Integrating eddy covariance and remotely sensed data to improve vegetation parameterization in a coupled land surface-groundwater model

Authors: Schreiner-Mcgraw, Adam; AJAMI, HOORI - University Of California; Anderson, Raymond - Ray; Wang, Dong; Kelley, Jason

Submitted to: American Geophysical Union Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 10/4/2021
Publication Date: 12/16/2021
Citation: Schreiner-Mcgraw, A.P., Ajami, H., Anderson, R.G., Wang, D., Kelley, J.R. 2021. Integrating eddy covariance and remotely sensed data to improve vegetation parameterization in a coupled land surface-groundwater model [abstract]. American Geophysical Union Meeting Abstract. Paper 897326.

Interpretive Summary:

Technical Abstract: The Central Valley of California is one of the most productive agricultural regions in the world and more than 230 different crops are grown within its boundaries. Increasing pressure on water resources, however, is making sustainable production difficult, and both land use practices and conjunctive surface water-groundwater use for irrigation have profound impacts on the land-atmosphere interactions and groundwater resources. Coupled hydrological models with land surface models (LSMs) are a vital tool in understanding the impacts of crop management on water resources. The variability in crops and agricultural practices within the Central Valley makes the representation of vegetation in LSMs difficult. In this contribution, we make use of eddy covariance and remote sensing observations of evapotranspiration (ET) to investigate parameterization approaches for croplands within an integrated hydrologic model that has been coupled with a LSM, ParFlow.CLM. Additionally, we use a flux variance similarity approach to partition the observed ET from 7 eddy covariance towers located in the Central Valley into evaporation and transpiration components. We test approaches to parameterize several perennial crop types within the LSM, including almonds, pistachios, and citrus, and quantify the impacts of these parameterization approaches on the simulated groundwater recharge. We find that at the daily to weekly temporal scale, transpiration data contains too much uncertainty to reliably parameterize croplands and its application is best at an annual time step. By improving the perennial crop parameterization in the Common Land Model, we investigate the impacts of water conservation practices, such as deficit irrigation, on land atmosphere interactions in the 12,276 km2 model domain encompassing the Kaweah River Basin, in California, and discuss the implications for groundwater management in the Central Valley.