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January 31, 2021

This paper, led by PhD student Nathan Dadap, uses Planet data to map, for the first time, drainage canals in the tropical peatlands of Southeast Asia, a hotspot of CO2 emissions. These canals lower the water table and cause large CO2 emissions associated with the resulting peat decompositon. We further showed that, contrary to common assuptions in estimating carbon emissions, there is significant diversity in drainage density within land use classes. These maps may therefore be useful in building better emissions estimates for this hotspot region. The paper is in review at AGU Advances, but can now be read as a preprint on the Earth and Space Science Open Archive, here.

January 2, 2021

The paper describes some fieldwork we completed as part of SMAPVEX19-21, to demonstrate explicitly (and without a difference in spatial scales) that L-band VOD is sensitive to xylem and leaf water potential variations. The paper is still undergoing typesetting, but the original version can be found here.

January 1, 2021

In the paper, we present an approach to estimate ecosystem-scale plant hydraulic parameters (e.g. maximum xylem conductance, P50, stomatal parameters) using model-data fusion combining a plant hydraulic model and assimilation of LPDR vegetation optical depth and ALEXI evapotranspiration. Using the new trait maps, we further show that one can derive 'hydraulic functional types' - alternative clusters to PFTs. When the hydraulic functional type mean is used instead of a PFT-wide mean (as in typical land surface models), ET forward model estimates improve even for a relatively small number of HFTs. You can read (or comment on!) the full paper here.

January 1, 2021

Carbon cycle models have become increasingly complex over time, as more and more processes are represented. But as models get more complex, the risk that they are mis-parametrized becomes increasingly large. We used CARDAMOM with a variety of different model structures, data assimilation types and errors, and more to systematically assess whether increasing model complexity always improves forecast performance (including accounting for prediction uncertainty). In fact, intermediate complexity models often perform better than more complex models. Increased complexity only improves forecast skill if net carbon flux data is available to sufficiently constrain the parameters. This suggests terrestrial biosphere models should focus more on constraining parameter uncertainties rather than simply increasing the number of processes represented. A list of other specific recommendations is also included in the paper, which can be read (or commented on!) here.

December 30, 2020

Greg leaves the group to go work in Anna Trugman's group at UCSB. We're grateful for Greg's time in the group, and that he'll still be close by!