NSF Award
2228047
Plants rely on water storage in the soils to continue developing leaves and undergoing photosynthesis during drought periods. This water source is called dynamic water storage. During drought periods, some plants alter their growth strategies to conserve dynamic water storage. This process by which plants adapt to drought conditions is complex, but critical to document as it impacts carbon, energy, and water cycles. This project will explore whether incorporating plant interaction with dynamic water can provide an accurate estimate of plant resilience to drought. This study will develop a modeling framework that incorporates feedbacks between dynamic water storage and vegetation growth and water-use. The framework will be tested for forested mountain sites in Colorado. This project will help evaluate how vegetation will adapt to the possibility of more frequent and extreme drought events in the future.<br/><br/>Severe drought events seem to be occurring with greater frequency. When rainfall is infrequent, natural vegetation relies on subsurface water stores, referred to as dynamic water storage, to sustain new leaf growth and photosynthesis. Prior work has documented changes in vegetation productivity and water-use efficiency as a result of reduced dynamic water storage during drought. Previous studies have also observed how vegetation can adopt different water-use strategies during drought periods. However, a knowledge gap persists around feedbacks between dynamic water storage and vegetation phenology and water-use. These feedbacks dynamically alter exchanges of water and energy between the land-surface and the atmosphere that are difficult to capture. In order to address this gap, this project will develop a modeling framework that accounts for interactions between phenologic changes in response to environmental conditions, vegetation water-use strategies, and dynamic water storage. The approach couples a land-surface hydrology model with a prognostic phenology model to capture these interactions. To reduce uncertainty in forecasting plant states, satellite remote sensing observations of plant greenness (i.e., fraction of photosynthetically active radiation) and plant density (i.e., leaf area index) are assimilated using a dual state-parameter Ensemble Kalman Filter. This approach will permit quantification of uncertainty in environmental conditions and the propagation of this uncertainty through estimates of dynamic water storage and land surface fluxes of water, energy, and carbon. The model will be validated against data collected from forested mountain basins in Colorado that are being studied by the Dynamic Water Cluster of the Critical Zone Network. This research will improve the predictability of land-atmosphere interactions that characterize drought events in western mountain basins.<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.
