Understanding water and energy fluxes in the Amazonia: Lessons from an observation‐model intercomparison

Abstract

Tropical forests are an important part of global water and energy cycles, but the mechanisms that drive seasonality of their land‐atmosphere exchanges have proven challenging to capture in models. Here, we (1) report the seasonality of fluxes of latent heat (LE), sensible heat (H), and outgoing short and longwave radiation at four diverse tropical forest sites across Amazonia—along the equator from the Caxiuanã and Tapajós National Forests in the eastern Amazon to a forest near Manaus, and from the equatorial zone to the southern forest in Reserva Jaru; (2) investigate how vegetation and climate influence these fluxes; and (3) evaluate land surface model performance by comparing simulations to observations. We found that previously identified failure of models to capture observed dry‐season increases in evapotranspiration (ET) was associated with model overestimations of (1) magnitude and seasonality of Bowen ratios (relative to aseasonal observations in which sensible was only 20%–30% of the latent heat flux) indicating model exaggerated water limitation, (2) canopy emissivity and reflectance (albedo was only 10%–15% of incoming solar radiation, compared to 0.15%–0.22% simulated), and (3) vegetation temperatures (due to underestimation of dry‐season ET and associated cooling). These partially compensating model‐observation discrepancies (e.g., higher temperatures expected from excess Bowen ratios were partially ameliorated by brighter leaves and more interception/evaporation) significantly biased seasonal model estimates of net radiation (Rn), the key driver of water and energy fluxes (LE ~ 0.6 Rn and H ~ 0.15 Rn), though these biases varied among sites and models. A better representation of energy‐related parameters associated with dynamic phenology (e.g., leaf optical properties, canopy interception, and skin temperature) could improve simulations and benchmarking of current vegetation–atmosphere exchange and reduce uncertainty of regional and global biogeochemical models.

Accurate delineation of individual tree crowns in tropical forests from aerial RGB imagery using Mask R‐CNN

“…However, the strength of the carbon sink is diminishing as a result of global warming (Brienen et al, 2015;Hubau et al, 2020) and there are concerns that forests are reaching a tipping point beyond which they could switch irreversibly to open savanna systems (Chai et al, 2021). Forecasting the future of tropical forests is challenging because little is known about the ways different species will respond to changing climate, or the resilience provided by that diversity (Fisher et al, 2018;Gallup et al, 2021;Koven et al, 2020;Restrepo-Coupe et al, 2021). To understand the likely responses of forests to further climate change, ecosystem models need to represent growth and mortality processes of individual trees more accurately than is currently the case (Kellner et al, 2019;Piponiot et al, 2022;Zuidema & van der Sleen, 2022).…”

CAMELE: Collocation-Analyzed Multi-source Ensembled Land Evapotranspiration Data

“…During past decades, a great number of studies have investigated and developed different methods for the estimation of global land evapotranspiration, which leads to lots of datasets. Due to the difference in employed algorithms and principles, discrepancies are quite common among different simulations (Restrepo-Coupe et al, 2021;Han and Tian, 2020;Zhang et al, 2021b). In addition, evaluation of ET products is always challenging due to the lack of sufficient observations at global scale, which limits the direct uses of these data (Pan et al, 2020;Baker et al, 2021).…”

Accurate delineation of individual tree crowns in tropical forests from aerial RGB imagery using Mask R-CNN

“…However, the strength of the carbon 50 sink is diminishing as a result of global warming (2,3) and there are concerns that forests are reaching a tipping point beyond which they could switch irreversibly to open savanna systems (4). However, forecasting the future of tropical forests is challenging, because little is known about the ways 55 different species will respond to changing climate, or the resilience provided by that diversity (5)(6)(7)(8). To understand the likely responses of forests to further climate change, ecosystem models need to represent growth and mortality processes of individual trees more accurately than is currently the case 60 (9)(10)(11).…”

“…Forecasting the future of tropical forests is challenging, because little is known about the ways different species will respond to changing climate, or the resilience provided by that diversity (5)(6)(7)(8). To understand the likely responses of forests to further climate change, ecosystem models need to represent growth and mortality processes of individual trees more accurately than is currently the case (9)(10)(11).…”