental Science·Early-career
Computer models of land ecosystems are one of the biggest sources of uncertainty in predicting climate change. These models must describe how plants and soils respond to their environment, for example how much carbon dioxide plants take up or how fast soil organic matter decays.
- Fields
Earth & Environmental Science
Ecology & Evolution
Mathematics & Statistics
- Topics
terrestrial ecosystem models
carbon cycle
data assimilation
- Grants
- 1 known grant, through Jan 2027
What they work on
- Use the CARDAMOM data assimilation framework, which combines a simple ecosystem model, satellite data and Markov Chain Monte Carlo sampling, to map optimal plant and ecosystem parameters for each pixel across the globe.
- Test whether parameter maps derived from satellite data can reveal environmental filtering relationships that predict ecosystem parameters from climate, soil clay content and other well-mapped factors, without relying on field measurements.
- Derive and demonstrate environmental filtering relationships for heterotrophic respiration, the carbon dioxide released as soil microbes decompose organic matter, whose spatial patterns field data alone cannot constrain.
- Determine whether applying these relationships improves model predictions of how much carbon dioxide ecosystems absorb.
More about the research
Most models assign these behaviors using a handful of broad "plant functional types" based on land cover. That approach ignores the huge variety of plant behavior around the world. Earlier work suggests that local conditions, such as how dry a place is or how much clay is in its soil, also shape how vegetation responds. This project asks whether satellite data, combined with a model, can reveal these relationships worldwide, including in regions where field measurements are scarce, such as the tropics.
Why it matters
Better rules for how plants and soils differ across the globe could make climate projections more accurate, including predictions of how much carbon dioxide ecosystems absorb, how plants grow and how fast soils decompose. This matters most for poorly sampled regions such as the tropics. It could help scientists and policymakers who depend on Earth system models to anticipate how ecosystems will respond to a changing climate.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Kevin Schaefer
University of Colorado Boulder, CO·Earth & Environmental Science
Quantify how different environmental conditions affect mercury accumulation in permafrost-region soils, with uncertainty estimates, by linking mercury pathways to existing models of the land carbon cycle.
Serita Frey
University of New Hampshire, NH·Earth & Environmental Science
Test whether decades of soil warming produce cycles of organic matter decay punctuated by shifts in the soil microbial community, continuing core measurements across Harvard Forest plots warmed 5 degrees Celsius for 13, 16 and 28 years.
Guiling Wang
University of Connecticut, CT·Earth & Environmental Science
Develop machine learning and deep learning models that predict global vegetation properties from selected climate variables, trained on satellite remote sensing, reanalysis data and CMIP6 Earth System Model output.
The school
Stanford University: 87 labs on PI Pages, $1.6B a year on research, 739 research doctorates a year.
Reach out
A start for your email. Fill in the brackets.