Influence of physiological phenology on the seasonal pattern of ecosystem respiration in deciduous forests

Migliavacca, Mirco; Reichstein, Markus; Richardson, Andrew D.; Mahecha, Miguel D.; Cremonese, Edoardo; Delpierre, Nicolas; Galvagno, Marta; Law, B. E.; Wohlfahrt, Georg; Black, T. Andrew; Carvalhais, Nuno; Ceccherini, Guido; Chen, Jiquan; Gobron, Nadine; Koffi, Ernest N.; Munger, J. William; Pérez-Priego, Óscar; Robustelli, Monica; Tomelleri, Enrico; Cescatti, Alessandro

doi:10.1111/gcb.12671

Cited by 62 publications

(47 citation statements)

References 72 publications

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“…This procedure was used, on one hand, to filter out the VI values measured during adverse meteorological conditions (i.e., rainy, foggy, or overcast half-hours [34,48]), and on the other hand, Petach et al [29] suggests to apply a threshold on PAR to reduce the variability of CamNDVI due to changes in illumination conditions. Here, we selected a more conservative threshold than Petach et al [29].…”

Section: Data Filtering and To Compute Daily Vis And Gppmentioning

confidence: 99%

“…Moreover, the increasing number of sites with PhenoCam associated with EC flux measurements open interesting perspectives to evaluate: first, the consistency between PTDs derived from PhenoCam-based VIs and PTDs of ecosystem functioning (physiological phenology, i.e., [48]); second, the direct relationship between PhenoCam-based VIs and GPP. However, to our knowledge, only a few studies pay special attention to the differences between phenology of ecosystem structure and of ecosystem functioning and carbon fluxes [28,32].…”

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confidence: 99%

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Using Near-Infrared-Enabled Digital Repeat Photography to Track Structural and Physiological Phenology in Mediterranean Tree–Grass Ecosystems

Luo

El-Madany

Filippa³

et al. 2018

Remote Sensing

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Tree-grass ecosystems are widely distributed. However, their phenology has not yet been fully characterized. The technique of repeated digital photographs for plant phenology monitoring (hereafter referred as PhenoCam) provide opportunities for long-term monitoring of plant phenology, and extracting phenological transition dates (PTDs, e.g., start of the growing season). Here, we aim to evaluate the utility of near-infrared-enabled PhenoCam for monitoring the phenology of structure (i.e., greenness) and physiology (i.e., gross primary productivity-GPP) at four tree-grass Mediterranean sites. We computed four vegetation indexes (VIs) from PhenoCams: (1) green chromatic coordinates (GCC), (2) normalized difference vegetation index (CamNDVI), (3) near-infrared reflectance of vegetation index (CamNIRv), and (4) ratio vegetation index (CamRVI). GPP is derived from eddy covariance flux tower measurement. Then, we extracted PTDs and their uncertainty from different VIs and GPP. The consistency between structural (VIs) and physiological (GPP) phenology was then evaluated. CamNIRv is best at representing the PTDs of GPP during the Green-up period, while CamNDVI is best during the Dry-down period. Moreover, CamNIRv outperforms the other VIs in tracking growing season length of GPP. In summary, the results show it is promising to track structural

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Section: Data Filtering and To Compute Daily Vis And Gppmentioning

confidence: 99%

mentioning

confidence: 99%

Using Near-Infrared-Enabled Digital Repeat Photography to Track Structural and Physiological Phenology in Mediterranean Tree–Grass Ecosystems

Luo

El-Madany

Filippa³

et al. 2018

Remote Sensing

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“…Post et al (2016) used NEE data from the RO and WÜ sites to estimate parameters for C 3 grass and coniferous forest, as well as FLUXNET data from the Fontainebleau site (FR-Fon) in France, located about 300 km southwest of the Rur catchment (48.4763 • N, 2.7801 • E; e.g., Migliavacca et al, 2015). The validation was based on NEE data from EC sites of corresponding PFTs that were located ∼ 600 km away from the parameter estimation sites.…”

Section: Community Land Model Setupmentioning

confidence: 99%

Evaluation and uncertainty analysis of regional-scale CLM4.5 net carbon flux estimates

Post

Franssen²,

Han³

et al. 2018

Biogeosciences

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Abstract. Modeling net ecosystem exchange (NEE) at the regional scale with land surface models (LSMs) is relevant for the estimation of regional carbon balances, but studies on it are very limited. Furthermore, it is essential to better understand and quantify the uncertainty of LSMs in order to improve them. An important key variable in this respect is the prognostic leaf area index (LAI), which is very sensitive to forcing data and strongly affects the modeled NEE. We applied the Community Land Model (CLM4.5-BGC) to the Rur catchment in western Germany and compared estimated and default ecological key parameters for modeling carbon fluxes and LAI. The parameter estimates were previously estimated with the Markov chain Monte Carlo (MCMC) approach DREAM (zs) for four of the most widespread plant functional types in the catchment. It was found that the catchment-scale annual NEE was strongly positive with default parameter values but negative (and closer to observations) with the estimated values. Thus, the estimation of CLM parameters with local NEE observations can be highly relevant when determining regional carbon balances. To obtain a more comprehensive picture of model uncertainty, CLM ensembles were set up with perturbed meteorological input and uncertain initial states in addition to uncertain parameters. C 3 grass and C 3 crops were particularly sensitive to the perturbed meteorological input, which resulted in a strong increase in the standard deviation of the annual NEE sum (σ NEE ) for the different ensemble members from ∼ 2 to 3 g C m −2 yr −1 (with uncertain parameters) to ∼ 45 g C m −2 yr −1 (C 3 grass) and ∼ 75 g C m −2 yr −1 (C 3 crops) with perturbed forcings. This increase in uncertainty is related to the impact of the meteorological forcings on leaf onset and senescence, and enhanced/reduced drought stress related to perturbation of precipitation. The NEE uncertainty for the forest plant functional type (PFT) was considerably lower (σ NEE ∼ 4.0-13.5 g C m −2 yr −1 with perturbed parameters, meteorological forcings and initial states). We conclude that LAI and NEE uncertainty with CLM is clearly underestimated if uncertain meteorological forcings and initial states are not taken into account.

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“…For the spatial variability of RE (Reref ), even though it has been gradually confirmed to vary systematically with the spatial variation of productivity (GPP or LAI) and temperature [8,[20][21][22]27,50], to our knowledge, this process has not yet been linked to moisture change in satellite-based RE studies at large scales and with a high resolution. Our findings reveal that Reref has a significant linear relation with LSWI mean in both the Tibetan and Inner Mongolian grasslands (Figure 3, p < 0.01), which is also supported by another recent study in northern China [24].…”

Section: Model Evaluationmentioning

confidence: 97%

“…The seasonal dynamics of RE are reflected by f (T, P, W), which is mainly controlled by the temporal variability of temperature [48,49], vegetation productivity [43,50], and water [20][21][22]40]. Especially in the arid and semi-arid ecosystems, water is the main limiting factor of the seasonality of RE [51,52].…”

Section: Representation Of Temporal Variability Of Rementioning

confidence: 99%

A Satellite-Based Model for Simulating Ecosystem Respiration in the Tibetan and Inner Mongolian Grasslands

Ren

et al. 2018

Remote Sensing

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Abstract:It is important to accurately evaluate ecosystem respiration (RE) in the alpine grasslands of the Tibetan Plateau and the temperate grasslands of the Inner Mongolian Plateau, as it serves as a sensitivity indicator of regional and global carbon cycles. Here, we combined flux measurements taken between 2003 and 2013 from 16 grassland sites across northern China and the corresponding MODIS land surface temperature (LST), enhanced vegetation index (EVI), and land surface water index (LSWI) to build a satellite-based model to estimate RE at a regional scale. First, the dependencies of both spatial and temporal variations of RE on these biotic and climatic factors were examined explicitly. We found that plant productivity and moisture, but not temperature, can best explain the spatial pattern of RE in northern China's grasslands; while temperature plays a major role in regulating the temporal variability of RE in the alpine grasslands, and moisture is equally as important as temperature in the temperate grasslands. However, the moisture effect on RE and the explicit representation of spatial variation process are often lacking in most of the existing satellite-based RE models. On this basis, we developed a model by comprehensively considering moisture, temperature, and productivity effects on both temporal and spatial processes of RE, and then, we evaluated the model performance. Our results showed that the model well explained the observed RE in both the alpine (R 2 = 0.79, RMSE = 0.77 g C m −2 day −1 ) and temperate grasslands (R 2 = 0.75, RMSE = 0.60 g C m −2 day −1 ). The inclusion of the LSWI as the water-limiting factor substantially improved the model performance in arid and semi-arid ecosystems, and the spatialized basal respiration rate as an indicator for spatial variation largely determined the regional pattern of RE. Finally, the model accurately reproduced the seasonal and inter-annual variations and spatial variability of RE, and it avoided overestimating RE in water-limited regions compared to the popular process-based model. These findings provide a better understanding of the biotic and climatic controls over spatiotemporal patterns of RE for two typical grasslands and a new alternative up-scaling method for large-scale RE evaluation in grassland ecosystems.

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Influence of physiological phenology on the seasonal pattern of ecosystem respiration in deciduous forests

Cited by 62 publications

References 72 publications

Using Near-Infrared-Enabled Digital Repeat Photography to Track Structural and Physiological Phenology in Mediterranean Tree–Grass Ecosystems

Using Near-Infrared-Enabled Digital Repeat Photography to Track Structural and Physiological Phenology in Mediterranean Tree–Grass Ecosystems

Evaluation and uncertainty analysis of regional-scale CLM4.5 net carbon flux estimates

A Satellite-Based Model for Simulating Ecosystem Respiration in the Tibetan and Inner Mongolian Grasslands

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