The proposed work will encompass three intertwined approaches: 1) a better representation of plant water stress and soil moisture, by implementing a plant hydraulic model and new stomatal conductance model in the NOAH land model of the CFS operational forecast model; 2) linking the model surface fluxes and vegetation water status to remote sensing observations of surface fluxes based on solar-induced fluorescence (SIF – a proxy for photosynthesis), and plant water stress strategy based on microwave vegetation optical depth (VOD – directly related to vegetation water status), and 3) defining a quantitative assessment of the model improvement through changes in land-atmosphere interaction using an improved causal statistical model that explicitly defines the time scales of the feedback from weekly to interannual, and its impact on droughts.
In part 1, we will implement a plant hydraulic model that directly resolves the transport of water throughout the plant xylem and an improved stomatal conductance model based on optimal carbon gain. This will define a physically-based response of plants to water stress through either stomatal closure (demand break down) under reduced leaf water potential or through xylem cavitation (supply break down).
In part 2, we will constrain this new model and avoid over-parameterization by making use of a recent vegetation water stress index derived from vegetation optical depth (VOD) measurements from the ASMR satellite, as it can directly sense the canopy water status at a spatial scale (~25 to 50km) relevant to weather and land-surface modeling prediction. We will also use novel estimates of surface fluxes based on solar-induced fluorescence (SIF) measurements to further constrain the dynamics of plant transpiration and the new model parameterization.
We will test the implemented models with typical metrics (2m temperature and humidity, precipitation and geopotential heights) but also with a new causal statistical technique based on multivariate Granger causality. We recently showed that such a technique could be used to assess vegetation-atmosphere feedback and to isolate the time scales of the feedback from monthly to interannual in coupled land-(ocean-)atmosphere models. We will extend this analysis to non-stationary/non-periodic time series using temporal wavelets (as opposed to Fourier transform currently used) so that we can further highlight the time dependence of the feedback in the coupled model. This will further help refine the model during droughts.
This work is directly relevant to the MAPP drought competition 1 as well as to NOAA’s long-term climate goals as it “advances the understanding, monitoring, and prediction of drought [with] improved understanding of predictability relevant to drought, [and] model development.” It also uses “new monitoring […] products”. It will also improve subseasonal to seasonal prediction, which is critical for water resource and ecosystem health forecasts.