NSF Award
2406931
Terrestrial ecologists have faced a long-standing challenge - how to separate the uptake of atmospheric CO2 by terrestrial ecosystems (net ecosystem exchange, NEE) into its two offsetting components: gross primary production (GPP) and ecosystem respiration (Reco). A similar challenge has been plaguing hydrologists - how to quantify the two components of water vapor flux from land: transpiration (T) through plant stomata (pores) and evaporation (E) from non-stomatal surfaces. Despite numerous efforts in the past decades to partition the “direct observables” of NEE and ET, considerable biases remain in our estimates of the components. Without credible in-situ observation of GPP, Reco, T, and E, it will be impossible to understand or predict the complex dynamics of coupled carbon and water cycles under changing climate with the needed precision. Biases in these component flux estimates can further distort our understanding of their relationships, e.g., T:ET ratio and water use efficiency (WUE = GPP/T), driving uncertainty in how they are affected by environmental change. Therefore, it is urgent to develop innovative approaches that accurately partition NEE and ET into their component fluxes. This will reduce uncertainties in current terrestrial biosphere models (TBMs), improve water resource management, and inform nature-based climate solutions. <br/><br/>This project aims to harness new theoretical and data advances in the remote sensing of Solar-Induced chlorophyll Fluorescence (SIF) to jointly partition ecosystem ET and NEE fluxes. It has three technical aims: 1) developing a mechanistic approach to jointly partitioning ET and NEE into their component fluxes using concurrent canopy-scale SIF and flux measurements across diverse NEON ecoregions and hydroclimatic regimes; 2) disentangling interacting mechanisms that control the temporal and spatial dynamics of individual component fluxes, T:ET ratio, and WUE across biomes and hydroclimatic regimes, and 3) improving the NCAR Community Land Model (CLM5) by better representing the interacting mechanisms among component fluxes through a hybrid modeling approach that embeds mechanisms into a deep learning framework, i.e., Biology-Informed Neural Networks (BINN). The proposed SIF-based joint-partitioning framework, guided by new ecophysiological theories, will provide valuable datasets of individual component fluxes, T:ET ratio, and WUE across diverse biomes and hydroclimatic regimes. These datasets will enhance the fidelity and realism of TBMs in predicting ecological and hydrological dynamics at multiple scales under climate change. This project will support a diverse array of students via the annual training course “New Advances in Land Carbon Cycle Modeling,” engaging high-school students via the Project SEED program, and contributing to undergraduate/graduate education at Cornell University and Indiana University Indianapolis.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
