Carbon, Water and Energy Fluxes of Terrestrial Ecosystems in Italy

Papale, Dario; Migliavacca, Mirco; Cremonese, Edoardo; Cescatti, Alessandro; Alberti, Giorgio; Balzarolo, Manuela; Marchesini, Luca Belelli; Canfora, E.; Casa, Raffaele; Duce, Pierpaolo; Facini, Osvaldo; Galvagno, Marta; Genesio, L.; Gianelle, Damiano; Magliulo, Vincenzo; Matteucci, Giorgio; Montagnani, Leonardo; Petrella, Fabio; Pitacco, Andrea; Seufert, G.; Spano, Donatella; Stefani, P.; Vaccari, Francesco Primo; Valentini, Riccardo

doi:10.1007/978-3-642-32424-6_2

Cited by 10 publications

(5 citation statements)

References 28 publications

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“…These adaptations serve to limit oxidative damage under high light and low temperature conditions, where an imbalance between electron supply and demand exists, arising from an imbalance between temperature-insensitive photochemical rates and temperature-sensitive biochemical rates. The reversion of these adaptations and resumption of the intrinsic quantum yield efficiency and photosynthesis requires sustained temperatures above a certain critical threshold (Tanja et al, 2003) and exhibits a delay with respect to instantaneous air temperatures (Pelkonen and Hari, 1980;Mäkelä et al, 2004). Approaches accounting for a delayed resumption of photosynthesis after cold periods offer scope for further improvement of the P-model and may be included in global vegetation and Earth system models where this effect is currently not accounted for (Tanja et al, 2003;Rogers et al, 2017).…”

Section: Discussionmentioning

confidence: 99%

P-model v1.0: an optimality-based light use efficiency model for simulating ecosystem gross primary production

et al. 2020

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Abstract. Terrestrial photosynthesis is the basis for vegetation growth and drives the land carbon cycle. Accurately simulating gross primary production (GPP, ecosystem-level apparent photosynthesis) is key for satellite monitoring and Earth system model predictions under climate change. While robust models exist for describing leaf-level photosynthesis, predictions diverge due to uncertain photosynthetic traits and parameters which vary on multiple spatial and temporal scales. Here, we describe and evaluate a GPP (photosynthesis per unit ground area) model, the P-model, that combines the Farquhar–von Caemmerer–Berry model for C3 photosynthesis with an optimality principle for the carbon assimilation–transpiration trade-off, and predicts a multi-day average light use efficiency (LUE) for any climate and C3 vegetation type. The model builds on the theory developed in Prentice et al. (2014) and Wang et al. (2017a) and is extended to include low temperature effects on the intrinsic quantum yield and an empirical soil moisture stress factor. The model is forced with site-level data of the fraction of absorbed photosynthetically active radiation (fAPAR) and meteorological data and is evaluated against GPP estimates from a globally distributed network of ecosystem flux measurements. Although the P-model requires relatively few inputs, the R2 for predicted versus observed GPP based on the full model setup is 0.75 (8 d mean, 126 sites) – similar to comparable satellite-data-driven GPP models but without predefined vegetation-type-specific parameters. The R2 is reduced to 0.70 when not accounting for the reduction in quantum yield at low temperatures and effects of low soil moisture on LUE. The R2 for the P-model-predicted LUE is 0.32 (means by site) and 0.48 (means by vegetation type). Applying this model for global-scale simulations yields a total global GPP of 106–122 Pg C yr−1 (mean of 2001–2011), depending on the fAPAR forcing data. The P-model provides a simple but powerful method for predicting – rather than prescribing – light use efficiency and simulating terrestrial photosynthesis across a wide range of conditions. The model is available as an R package (rpmodel).

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Section: Discussionmentioning

confidence: 99%

P-model v1.0: an optimality-based light use efficiency model for simulating ecosystem gross primary production

et al. 2020

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“…The Julian day of the year (DOY) and the potential incoming radiation (SW_IN_POT [W/m 2 ]) were also used in order to provide information about seasonality and length of the day. The SW_IN_POT was also aggregated at daily resolution to better track the seasonality of light conditions; the first derivatives of half hourly and daily SW_IN_POT were also calculated to add specific information about the seasonal and diurnal dynamics of light (Bodesheim, Jung, Gans, Mahecha, & Reichstein, 2018; Papale, Black, et al, 2015; Papale, Migliavacca, et al, 2015).…”

Section: Methodsmentioning

confidence: 99%

Partitioning net carbon dioxide fluxes into photosynthesis and respiration using neural networks

Tramontana

Migliavacca

Jung

et al. 2020

Global Change Biology

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“…This vegetation type is representative of a typical Centro-European species-rich mesophile grasslands at intermediate altitudes (between the Arrenatherion and Nardus types) of the montane and sub-alpine levels. The IT-MBo meadow soil can be classified as a Typic Hapludalfs, lyme loamy, mixed, mesic with the following characteristics in the 0-30 cm horizon: total soil organic content (SOC) = 9.4 ± 0.4 kg C m −2 ; total N = 0.29 ± 0.02 kg N m −2 ; and soil bulk density = 0.79 ± 0.29 g cm −3 [47].…”

Section: Study Sitesmentioning

confidence: 99%

VIS-NIR, Red-Edge and NIR-Shoulder Based Normalized Vegetation Indices Response to Co-Varying Leaf and Canopy Structural Traits in Heterogeneous Grasslands

et al. 2020

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Red-edge (RE) spectral vegetation indices (SVIs)—combining bands on the sharp change region between near infrared (NIR) and visible (VIS) bands—alongside with SVIs solely based on NIR-shoulder bands (wavelengths 750–900 nm) have been shown to perform well in estimating leaf area index (LAI) from proximal and remote sensors. In this work, we used RE and NIR-shoulder SVIs to assess the full potential of bands provided by Sentinel-2 (S-2) and Sentinel-3 (S-3) sensors at both temporal and spatial scales for grassland LAI estimations. Ground temporal and spatial observations of hyperspectral reflectance and LAI were carried out at two grassland sites (Monte Bondone, Italy, and Neustift, Austria). A strong correlation (R2 > 0.8) was observed between grassland LAI and both RE and NIR-shoulder SVIs on a temporal basis, but not on a spatial basis. Using the PROSAIL Radiative Transfer Model (RTM), we demonstrated that grassland structural heterogeneity strongly affects the ability to retrieve LAI, with high uncertainties due to structural and biochemical PTs co-variation. The RENDVI783.740 SVI was the least affected by traits co-variation, and more studies are needed to confirm its potential for heterogeneous grasslands LAI monitoring using S-2, S-3, or Gaofen-5 (GF-5) and PRISMA bands.

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Carbon, Water and Energy Fluxes of Terrestrial Ecosystems in Italy

Cited by 10 publications

References 28 publications

P-model v1.0: an optimality-based light use efficiency model for simulating ecosystem gross primary production

P-model v1.0: an optimality-based light use efficiency model for simulating ecosystem gross primary production

Partitioning net carbon dioxide fluxes into photosynthesis and respiration using neural networks

VIS-NIR, Red-Edge and NIR-Shoulder Based Normalized Vegetation Indices Response to Co-Varying Leaf and Canopy Structural Traits in Heterogeneous Grasslands

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