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See detailModelling carbon fluxes of forest and grassland ecosystems in Western Europe using the CARAIB dynamic vegetation model: evaluation against eddy covariance data.
Henrot, Alexandra-Jane ULg; François, Louis ULg; Dury, Marie ULg et al

in Geophysical Research Abstracts (2015, April), 17

Eddy covariance measurements are an essential resource to understand how ecosystem carbon fluxes react in response to climate change, and to help to evaluate and validate the performance of land surface ... [more ▼]

Eddy covariance measurements are an essential resource to understand how ecosystem carbon fluxes react in response to climate change, and to help to evaluate and validate the performance of land surface and vegetation models at regional and global scale. In the framework of the MASC project (« Modelling and Assessing Surface Change impacts on Belgian and Western European climate »), vegetation dynamics and carbon fluxes of forest and grassland ecosystems simulated by the CARAIB dynamic vegetation model (Dury et al., iForest - Biogeosciences and Forestry, 4:82-99, 2011) are evaluated and validated by comparison of the model predictions with eddy covariance data. Here carbon fluxes (e.g. net ecosystem exchange (NEE), gross primary productivity (GPP), and ecosystem respiration (RECO)) and evapotranspiration (ET) simulated with the CARAIB model are compared with the fluxes measured at several eddy covariance flux tower sites in Belgium and Western Europe, chosen from the FLUXNET global network (http://fluxnet.ornl.gov/). CARAIB is forced either with surface atmospheric variables derived from the global CRU climatology, or with in situ meteorological data. Several tree (e.g. Pinus sylvestris, Fagus sylvatica, Picea abies) and grass species (e.g. Poaceae, Asteraceae) are simulated, depending on the species encountered on the studied sites. The aim of our work is to assess the model ability to reproduce the daily, seasonal and interannual variablility of carbon fluxes and the carbon dynamics of forest and grassland ecosystems in Belgium and Western Europe. [less ▲]

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See detailunité: Eau-Environnement-Développement (ULg Campus Arlon): la télédétection au service de l'agriculture
Wellens, Joost ULg; Lang, Marie ULg; Benabdelouahab, Tarik et al

Diverse speeche and writing (2015)

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See detailRumination time, milk yield, milking frequency of grazing dairy cows milked by a mobile automatic system during mild heat stress
Lessire, Françoise ULg; Hornick, Jean-Luc ULg; Minet, Julien ULg et al

in Advances in Animal Biosciences (2015), 6(01), 12-14

Grazing dairy cows milked by an automatic system (AS) experienced mild heat stress (HS) periods, twice during the summer. The daily temperature humidity index (THI) during these periods were higher than ... [more ▼]

Grazing dairy cows milked by an automatic system (AS) experienced mild heat stress (HS) periods, twice during the summer. The daily temperature humidity index (THI) during these periods were higher than 72. Milk production, as well as milking frequency, rumination time and milk fat to protein ratio (F/P) during these periods were compared to adjacent periods with mean THI of 61. The daily milking frequency, the total number of visits to AS and the milk production were significantly higher in HS periods (2.12 vs 1.97, 2.99 vs 2.69, and 19.7 vs 18.5 kg milk per cow, respectively). There were significant interactions between times and periods for milking frequency and number of visits, while the daily rumination time was significantly lower (339 vs 419 min) and the F/P in milk tended to be decreased (1.17 vs 1.23). These results could be explained by changes in cow behaviour during HS periods. [less ▲]

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See detailWheat yield sensitivity to climate change across a European transect for a large ensemble of crop models
Pirttioja, N.; Carter, Timothy; Fronzek, S. et al

in Soussana, Jean-Francois (Ed.) Proceedings of the Climate Smart Agriculture 2015 conference (2015, March)

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See detailBayesian inversions of a dynamic vegetation model at four European grassland sites
Minet, Julien ULg; Laloy, Eric; Tychon, Bernard ULg et al

in Biogeosciences (2015), 12(9), 2809--2829

Eddy covariance data from four European grassland sites are used to probabilistically invert the CARAIB (CARbon Assimilation In the Biosphere) dynamic vegetation model (DVM) with 10 unknown parameters ... [more ▼]

Eddy covariance data from four European grassland sites are used to probabilistically invert the CARAIB (CARbon Assimilation In the Biosphere) dynamic vegetation model (DVM) with 10 unknown parameters, using the DREAM(ZS) (DiffeRential Evolution Adaptive Metropolis) Markov chain Monte Carlo (MCMC) sampler. We focus on comparing model inversions, considering both homoscedastic and heteroscedastic eddy covariance residual errors, with variances either fixed a priori or jointly inferred together with the model parameters. Agreements between measured and simulated data during calibration are comparable with previous studies, with root mean square errors (RMSEs) of simulated daily gross primary productivity (GPP), ecosystem respiration (RECO) and evapotranspiration (ET) ranging from 1.73 to 2.19, 1.04 to 1.56 g C m−2 day−1 and 0.50 to 1.28 mm day−1, respectively. For the calibration period, using a homoscedastic eddy covariance residual error model resulted in a better agreement between measured and modelled data than using a heteroscedastic residual error model. However, a model validation experiment showed that CARAIB models calibrated considering heteroscedastic residual errors perform better. Posterior parameter distributions derived from using a heteroscedastic model of the residuals thus appear to be more robust. This is the case even though the classical linear heteroscedastic error model assumed herein did not fully remove heteroscedasticity of the GPP residuals. Despite the fact that the calibrated model is generally capable of fitting the data within measurement errors, systematic bias in the model simulations are observed. These are likely due to model inadequacies such as shortcomings in the photosynthesis modelling. Besides the residual error treatment, differences between model parameter posterior distributions among the four grassland sites are also investigated. It is shown that the marginal distributions of the specific leaf area and characteristic mortality time parameters can be explained by site-specific ecophysiological characteristics. [less ▲]

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See detailOutcomes from the MACSUR grassland model inter-comparison with the model CARAIB
Minet, Julien ULg; Laloy, Eric; Tychon, Bernard ULg et al

Conference (2014, October 15)

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See detailThe grassland model intercomparison of the MACSUR (Modelling European Agriculture with Climate Change for Food Security) European knowledge hub
Ma, Shaoxiu; Acutis, Marco; Barcza, Zoltan et al

in Proceedings of the 7th International Congress on Environmental Modelling and Software (iEMSs) June 15-19, 2014, San Diego, California, USA (2014, June)

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See detailBayesian inference of a dynamic vegetation model for grassland
Minet, Julien ULg; Laloy, Eric; Tychon, Bernard ULg et al

Conference (2014, April 02)

As a part of the MACSUR task L2.4, we probabilistically calibrated the CARAIB dynamic vegetation model by Markov chain Monte Carlo (MCMC) simulation with the DREAMZS sampler.. CARAIB is a mechanistic ... [more ▼]

As a part of the MACSUR task L2.4, we probabilistically calibrated the CARAIB dynamic vegetation model by Markov chain Monte Carlo (MCMC) simulation with the DREAMZS sampler.. CARAIB is a mechanistic model that calculates the carbon assimilation of the vegetation as a function of the soil and climatic conditions, and can thus be used for simulating grassland production under cutting or grazing management. Bayesian model inversion was performed at 4 grassland sites across Europe: Oensingen, CH; Grillenburg, DE; Laqueuille, FR and Monte-Bodone, IT. Four daily measured variables from these sites: the Gross Primary Productivity (GPP), Net Ecosystem Exchange (NEE), Evapotranspiration (ET) and Soil Water Content (SWC) were used to sample 10 parameters related to rooting depth, stomatal conductance, specific leaf area, carbon-nitrogen ratio and water stresses. The maximized likelihood function therefore involved four objectives, whereas the applied Bayesian framework allowed for assessing the so called parameter posterior probability density function (pdf), which quantifies model parameter uncertainty caused by measurement and model errors. Sampling trials were performed using merged data from all sites (all-sites-sampling) and for each site (site-specific sampling) separately. The derived posterior parameter pdfs from the all-sites sampling and site-specific sampling runs showed differences in relation with the specificities of each site. Analysis of these distributions also revealed model sensitivity to parameters conditioned on the measured data, as well as parameter correlations. [less ▲]

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See detailExamining wheat yield sensitivity to temperature and precipitation changes for a large ensemble of crop models using impact response surfaces"
Pirttioja, N.; Fronzek, S.; Bindi, Marco et al

in Rotter, Reimund; Ewert, Frank (Eds.) Modelling climate change impacts on crop production for food security - Abstract book (2014, February)

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See detailCan a global dynamic vegetation model be used for both grassland and crop modeling at the local scale?
Minet, Julien ULg; Tychon, Bernard ULg; Jacquemin, Ingrid ULg et al

Poster (2014, February)

We report on the use of a dynamic vegetation model, CARAIB, within two modeling exercises in the framework of MACSUR. CARAIB is a physically-based, mechanistic model that calculates the carbon ... [more ▼]

We report on the use of a dynamic vegetation model, CARAIB, within two modeling exercises in the framework of MACSUR. CARAIB is a physically-based, mechanistic model that calculates the carbon assimilation of the vegetation as a function of the soil and climatic conditions. Within MACSUR, it was used in the model intercomparison exercises for grassland and crop modeling, in the LiveM 2.4 and CropM WP4 tasks, respectively. For grassland modeling, blind model runs at 11 locations were performed for various time ranges (few years). For crop modeling, a sensitivity analysis for building impact response surfaces (IRS) was performed, based on a bench of model runs at different levels of perturbation in the temperature and precipitation input data over 30 years. For grassland modeling, specific management functions accounting for the cutting or grazing of the grass were added to the model, in the framework of the MACSUR intercomparison. Initially developed for modeling the carbon dynamics of the natural vegetation, CARAIB was already adapted for crop modeling but further modifications regarding the management, i.e., yearly-dependent sowing dates, were introduced. For grassland modeling, simulation results will be further intercompared with other modeling groups, but preliminary results showed that the model could cope with the introduction of the grass cutting module. For crop modeling, building the IRS over 30 years permitted to assess the sensitivity of the model to temperature and precipitation changes. So far, the participation of CARAIB in the intercomparison exercises within MACSUR resulted in further improvements of the model by introducing new functionalities. [less ▲]

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See detailImplementing agricultural land-use in the CARAIB dynamic vegetation model
François, Louis ULg; Jacquemin, Ingrid ULg; Fontaine, Corentin et al

Conference (2014)

CARAIB (Dury et al., iForest - Biogeosciences and Forestry, 4:82-99, 2011) is a state-of-the-art dynamic vegetation model with various modules dealing with (i) soil hydrology, (ii) photosynthesis/stomatal ... [more ▼]

CARAIB (Dury et al., iForest - Biogeosciences and Forestry, 4:82-99, 2011) is a state-of-the-art dynamic vegetation model with various modules dealing with (i) soil hydrology, (ii) photosynthesis/stomatal regulation, (iii) carbon allocation and biomass growth, (iv) litter/soil carbon dynamics, (v) vegetation cover dynamics, (vi) seed dispersal, and (vii) vegetation fires. Climate and atmospheric CO2 are the primary inputs. The model calculates all major water and CO2/carbon fluxes and pools. It can be run with plant functional types or species (up to 100 different species) at various spatial scales, from the municipality to country or continental levels. Within the VOTES project (Fontaine et al., Journal of Land Use Science, 2013, DOI:10.1080/1747423X.2013.786150), the model has been improved to include crops and meadows, and some modules have been written to translate model outputs into quantitative indicators of ecosystem services (e.g., evaluate crop yield from net primary productivity or calculate soil erosion from runoff, slope, grown species and various soil attributes). The model was run over an area covering four municipalities in central Belgium, where land-use is dominated by crops, meadows, housing and some forests and was introduced in the model at the land parcel level. Simulations were also performed for the future. In these simulations, CARAIB was combined with the Aporia Agent-Based Model, to project land-use changes up to 2050. This approach is currently extended within the MASC project (funded by Belgian Science Policy, BELSPO) to the whole Belgian territory (at 1 km2) and to Western Europe (at 20 km x 20 km). [less ▲]

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See detailCARAIB USER'S GUIDE
Minet, Julien ULg; Jacquemin, Ingrid ULg; François, Louis ULg

Learning material (2013)

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See detailCommunity gardening in Wallonia and Brussels : proposals for research and actions
Minet, Julien ULg; Stevenne, Kari; Loicq, Gaël et al

Scientific conference (2013, May 23)

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See detailValidation of ground penetrating radar full-waveform inversion for field scale soil moisture mapping
Minet, Julien ULg; Bogaert, P.; Vanclooster, M. et al

in Journal of Hydrology (2012), 424-425

Ground penetrating radar (GPR) is an efficient method for soil moisture mapping at the field scale, bridging the scale gap between small-scale invasive sensors and large-scale remote sensing instruments ... [more ▼]

Ground penetrating radar (GPR) is an efficient method for soil moisture mapping at the field scale, bridging the scale gap between small-scale invasive sensors and large-scale remote sensing instruments. Nevertheless, commonly-used GPR approaches for soil moisture characterization suffer from several limitations and the determination of the uncertainties in GPR soil moisture sensing has been poorly addressed. Herein, we used an advanced proximal GPR method based on full-waveform inversion of ultra-wideband radar data for mapping soil moisture and uncertainties in the soil moisture maps were evaluated by three different methods. First, GPR-derived soil moisture uncertainties were computed from the GPR data inversion, according to measurements and modeling errors and to the sensitivity of the electromagnetic model to soil moisture. Second, the reproducibility of the soil moisture mapping was evaluated. Third, GPR-derived soil moisture was compared with ground-truth measurements (soil core sampling). The proposed GPR method appeared to be highly precise and accurate, with spatially averaged GPR inversion uncertainty of 0.0039 m3m-3, a repetition uncertainty of 0.0169 m3m-3 and an uncertainty of 0.0233 m3m-3 when compared with ground-truth measurements. These uncertainties were mapped and appeared to be related to some local model inadequacies and to small-scale variability of soil moisture. In a soil moisture mapping framework, the interpolation was found to be the determinant source of the observed uncertainties. The proposed GPR method was proven to be largely reliable in terms of accuracy and precision and appeared to be highly efficient for soil moisture mapping at the field scale. [less ▲]

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See detailIntegrating coarse-scale uncertain soil moisture data into a fine-scale hydrological modelling scenario
Vernieuwe, H.; De Baets, B.; Minet, Julien ULg et al

in Hydrology and Earth System Sciences (2011), 15

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See detailEffect of high-resolution spatial soil moisture variability on simulated runoff response using a distributed hydrologic model
Minet, Julien ULg; Laloy, E.; Lambot, S. et al

in Hydrology and Earth System Sciences (2011), 15

The importance of the spatial variability of antecedent soil moisture conditions on runoff response is widely acknowledged in hillslope hydrology. Using a distributed hydrologic model, this paper aims at ... [more ▼]

The importance of the spatial variability of antecedent soil moisture conditions on runoff response is widely acknowledged in hillslope hydrology. Using a distributed hydrologic model, this paper aims at investigating the effects of soil moisture spatial variability on runoff in various field conditions and at finding the structure of the soil moisture pattern that approaches the measured soil moisture pattern in terms of field scale runoff. High spatial resolution soil moisture was surveyed in ten different field campaigns using a proximal ground penetrating radar (GPR) mounted on a mobile platform. Based on these soil moisture measurements, seven scenarios of spatial structures of antecedent soil moisture were used and linked with a field scale distributed hydrological model to simulate field scale runoff. Accounting for spatial variability of soil moisture resulted in higher predicted field scale runoff as compared to the case where soil moisture was kept constant. The ranges of possible hydrographs were delineated by the extreme scenarios where soil moisture was directly and inversely modelled according to the topographic wetness index (TWI). These behaviours could be explained by the sizes and relative locations of runoff contributing areas, knowing that runoff was generated by infiltration excess over a certain soil moisture threshold. The most efficient scenario for modeling the within field spatial structure of soil moisture appeared to be when soil moisture is directly arranged according to the TWI, especially when measured soil moisture and TWI were correlated. The novelty of this work is to benefit from a large set of high-resolution soil moisture measurements allowing to model effectively the within field distribution of soil moisture and its impact on the field scale hydrograph. These observations contributed to the current knowledge of the impact of antecedent soil moisture spatial variability on the field scale runoff. [less ▲]

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See detailReconstruction of sub-wavelength fractures and physical properties of masonry media using full-waveform inversion of proximal penetrating radar
Patriarca, C.; Lambot, S.; Mahmoudzadeh, M. R. et al

in Journal of Applied Geophysics (2011), 74(1)

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See detailMapping shallow soil moisture profiles at the field scale using full-waveform inversion of ground penetrating radar data
Minet, Julien ULg; Wahyudi, A.; Bogaert, P. et al

in Geoderma (2011), 161

Full-waveform inversions were applied to retrieve surface, two-layered and continuous soil moisture profiles from ground penetrating radar (GPR) data acquired in an 11-ha agricultural field situated in ... [more ▼]

Full-waveform inversions were applied to retrieve surface, two-layered and continuous soil moisture profiles from ground penetrating radar (GPR) data acquired in an 11-ha agricultural field situated in the loess belt area in central Belgium. The radar system consisted of a vector network analyzer combined with an off-ground horn antenna operating in the frequency range 200–2000 MHz. The GPR system was computer controlled and synchronized with a differential GPS for real-time data acquisition. Several inversion strategies were also tested using numerical experiments, which in particular demonstrated the potentiality to reconstruct simplified two-layered configurations from more complex, continuous dielectric profiles as prevalent in the environment. The surface soil moisture map obtained assuming a one-layered model showed a global moisture pattern mainly explained by the topography while local moisture patterns indicated a line effect. Two-layered and profile inversions provided consistent estimates with respect to each other and field observations, showing significant moisture increases with depth. However, some discrepancies were ob- served between the measured and modeled GPR data in the higher frequency ranges, mainly due to surface roughness effects which were not accounted for. The proposed GPR method and inversion strategies showed great promise for high-resolution, real-time mapping of soil moisture at the field scale. [less ▲]

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