67 research outputs found
Influence of leaf area index prescriptions on simulations of heat, moisture, and carbon fluxes
Leaf area index (LAI), the total one-sided surface area of leaf per ground surface area, is a key component of land surface models. The authors investigate the influence of differing, plausible LAI prescriptions on heat, moisture, and carbon fluxes simulated by the Community Atmosphere Biosphere Land Exchange version 1.4b (CABLEv1.4b) model over the Australian continent. A 15-member ensemble monthly LAI dataset is generated using the Moderate Resolution Imaging Spectroradiometer (MODIS) LAI product and gridded observations of temperature and precipitation. Offline simulations lasting 29 years (1980â2008) are carried out at 25-km resolution with the composite monthly means from the MODIS LAI product (control simulation) and compared with simulations using each of the 15-member ensemble monthly varying LAI datasets generated. The imposed changes in LAI did not strongly influence the sensible and latent fluxes, but the carbon fluxes were more strongly affected. Croplands showed the largest sensitivity in gross primary production with differences ranging from â90% to 60%. Plant function types (PFTs) with high absolute LAI and low interannual variability, such as evergreen broadleaf trees, showed the least response to the different LAI prescriptions, while those with lower absolute LAI and higher interannual variability, such as croplands, were more sensitive. The authors show that reliance on a single LAI prescription may not accurately reflect the uncertainty in the simulation of terrestrial carbon fluxes, especially for PFTs with high interannual variability. The study highlights that accurate representation of LAI in land surface models is key to the simulation of the terrestrial carbon cycle. Hence, this will become critical in quantifying the uncertainty in future changes in primary production
Global evaluation of gross primary productivity in the JULES land surface model v3.4.1
This study evaluates the ability of the JULES land surface model (LSM) to simulate
gross primary productivity (GPP) on regional and global scales for 2001â2010. Model
simulations, performed at various spatial
resolutions and driven with a variety of meteorological datasets (WFDEI-GPCC, WFDEI-CRU and
PRINCETON), were compared to the MODIS GPP product, spatially gridded estimates of upscaled
GPP from the FLUXNET network (FLUXNET-MTE) and the CARDAMOM terrestrial carbon cycle
analysis. Firstly, when JULES was driven with the WFDEI-GPCC dataset (at 0.â5°âĂâ0.â5°
spatial resolution), the annual average global GPP simulated by JULES for
2001â2010 was higher than the observation-based estimates (MODIS and FLUXNET-MTE), by
25 and 8âŻ%, respectively, and CARDAMOM estimates by 23âŻ%. JULES was able to
simulate the standard deviation of monthly GPP fluxes compared to
CARDAMOM and the observation-based estimates on global scales. Secondly, GPP
simulated by JULES for various biomes (forests,
grasslands and shrubs) on global and regional scales were compared. Differences among JULES,
MODIS, FLUXNET-MTE and CARDAMOM on global scales were due to differences in simulated
GPP in the tropics. Thirdly, it was shown that spatial resolution (0.â5°âĂâ0.â5°, 1°âĂâ1° and 2°âĂâ2°) had little
impact on simulated GPP on these large scales, with global GPP ranging from
140 to 142âŻPgCâyear<sup>â1</sup>. Finally, the sensitivity of JULES to meteorological driving
data, a major source of model uncertainty, was examined. Estimates of annual average
global GPP were higher when JULES was driven with the PRINCETON meteorological
dataset than when driven with the WFDEI-GPCC dataset by 3âŻPgCâyear<sup>â1</sup>. On regional
scales, differences between the two were observed, with the WFDEI-GPCC-driven
model simulations estimating higher GPP in the tropics (5°âŻNâ5°âŻS)
and the PRINCETON-driven model simulations estimating higher GPP in the
extratropics (30â60°âŻN)
THE PGR NETWORKS IN FRANCE: COLLABORATION OF USERS AND THE GENETIC RESOURCE CENTRE ON SMALL GRAIN CEREALS
Plant genetic resources (PGR) have been used in breeding programs for many decades to produce modern varieties by introducing genes of interest, in particular, resistance genes. Nevertheless, these resources remain underestimated if we focus on abiotic stress tolerance or new agricultural techniques, which consider productivity with regard to the environment. In recent years, new users, such as scientists and farmers, have discovered diverse sources of interest for screening and exploiting natural diversity conserved in PGR collections.In the case of the French cereals PGR Network, a share of the responsibility, based on the knowledge and ability of network members, has been decided in order to better promote the use of PGR. The main species of Triticum (wheat), Hordeum (barley), Secale (rye), ĂTriticosecale (triticale), Avena (oat) genera and their wild relatives are held in the collection. By combining phenotypic and genotypic data, the whole genetic resource collection has been structured into smaller functional groups of accessions, in order to facilitate the access and meet the increasing number of different requirements for the distribution of adapted samples of accessions.New panels are being processed to give breeders and scientists new useful tools to study, for instance, stress resistance or to develop association studies. All these data obtained from the French small grain cereal Network will be progressively available through the INRA Genetic Resource Website (http://urgi.versailles.inra.fr/siregal/siregal/welcome.do)
Ecosystem-atmosphere interactions in the Arctic: using data-model approaches to understand carbon cycle feedbacks
The terrestrial CO2 exchange in the Arctic plays an important role in the global carbon (C) cycle. The
Arctic ecosystems, containing a large amount of organic carbon (C), are experiencing ongoing warming in
recent decades, which is affecting the C cycling and the feedback interactions between its different
components. To improve our understanding of the atmosphere-ecosystem interactions, the Greenland
Ecosystem Monitoring (GEM) program measures ecosystem CO2 exchange and links it to biogeochemical
processes. However, this task remains challenging in northern latitudes due to an insufficient number of
measurement sites, particularly covering full annual cycles, but also the frequent gaps in data affected by
extreme conditions and remoteness. Combining ecosystem models and field observations we are able to study
the underlying processes of Arctic CO2 exchange in changing environments. The overall aim of the research is
to use data-model approaches to analyse the patterns of C exchange and their links to biological processes in
Arctic ecosystems, studied in detail both from a measurement and a modelling perspective, but also from a
local to a pan-arctic scale.
In Paper I we found a compensatory response of photosynthesis (GPP) and ecosystem respiration (Reco),
both highly sensitive to the meteorological drivers (i.e. temperatures and radiation) in Kobbefjord, West
Greenland tundra. This tight relationship led to a relatively insensitive net ecosystem exchange (NEE) to the
meteorology, despite the large variability in temperature and precipitations across growing seasons. This tundra
ecosystem acted as a consistent sink of C (-30 g C m-2), except in 2011 (41 g C m-2), which was associated with
a major pest outbreak. In Paper II we estimated this decrease of C sink strength of 118-144 g C m-2 in the
anomalous year (2011), corresponding to 1210-1470 tonnes C at the Kobbefjord catchment scale. We
concluded that the meteorological sensitivity of photosynthesis and respiration were similar, and hence
compensatory, but we could not explain the causes. Therefore, in Paper III we used a calibrated and validated
version of the Soil-Plant-Atmosphere model to explore full annual C cycles and detail the coupling between
GPP and Reco. From this study we found two key results. First, similar metrological buffering to growing season
reduced the full annual C sink strength by 60%. Second, plant traits control the compensatory effect observed
(and estimated) between gross primary production and ecosystem respiration. Because a site-specific location
is not representative of the entire Arctic, we further evaluated the pan-Arctic terrestrial C cycling using the
CARDAMOM data assimilation system in Paper IV. Our estimates of C fluxes, pools and transit times are in
good agreement with different sources of assimilated and independent data, both at pan-Arctic and local scale.
Our benchmarking analysis with extensively used Global Vegetation Models (GVM) highlights that GVM
modellers need to focus on the vegetation C dynamics, but also the respiratory losses, to improve our
understanding of internal C cycle dynamics in the Arctic.
Data-model approaches generate novel outputs, allowing us to explore C cycling mechanisms and
controls that otherwise would not have been possible to address individually. Also, discrepancies between data
and models can provide information about knowledge gaps and ecological indicators not previously detected
from field observations, emphasizing the unique synergy that models and data are capable of bringing together
Comparative predictions of discharge from an artificial catchment (Chicken Creek) using sparse data
Ten conceptually different models in predicting discharge from the artificial Chicken Creek catchment in North-East Germany were used for this study. Soil texture and topography data were given to the modellers, but discharge data was withheld. We compare the predictions with the measurements from the 6 ha catchment and discuss the conceptualization and parameterization of the models. The predictions vary in a wide range, e.g. with the predicted actual evapotranspiration ranging from 88 to 579 mm/y and the discharge from 19 to 346 mm/y. The predicted components of the hydrological cycle deviated systematically from the observations, which were not known to the modellers. Discharge was mainly predicted as subsurface discharge with little direct runoff. In reality, surface runoff was a major flow component despite the fairly coarse soil texture. The actual evapotranspiration (AET) and the ratio between actual and potential ET was systematically overestimated by nine of the ten models. None of the model simulations came even close to the observed water balance for the entire 3-year study period. The comparison indicates that the personal judgement of the modellers was a major source of the differences between the model results. The most important parameters to be presumed were the soil parameters and the initial soil-water content while plant parameterization had, in this particular case of sparse vegetation, only a minor influence on the results
Influence du mode de coulĂ©e sur la tenue en fatigue dâun alliage cobalt-chrome utilisĂ© en odontologie
Des essais de fatigue ont Ă©tĂ© effectuĂ©s sur des Ă©prouvettes obtenues par coulĂ©e centrifuge dâun alliage cobalt-chrome utilisĂ© en odontologie. La contrainte maximale subie au cours de chaque cycle Ă©tait lĂ©gĂšrement supĂ©rieure Ă la limite dâĂ©lasticitĂ© de lâalliage. Les distributions des nombres de cycles Ă rupture montrent que lâutilisation de mĂ©tal « de rĂ©cupĂ©ration » nâaffecte que faiblement la tenue en fatigue, tandis que la coulĂ©e sous air conduit Ă de meilleurs rĂ©sultats que la coulĂ©e sous vide
Multi-model data fusion as a tool for PUB: example in a Swedish mesoscale catchment
Post-processing the output of different rainfall-runoff models allows one to pool strengths of each model to produce more reliable predictions. As a new approach in the frame of the "Prediction in Ungauged Basins" initiative, this study investigates the geographical transferability of different parameter sets and data-fusion methods which were applied to 5 different rainfall-runoff models for a low-land catchment in Central Sweden. After usual calibration, we adopted a proxy-basin validation approach between two similar but non-nested sub-catchments in order to simulate ungauged conditions.
Many model combinations outperformed the best single model predictions with improvements of efficiencies from 0.70 for the best single model predictions to 0.77 for the best ensemble predictions. However no "best" data-fusion method could be determined as similar performances were obtained with different merging schemes. In general, poorer model performance, i.e. lower efficiency, was less likely to occur for ensembles which included more individual models
Ensemble modelling of nitrogen fluxes: data fusion for a Swedish meso-scale catchment
Model predictions of biogeochemical fluxes at the landscape scale are highly uncertain, both with respect to stochastic (parameter) and structural uncertainty. In this study 5 different models (LASCAM, LASCAM-S, a self-developed tool, SWAT and HBV-N-D) designed to simulate hydrological fluxes as well as mobilisation and transport of one or several nitrogen species were applied to the mesoscale River Fyris catchment in mid-eastern Sweden.
Hydrological calibration against 5 years of recorded daily discharge at two stations gave highly variable results with Nash-Sutcliffe Efficiency (NSE) ranging between 0.48 and 0.83. Using the calibrated hydrological parameter sets, the parameter uncertainty linked to the nitrogen parameters was explored in order to cover the range of possible predictions of exported loads for 3 nitrogen species: nitrate (NO3), ammonium (NH4) and total nitrogen (Tot-N). For each model and each nitrogen species, predictions were ranked in two different ways according to the performance indicated by two different goodness-of-fit measures: the coefficient of determination R2 and the root mean square error RMSE. A total of 2160 deterministic Single Model Ensembles (SME) was generated using an increasing number of members (from the 2 best to the 10 best single predictions). Finally the best SME for each model, nitrogen species and discharge station were selected and merged into 330 different Multi-Model Ensembles (MME). The evolution of changes in R2 and RMSE was used as a performance descriptor of the ensemble procedure.
In each studied case, numerous ensemble merging schemes were identified which outperformed any of their members. Improvement rates were generally higher when worse members were introduced. The highest improvements were achieved for the nitrogen SMEs compiled with multiple linear regression models with R2 selected members, which resulted in the RMSE decreasing by up to 90%
Reproductive success of a marine teleost was correlated with proactive and reactive stressâcoping styles
Influence des conditions de solidification sur les caractéristiques de l'alliage WIRON 88.
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