2024 Abstracts

Improving Environmental System Science Approaches to Evapotranspiration Partitioning Through Data Fusion


Stephen Good1* (stephen.good@oregonstate.edu), Han Chen1, Kelly Caylor2, Lixin Wang3, Richard Fiorella4


1Oregon State University, Corvallis, OR; 2University of California–Santa Barbara, CA; 3Indiana University–Purdue University Indianapolis, Indianapolis, IN; 4Los Alamos National Laboratory, Los Alamos, NM


This project aims to address the uncertainty in estimates of T/ET, which is the ratio of transpiration (T) to evapotranspiration (ET) flux in hydrologic models. Previous research has shown that models often exhibit compensating errors such that total ET estimates have relatively small biases in comparison to eddy covariance tower ET measurements, yet exhibit strongly divergent T/ET ratios. This uncertainty in T/ET ratios limits the utility of Earth System Models in applications that heavily rely on this partitioning, including investigations of soil moisture dynamics, vegetation dynamics and productivity, food security, and watershed hydrologic response, among others. To provide new insight for mechanistic modeling of ET partitioning within Earth System Models and improve future predictions, this project will evaluate different T/ET partitioning methods through cross-site synthesis. Researchers use these to produce benchmark T/ET data products at long-term research sites and in an upscaled estimate, global T/ET estimates derived from calibrated E3SM Land Model (ELM) simulations under current and future conditions. In doing so, the project will provide fundamental advancements in the characterization of process-based model uncertainty, improvement of modeling with T/ET fusion estimates, and estimation of future declines in T/ET under changing climates. The proposed research will leverage data from AmeriFlux networks and other networks and aligns with the DOE Model-Experiment (ModEx) paradigm. The project addresses ESS Science Research Area 3: “Synthesis Research for Transferable Insights” and will improve understanding broadly across the water, carbon, and energy cycles.