114 research outputs found
Implications of carbon cycle steady state assumption for biogeochemical modeling performance and inverse parameter retrieval
We analyze the impacts of the steady state assumption on inverse model parameter
retrieval from biogeochemical models. An inverse model parameterization study using
eddy covariance CO2 flux data was performed with the Carnegie Ames Stanford
Approach (CASA) model under conditions of strict and relaxed carbon cycle steady state
assumption (CCSSA) in order to evaluate both the robustness of the model’s structure for
the simulation of net ecosystem carbon fluxes and the assessment of the CCSSA
effects on simulations and parameter estimation. Net ecosystem production (NEP)
measurements from several eddy covariance sites were compared with NEP estimates
from the CASA model driven by local weather station climate inputs as well as by
remotely sensed fraction of photosynthetically active radiation absorbed by vegetation
and leaf area index. The parameters considered for optimization are directly related to
aboveground and belowground modeled responses to temperature and water availability,
as well as a parameter (h) that relaxed the CCSSA in the model, allowing for site level
simulations to be initialized either as net sinks or sources. A robust relationship was
observed between NEP observations and predictions for most of the sites through the
range of temporal scales considered (daily, weekly, biweekly, and monthly), supporting
the conclusion that the model structure is able to capture the main processes explaining
NEP variability. Overall, relaxing CCSSA increased model efficiency (21%) and
decreased normalized average error ( 92%). Intersite variability was a major source of
variance in model performance differences between fixed (CCSSAf) and relaxed
(CCSSAr) CCSSA conditions. These differences were correlated with mean annual NEP
observations, where an average increase in modeling efficiency of 0.06 per 100 g Cm 2 a 1
(where a is years) of NEP is observed (a < 0.003). The parameter h was found to
be a key parameter in the optimization exercise, generating significant model efficiency
losses when removed from the initial parameter set and parameter uncertainties were
significantly lower under CCSSAr. Moreover, modeled soil carbon stocks were generally
closer to observations once the steady state assumption was relaxed. Finally, we also show
that estimates of individual parameters are affected by the steady state assumption. For
example, estimates of radiation-use efficiency were strongly affected by the CCSSAf
indicating compensation effects for the inadequate steady state assumption, leading to effective and thus biased parameters. Overall, the importance of model structural evaluation
in data assimilation approaches is thus emphasize
Climate control of terrestrial carbon exchange across biomes and continents
Understanding the relationships between climate and carbon exchange by terrestrial ecosystems is critical to predict future levels of atmospheric carbon dioxide because of the potential accelerating effects of positive climate-carbon cycle feedbacks. However, directly observed relationships between climate and terrestrial CO2exchange with the atmosphere across biomes and continents are lacking. Here we present data describing the relationships between net ecosystem exchange of carbon (NEE) and climate factors as measured using the eddy covariance method at 125 unique sites in various ecosystems over six continents with a total of 559 site-years. We find that NEE observed at eddy covariance sites is (1) a strong function of mean annual temperature at mid- and high-latitudes, (2) a strong function of dryness at mid- and low-latitudes, and (3) a function of both temperature and dryness around the mid-latitudinal belt (45°N). The sensitivity of NEE to mean annual temperature breaks down at ∼16 ®C (a threshold value of mean annual temperature), above which no further increase of CO,.2uptake with temperature was observed and dryness influence overrules temperature influence. © 2010 lOP Publishing Ltd
The PROFOUND Database for evaluating vegetation models and simulating climate impacts on European forests
Process-based vegetation models are widely used to predict local and global ecosystem dynamics and climate change impacts. Due to their complexity, they require careful parameterization and evaluation to ensure that projections are accurate and reliable. The PROFOUND Database (PROFOUND DB) provides a wide range of empirical data on European forests to calibrate and evaluate vegetation models that simulate climate impacts at the forest stand scale. A particular advantage of this database is its wide coverage of multiple data sources at different hierarchical and temporal scales, together with environmental driving data as well as the latest climate scenarios. Specifically, the PROFOUND DB provides general site descriptions, soil, climate, CO2, nitrogen deposition, tree and forest stand level, and remote sensing data for nine contrasting forest stands distributed across Europe. Moreover, for a subset of five sites, time series of carbon fluxes, atmospheric heat conduction and soil water are also available. The climate and nitrogen deposition data contain several datasets for the historic period and a wide range of future climate change scenarios following the Representative Concentration Pathways (RCP2.6, RCP4.5, RCP6.0, RCP8.5). We also provide pre-industrial climate simulations that allow for model runs aimed at disentangling the contribution of climate change to observed forest productivity changes. The PROFOUND DB is available freely as a "SQLite" relational database or "ASCII" flat file version (at https://doi.org/10.5880/PIK.2020.006/; Reyer et al., 2020). The data policies of the individual contributing datasets are provided in the metadata of each data file. The PROFOUND DB can also be accessed via the ProfoundData R package (https://CRAN.R- project.org/package=ProfoundData; Silveyra Gonzalez et al., 2020), which provides basic functions to explore, plot and extract the data for model set-up, calibration and evaluation.Peer reviewe
Radiative exchange in forest canopies : the case of coniferous forests
International audienc
Tolerance a la chaleur des taurillons en Guadeloupe. Consequences sur la croissance et la production de la viande
* INRA : centre de Bordeaux Diffusion du document : INRA : centre de Bordeaux Diplôme : Dr. d'Universit
Productive responses of ruminants under high temperature conditions
International audienc
Bioclimatologie des ruminants domestiques en zone tropicale
National audienc
Modele a deux couches d'evapotranspiration de la foret landaise
National audienc
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