Integrating partitioned evapotranspiration data into hydrologic models:
vegetation parameterization and uncertainty quantification of simulated
plant water use
Accurate simulation of plant water use across agricultural 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. Recently,
the flux variance similarity approach has been developed to partition ET
to transpiration (T) and evaporation, providing an opportunity to use T
data to parameterize models. To explore the value of T/ET data in
improving hydrologic model performance, we examined multiple approaches
to incorporate these observations for vegetation parameterization. We
used ET observations from 5 eddy covariance towers located in the San
Joaquin Valley, California, to parameterize orchard crops in an
integrated land surface – groundwater model. We find that a simple
approach of selecting the best parameter sets based on ET and T
performance metrics works best at these study sites. Selecting
parameters based on performance relative to observed ET creates an
uncertainty of 27% relative to the observed value. When parameters are
selected using both T and ET data, this uncertainty drops to 24%.
Similarly, the uncertainty in potential groundwater recharge drops from
63% to 58% when parameters are selected with ET or T and ET data,
respectively. Additionally, using crop type parameters results in
similar levels of simulated ET as using site-specific parameters.
Different irrigation schemes create high amounts of uncertainty and
highlight the need for accurate estimates of irrigation when performing
water budget studies.