A simple and objective method to partition evapotranspiration into transpiration and evaporation at eddy-covariance sites

Li, Xi; Gentine, Pierre; Lin, Chungwei; Zhou, Sha; Sun, Zhenhao; Zheng, Yi; Liu, Jie; Zheng, Chunmiao

doi:10.1016/j.agrformet.2018.11.017

Cited by 144 publications

(89 citation statements)

References 58 publications

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“…A modified (in this case binned) parameter optimization approach was used by Li et al (2019), which follows the model proposed by Lin et al (2018):…”

Section: Partitioning Et Using Half-hourly Eddy Covariance Observationsmentioning

confidence: 99%

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Reviews and syntheses: Turning the challenges of partitioning ecosystem evaporation and transpiration into opportunities

Stoy¹,

El‐Madany²,

Fisher³

et al. 2019

Preprint

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Evaporation (E) and transpiration (T) respond differently to ongoing changes in climate, atmospheric composition, and land use. Our ability to partition evapotranspiration (ET) into E and T is limited at the ecosystem scale, which renders the validation of satellite data and land surface models incomplete. Here, we review current progress in partitioning E and T, and provide a prospectus for how to improve theory and observations going forward. Recent advancements in analytical techniques provide additional opportunities for partitioning E and T at the ecosystem scale, but their assumptions have yet to be fully 35 tested. Many approaches to partition E and T rely on the notion that plant canopy conductance and ecosystem water use efficiency (EWUE) exhibit optimal responses to atmospheric vapor pressure deficit (D). We use observations from 240 eddy covariance flux towers to demonstrate that optimal ecosystem response to D is a reasonable assumption, in agreement with recent studies, but the conditions under which this assumption holds require further analysis. Another critical assumption for many ET partitioning approaches is that ET can be approximated as T during ideal transpiring conditions, which has been 40 Biogeosciences Discuss., https://doi.

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“…A modified (in this case binned) parameter optimization approach was used by Li et al (2019), which follows the model proposed by Lin et al (2018):…”

Section: Partitioning Et Using Half-hourly Eddy Covariance Observationsmentioning

confidence: 99%

“…et al (2018) andLi et al (2019) methods both circumvent the assumption that T/ET approaches unity at some periods by estimating ecosystem conductances. The Transpiration Estimation Algorithm (TEA) fromNelson et al (2018) utilizes a non-parametric model and thereby further limits assumptions made about how the ecosystem functions.…”

mentioning

confidence: 99%

Reviews and syntheses: Turning the challenges of partitioning ecosystem evaporation and transpiration into opportunities

Stoy¹,

El‐Madany²,

Fisher³

et al. 2019

Preprint

Self Cite

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“…The sampling frequency is half hourly. The data filter procedure follows previous typical ones (e.g., Li, Gentine, et al, 2019;Lin et al, 2018;Zhou et al, 2015Zhou et al, , 2016, which can be summarized as follows: First, only measured and good-quality gap-filled data (Reichstein et al, 2005) were selected. Second, we removed the data that were sampled during rainy days and the following day to avoid issues with rain interception and sensor saturation at high RH (Medlyn et al, 2011).…”

Section: Datamentioning

confidence: 99%

Physics‐Constrained Machine Learning of Evapotranspiration

Zhao

Gentine

Reichstein

et al. 2019

Geophysical Research Letters

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Estimating ecosystem evapotranspiration (ET) is important to understanding the global water cycle and to study land-atmosphere interactions. We developed a physics constrained machine learning (ML) model (hybrid model) to estimate latent heat flux (LE), which conserves the surface energy budget. By comparing model predictions with observations at 82 eddy covariance tower sites, our hybrid model shows similar performance to the pure ML model in terms of mean metrics (e.g., mean absolute percent errors) but, importantly, the hybrid model conserves the surface energy balance, while the pure ML model does not. A second key result is that the hybrid model extrapolates much better than the pure ML model, emphasizing the benefits of combining physics with ML for increased generalizations. The hybrid model allows inferring the structural dependence of ET and surface resistance (r s ), and we find that vegetation height and soil moisture are the main regulators of ET and r s . Plain Language SummaryA physics constrained machine learning model is developed using the FLUXNET2015 Tier 1 data set. This new approach is able to effectively retrieve latent heat flux while constraining energy conservation in the surface energy budget. This hybrid model has better performance in extrapolation than a pure machine learning model. Key Points: • A physics-constrained machine learning model of evapotranspiration (hybrid model) is developed and trained using the FLUXNET 2015 data set • The evapotranspiration retrieved by the hybrid model is as accurate as pure machine learning model and also conserves surface energy balance • The hybrid model better reproduces extremes and thus better extrapolates compared to the pure machine learning approach Supporting Information:• Supporting Information S1• Figure S1 • Table S1

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“…In dry semi-arid climates, where precipitation satisfies 20-50% of potential evapotranspiration (Arora, 2002), shrubs have invaded hundreds of millions of hectares of grasslands (Archer et al, 2017;Bestelmeyer et al, 2018). In semi-arid systems, precipitation events are often small (< 5 mm) and interception and evaporation often account for 30-50% of mean annual precipitation, with the remaining precipitation available for infiltration and root water uptake (Li et al, 2019). Due to the strong correlation between stomatal conductance, transpiration and carbon fixation, these processes often result in rain-use efficiencies of roughly 40% (e.g.…”

Section: Evidence and Consequences Of Semi-arid Biome Shiftsmentioning

confidence: 99%

Forecasting semi‐arid biome shifts in the Anthropocene

Kulmatiski

Mackay

et al. 2020

New Phytologist

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Summary Shrub encroachment, forest decline and wildfires have caused large‐scale changes in semi‐arid vegetation over the past 50 years. Climate is a primary determinant of plant growth in semi‐arid ecosystems, yet it remains difficult to forecast large‐scale vegetation shifts (i.e. biome shifts) in response to climate change. We highlight recent advances from four conceptual perspectives that are improving forecasts of semi‐arid biome shifts. Moving from small to large scales, first, tree‐level models that simulate the carbon costs of drought‐induced plant hydraulic failure are improving predictions of delayed‐mortality responses to drought. Second, tracer‐informed water flow models are improving predictions of species coexistence as a function of climate. Third, new applications of ecohydrological models are beginning to simulate small‐scale water movement processes at large scales. Fourth, remotely‐sensed measurements of plant traits such as relative canopy moisture are providing early‐warning signals that predict forest mortality more than a year in advance. We suggest that a community of researchers using modeling approaches (e.g. machine learning) that can integrate these perspectives will rapidly improve forecasts of semi‐arid biome shifts. Better forecasts can be expected to help prevent catastrophic changes in vegetation states by identifying improved monitoring approaches and by prioritizing high‐risk areas for management.

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A simple and objective method to partition evapotranspiration into transpiration and evaporation at eddy-covariance sites

Cited by 144 publications

References 58 publications

Reviews and syntheses: Turning the challenges of partitioning ecosystem evaporation and transpiration into opportunities

Reviews and syntheses: Turning the challenges of partitioning ecosystem evaporation and transpiration into opportunities

Physics‐Constrained Machine Learning of Evapotranspiration

Forecasting semi‐arid biome shifts in the Anthropocene

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