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1. Calculating Canopy Stomatal Conductance from Eddy Covariance Measurements, in Light of the Energy Budget Closure Problem

   https://doi.org/https://doi.org/10.5194/bg-2020-154  

   Wehr, Richard; Saleska, Scott R. (May 2020, Biogeosciences discussions)

   Canopy stomatal conductance (gsV) is commonly estimated from eddy covariance (EC) measurements of latent heat flux (LE) by inverting the Penman-Monteith (PM) equation. That method implicitly represents the sensible heat flux (H) as the residual of all other terms in the site energy budget – even though H is measured at least as accurately as LE at every EC site while the rest of the energy budget almost never is. We argue that gsV should instead be calculated from EC measurements of both H and LE, using the flux-gradient formulation that defines conductance and underlies the PM equation. The flux-gradient formulation dispenses with unnecessary assumptions, is conceptually simpler, and provides more accurate values of gsV for all plausible scenarios in which the measured energy budget fails to close, as is common at EC sites. The PM equation, on the other hand, contributes biases and erroneous spatial and temporal patterns to gsV, skewing its relationships with drivers such as light and vapor pressure deficit. To minimize the impact of the energy budget closure problem on the PM equation, it was previously proposed that the eddy fluxes should be corrected to close the long-term energy budget while preserving the Bowen ratio (B = H/LE). We show that such a flux correction does not fully remedy the PM equation but should produce accurate values of gsV when combined with the flux-gradient formulation. 

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2. Hybrid modeling of evapotranspiration: inferring stomatal and aerodynamic resistances using combined physics-based and machine learning

   https://doi.org/10.1088/1748-9326/acbbe0  

   ElGhawi, Reda; Kraft, Basil; Reimers, Christian; Reichstein, Markus; Körner, Marco; Gentine, Pierre; Winkler, Alexander J (March 2023, Environmental Research Letters)

   Abstract The process of evapotranspiration transfers liquid water from vegetation and soil surfaces to the atmosphere, the so-called latent heat flux ( Q LE ), and modulates the Earth’s energy, water, and carbon cycle. Vegetation controls Q LE by regulating leaf stomata opening (surface resistance r s in the Big Leaf approach) and by altering surface roughness (aerodynamic resistance r a ). Estimating r s and r a across different vegetation types is a key challenge in predicting Q LE . We propose a hybrid approach that combines mechanistic modeling and machine learning for modeling Q LE . The hybrid model combines a feed-forward neural network which estimates the resistances from observations as intermediate variables and a mechanistic model in an end-to-end setting. In the hybrid modeling setup, we make use of the Penman–Monteith equation in conjunction with multi-year flux measurements across different forest and grassland sites from the FLUXNET database. This hybrid model setup is successful in predicting Q LE , however, this approach leads to equifinal solutions in terms of estimated physical parameters. We follow two different strategies to constrain the hybrid model and therefore control for the equifinality that arises when the two resistances are estimated simultaneously. One strategy is to impose an a priori constraint on r a based on mechanistic assumptions (theory-driven strategy), while the other strategy makes use of more observational data and adds a constraint in predicting r a through multi-task learning of both latent and sensible heat flux ( Q H ; data-driven strategy) together. Our results show that all hybrid models predict the target variables with a high degree of success, with R 2 = 0.82–0.89 for grasslands and R 2 = 0.70–0.80 for forest sites at the mean diurnal scale. The predicted r s and r a show strong physical consistency across the two regularized hybrid models, but are physically implausible in the under-constrained hybrid model. The hybrid models are robust in reproducing consistent results for energy fluxes and resistances across different scales (diurnal, seasonal, and interannual), reflecting their ability to learn the physical dependence of the target variables on the meteorological inputs. As a next step, we propose to test these heavily observation-informed parameterizations derived through hybrid modeling as a substitute for ad hoc formulations in Earth system models. 

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3. The Maximum Entropy Production (MEP) Method for Modeling Surface Energy Budget – Theory and Applications to Arctic Regions

   Wang, Jingfeng.; Sharif, Husayn E.; Ivanov, Valeriy Y.; Zhou, Wenbo; Sheshukov, Alexsey; Mazepa, Valeriy. (December 2019, Agu)

   The Maximum Entropy Production (MEP) method for modeling surface energy budget has been developed and validated at local, regional and global scale including the Arctic regions. The MEP model has solid theoretical foundation built on the Bayesian probability theory, information theory, non-equilibrium thermodynamics and boundary layer turbulence theory. Its formulation has advantageous features including closing energy budget at any space-time scales, independence of moisture and temperature gradient, wind speed and surface roughness, and free of tunable empirical parameters. Application of the MEP model has been covering all types of land covers including Arctic permafrost tundra, sea ice and snow surfaces. Recent tests using field experimental observations suggest that the MEP model using fewer input data and model parameters is able to simulate surface energy budget accurately. It is a more efficient alternative to the classical Penman-Monteith model of potential evapotranspiration. The MEP method has potential to influence the study of Arctic water-energy cycles and climate change. 

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4. A boreal forest model benchmarking dataset for North America: a case study with the Canadian Land Surface Scheme Including Biogeochemical Cycles (CLASSIC)

   https://doi.org/10.1088/1748-9326/ace376  

   Qu, Bo; Roy, Alexandre; Melton, Joe R; Black, T Andrew; Amiro, Brian; Euskirchen, Eugénie S; Ueyama, Masahito; Kobayashi, Hideki; Schulze, Christopher; Gosselin, Gabriel Hould; et al (July 2023, Environmental Research Letters)

   Abstract Climate change is rapidly altering composition, structure, and functioning of the boreal biome, across North America often broadly categorized into ecoregions. The resulting complex changes in different ecoregions present a challenge for efforts to accurately simulate carbon dioxide (CO₂) and energy exchanges between boreal forests and the atmosphere with terrestrial ecosystem models (TEMs). Eddy covariance measurements provide valuable information for evaluating the performance of TEMs and guiding their development. Here, we compiled a boreal forest model benchmarking dataset for North America by harmonizing eddy covariance and supporting measurements from eight black spruce (Picea mariana)-dominated, mature forest stands. The eight forest stands, located in six boreal ecoregions of North America, differ in stand characteristics, disturbance history, climate, permafrost conditions and soil properties. By compiling various data streams, the benchmarking dataset comprises data to parameterize, force, and evaluate TEMs. Specifically, it includes half-hourly, gap-filled meteorological forcing data, ancillary data essential for model parameterization, and half-hourly, gap-filled or partitioned component flux data on CO₂(net ecosystem production, gross primary production [GPP], and ecosystem respiration [ER]) and energy (latent [LE] and sensible heat [H]) and their daily aggregates screened based on half-hourly gap-filling quality criteria. We present a case study with the Canadian Land Surface Scheme Including Biogeochemical Cycles (CLASSIC) to: (1) demonstrate the utility of our dataset to benchmark TEMs and (2) provide guidance for model development and refinement. Model skill was evaluated using several statistical metrics and further examined through the flux responses to their environmental controls. Our results suggest that CLASSIC tended to overestimate GPP and ER among all stands. Model performance regarding the energy fluxes (i.e., LE and H) varied greatly among the stands and exhibited a moderate correlation with latitude. We identified strong relationships between simulated fluxes and their environmental controls except for H, thus highlighting current strengths and limitations of CLASSIC. 

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5. New Evidence for the Importance of Non‐Stomatal Pathways in Ozone Deposition During Extreme Heat and Dry Anomalies

   https://doi.org/10.1029/2021GL095717  

   Wong, A. Y. H.; Geddes, J. A.; Ducker, J. A.; Holmes, C. D.; Fares, S.; Goldstein, A. H.; Mammarella, I.; Munger, J. W. (April 2022, Geophysical Research Letters)

   Abstract Dry deposition could partially explain the observed response in ambient ozone to extreme hot and dry episodes. We examine the response of ozone deposition to heat and dry anomalies using three long‐term co‐located ecosystem‐scale carbon dioxide, water vapor and ozone flux measurement records. We find that, as expected, canopy stomatal conductance generally decreases during days with dry air or soil. However, during hot days, concurrent increases in non‐stomatal conductance are inferred at all three sites, which may be related to several temperature‐sensitive processes not represented in the current generation of big‐leaf models. This may offset the reduction in stomatal conductance, leading to smaller net reduction, or even net increase, in total deposition velocity. We find the response of deposition velocity to soil dryness may be related to its impact on photosynthetic activity, though considerable variability exists. Our findings emphasize the need for better understanding and representation of non‐stomatal ozone deposition. 

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