Integrating flux partitioning and eddy covariance to improve vegetation parameterization in a coupled land surface-groundwater model

Authors: Schreiner-Mcgraw, Adam; AJAMI, HOORI - UNIVERSITY OF CALIFORNIA; Anderson, Raymond - Ray; WOOD, JEFFREY - UNIVERSITY OF MISSOURI; Wang, Dong

Submitted to: Frontiers in Hydrology Meeting
Publication Type: Abstract Only
Publication Acceptance Date: 3/31/2022
Publication Date: 6/23/2022
Citation: Schreiner-Mcgraw, A.P., Ajami, H., Anderson, R.G., Wood, J.D., Wang, D. 2022. Integrating flux partitioning and eddy covariance to improve vegetation parameterization in a coupled land surface-groundwater model [abstract]. Frontiers in Hydrology Meeting. Paper 1032799.

Interpretive Summary:

Technical Abstract: Accurate simulation of plant water use across agro-ecosystems is essential for various applications, including precision agriculture, quantifying groundwater recharge, and optimizing irrigation rates. Previous approaches to integrating plant water use data into hydrologic models have relied on evapotranspiration (ET) observations. But, these observations cannot evaluate the accuracy of separate evaporation and transpiration parameterizations. The recently developed variance similarity approach partitions eddy covariance (EC) observations of ET to transpiration (T) and evaporation, providing an opportunity to use T data to parameterize vegetation in hydrologic models. To explore the value of T/ET data in improving hydrologic model performance, we examined multiple approaches to incorporate these observations to parameterize vegetation. We used ET observations from 5 EC towers in the San Joaquin Valley, California, and 4 EC towers in the Central Claypan Region of Missouri, to parameterize vegetation in an integrated land surface – groundwater model. We find that a simple approach of selecting the top-performing parameter sets based on ET and T performance metrics works well at most study sites. Furthermore, selecting parameters using both ET and T data reduces uncertainty of simulated ET compared to parameterization based on ET data alone. Similarly, the uncertainty in potential groundwater recharge is reduced by including T data in the vegetation parameterization. We discuss the value of T/ET data for parameterizing hydrologic models over various environments, including semi-arid irrigated agriculture, rainfed agriculture, native prairie, and broadleaf forest. Finally, we simulate ET under climate change scenarios and assess the extent to which vegetation parameterization approaches impact the simulated ET response. Insights from this work can help researchers understand how land management practices in major agricultural systems affect the water cycle and land-atmosphere interactions.