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Home » Advancing understanding of drought prediction from environmental stressors

Advancing understanding of drought prediction from environmental stressors

Plants and their strategies to deal with heat and moisture stress may play significant roles
in the evolution, maintenance and severity of drought. Water and carbon are exchanged through
plant stomates, and plants can regulate these exchanges in response to increased moisture stress,
becoming more efficient at using water and leading to less transpiration and increased retention of
soil moisture at root depth, thus dampening the effect of drought on the plants, but possibly
intensifying it in the atmosphere. On the other hand, during short dry periods trees can deliver soil
moisture from deeper roots to the atmosphere, moderating atmospheric dryness. Current methods
to monitor and forecast droughts have, at best, highly simplified representation of these and other
vegetation feedback mechanisms.

Mechanistic land surface process models with detailed representations of vegetation have
the advantage of being able to explicitly diagnose which plant responses alter stomatal regulation
and ecosystem function under varying environmental conditions. This project aims to better
understand plant-drought feedbacks using such a model, the Simple Biosphere Model v4.2 (SiB4).
Our working hypothesis is that a detailed biogeophysical land surface model that couples energy
and biogeochemical fluxes with explicit treatment of soil hydrology, canopy conductance and
turbulent transfer will do better job capturing precursor conditions that end up being classified
as drought. We propose to analyze cases of North American droughts from the satellite era and
(a) examine whether they evolved as characterized by drought outlook monitors, and (b) identify
the possible positive or negative feedbacks from vegetation response to climate conditions. In situ
radiation budget and trace gas measurements from NOAA GMD’s measurement networks will be
used to evaluate and improve SiB4 simulations, and a novel aspect of this project is to incorporate
new data constraints that have not been previously considered for drought monitoring. Carbon and
water fluxes are intrinsically linked, and vegetation responses to drought conditions are observable
in atmospheric carbon measurements. We will also make use of a variety of space-based
observations such as solar induced fluorescence (SIF), which reflects dynamic photosynthetic
responses to heat and water stress, and can be simulated by SiB4.

Understanding process-based plant responses to climate stressors such as high temperature
and deficits of vapor pressure and soil moisture has the potential to help predict the occurrence
and severity of a drought. This project is directly responsive to CPO’s strategy to address
challenges in the area of “Climate impacts on water resources” and climate intelligence capabilities
regarding “Observations and monitoring” and “Earth system science and modeling”. We directly
address CPO MAPP Competition #3 (Characterizing and Anticipating U.S. Droughts’ Complex
Interactions) Priority Area A: “Identify the array of complex interdisciplinary interactions that lead
to US drought and intervene during the evolution of drought, focusing on key processes and
feedbacks”, with emphasis on land cover and environmental effects, and Priority Area B: “Identify
key parameters and develop new/improved methodologies to more integrally characterize drought
occurrence”, where the proposal will demonstrate this methodology for a test drought case. These
will add to NIDIS’ resources for monitoring and predicting drought from a new perspective.

Climate Risk Area: Water Resources

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