Improving ESS approaches to evapotranspiration partitioning through data fusion

This project aims to address the uncertainty in modeled estimates of T/ET, which is the ratio of transpiration (T) to evapotranspiration

evapotranspiration

The process by which water moves from the land or ocean surface to the atmosphere (evaporation) and transpiration (evaporation from plants).

Source: USGS Source

(ET). Previous research has shown that models often exhibit total ET estimates that match observations well, while also exhibiting strongly divergent T/ET ratios. This uncertainty in T/ET ratios limits the utility of Earth System Models

earth system model (ESM)

A model that simulates how chemistry, biology, and physical forces work together. These models are similar to, but much more comprehensive than, global climate models.

Source: U.S. Department of Energy Source

in applications that heavily rely on this partitioning, including investigations of soil moisture dynamics, vegetation dynamics and productivity, food security, and watershed

watershed

An area of land that channels rainfall and snowmelt to creeks, streams, and rivers, and eventually flows to the lowest point in a basin.

Source: USGS Source

hydrologic response. This projects aim is to provide new insight for mechanistic modeling of ET partitioning within Earth System Models and improve future predictions by addressing three objectives.

The first objective is to create a benchmark dataset of T/ET values and associated uncertainties at select AmeriFlux locations, using established T/ET methods. Specifically, we will investigate four broad and overlapping categories of methods to estimate T/ET: theoretical models, high- frequency approaches, remote sensing,

remote sensing

The process of detecting and monitoring the physical characteristics of an area by measuring its reflected and emitted radiation at a distance (typically from satellite or aircraft).

Source: USGS Source

and geochemical models. We will synthesize

synthesis

The process of combining a number of things into a coherent whole to form a theory or system.

Source: National Library of Medicine Source

these different approaches through colocation analysis, a diagnostic approach to assess the uncertainty in a targeted quantity by analyzing multiple inputs of the same quantity when the ‘truth’ is not well known. The second objective is to improve parameterization of transpiration and evaporation processes within an advanced Earth Systems Model through a combination of surrogate modeling and Bayesian optimization. This approach uses machine learning

machine learning (ML)

Machine Learning (ML) refers to the field and practice of using algorithms that are able to “learn” by extracting patterns from a large body of data.

Source: Center of Excellence Source

methods and detailed uncertainty characterizations from Objective 1 to find parameters that result in model output best matching observational data. An ensemble of Energy Exascale Earth System (E3SM)

Energy Exascale Earth System Model (E3SM)

The Energy Exascale Earth System Model (E3SM) project includes Earth System Models, simulations, and predictions to investigate energy-relevant science using code optimized for DOE's advanced supercomputers.

Source: NVCL Web Page Source

Land Model (ELM) simulations will be run at select AmeriFlux sites used to create a data fusion product to evaluate patterns and uncertainties in T/ET estimates. The third objective is to forecast trends in T/ET in the future under different scenarios using the posterior parameter distributions from Objective 2. ELM will be run with future forcing conditions to determine if T/ET will decrease in the future. This will address key uncertainties in future ecohydrologic conditions relevant to carbon, water, and energy cycling.

In summary, this project will evaluate different T/ET partitioning methods and use them to produce benchmark T/ET data products at long-term research sites and in an upscaled estimate, global T/ET estimates derived from calibrated ELM model 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 and broad DOE Environmental System Science (ESS) objectives to improve understanding across the water, carbon, and energy cycles.