Goal: using theory, lab/field experiments, observational data, and modeling to understand, quantify, and
predict vegetation dynamics under changing climate

Our research team has the following foci:

1. Tree mortality quantification and prediction
Mechanistic exploration:
We use field manipulation experiments [SUrvival-MOrtality (SUMO) experiments, video] to modify key environmental factors (rainfall and temperature) to assess their effects on key components of the system. The ultimate goal is to better understand the key mechanisms (i.e., hydraulic failure, carbon starvation, and insects) that lead to tree mortality.   In addition, we are developing new techniques to quantify water and carbon transport within plants.

Global mortality quantification
We use remote sensing approaches (Quickbird/Worldview, LANDSAT and MODIS) to detect/quantify tree mortality.  We have also developed an novel tree mortality monitoring system based on the fusion of different information sources, including a real-time mortality signal from remote sensing imagery, vegetation change information simulated from a vegetation dynamics model (ED), radiative transfer and reflectance information from a forest reflectance model (FRT), and different sources of background information from forest inventory and remote sensing products. The fusion of different sources of information makes it feasible for the first time to accurately quantify tree mortality using Moderate Resolution Imaging Spectroradiometer (MODIS) imagery from NASA, a common remote sensing tool for monitoring global earth system processes.

Mechanistic modeling
: We are incorporating and improving experimentally tested tree mortality mechanisms and implementing them in the DOE-sponsored Community Land Model (CLM), which has been evaluated against ground observations (e.g. McDowell et al. 2013).

2. Nitrogen limitation on vegetation growth

Nitrogen is a dominant regulator of vegetation dynamics and the terrestrial carbon cycle, yet rather simplistic, empirical-type models are still used to predict the effect of nitrogen limitation and light competition on vegetation growth. Therefore, a large uncertainty exists in the current simulation of nitrogen related processes (e.g. photosynthesis and soil carbon storage response to nitrogen addition), which substantially affects the reliability of predicted terrestrial carbon fluxes. To reliably assess energy impacts on the global carbon cycle and future climates, we propose to develop, test, and calibrate a next-generation carbon-nitrogen dynamics model and integrate this model into the Community Earth System Model (CESM) developed by the National Center of Atmospheric Research (NCAR), Los Alamos National Laboratory (LANL), and many universities. Our dynamic carbon-nitrogen model will incorporate recent advances in nitrogen modeling and use recent advances in Markov Chain Monte Carlo simulation to rigorously calibrate and evaluate the developed model against observations, including soil fertilization and free air CO2 enrichment (FACE) observations across a range of different forest types. The calibrated model will be used to assess the effects of different energy use scenarios on global climates.
A nitrogen allocation model based on the nitrogen investment
balances among light capture, electron transport, carboxylation,
respiration and storage

3. Better representation of forest disturbances and consequent successional dynamics in models
Disturbances and succession are common ecological processes in forest ecosystems; however, most current dynamics global vegetation models (DGVMs) have not represented these processes well, mainly because they use simple "big leaf" models. NCAR and LANL have successfully incorporated an improved version of ED (with mortality and improved light competition) into the CLM framework through DOE-C (Climate project quantification and reduction of critical uncertainties associated with carbon cycle–climate system feedback, PI: Peter Thornton). We aim to make further improvements to more accurately represent
disturbance and succession dynamics through integration of field, remote sensing, and modeling approaches.

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