Romain Lardy

Regional-scale analysis of carbon and water cycles on managed grassland systems

Predicting regional and global carbon (C) and water dynamics on grasslands has become of major interest, as grasslands are one of the most widespread vegetation types worldwide, providing a number of ecosystem services (such as forage production and C storage). The present study is a contribution to a regional-scale analysis of the C and water cycles on managed grasslands. The mechanistic biogeochemical model PaSim (Pasture Simulation model) was evaluated at 12 grassland sites in Europe. A new parameterization was obtained on a common set of eco-physiological parameters, which represented an improvement of previous parameterization schemes (essentially obtained via calibration at specific sites). We found that C and water fluxes estimated with the parameter set are in good agreement with observations. The model with the new parameters estimated that European grassland are a sink of C with 213?g?C?m-2?yr-1, which is close to the observed net ecosystem exchange (NEE) flux of the studied sites (185?g?C?m-2?yr-1 on average). The estimated yearly average gross primary productivity (GPP) and ecosystem respiration (RECO) for all of the study sites are 1220 and 1006?g?C?m-2?yr-1, respectively, in agreement with observed average GPP (1230?g?C?m-2?yr-1) and RECO (1046?g?C?m-2?yr-1). For both variables aggregated on a weekly basis, the root mean square error (RMSE) was ~5-16?g?C?week-1 across the study sites, while the goodness of fit (R2) was ~0.4-0.9. For evapotranspiration (ET), the average value of simulated ET (415?mm?yr-1) for all sites and years is close to the average value of the observed ET (451?mm?yr-1) by flux towers (on a weekly basis, RMSE~2-8?mm?week-1; R2?=?0.3-0.9). However, further model development is needed to better represent soil water dynamics under dry conditions and soil temperature in winter. A quantification of the uncertainties introduced by spatially generalized parameter values in C and water exchange estimates is also necessary. In addition, some uncertainties in the input management data call for the need to improve the quality of the observational system. A mechanistic biogeochemical pasture simulation model (PaSim) is improved by using a common set of eco-physiological parameters.PaSim was evaluated at 12 grassland sites in Europe, performing regional-scale analysis of carbon and water cycles.PaSim estimated that European grasslands are a carbon sink of 213?g?C?m-2?yr-1.PaSim overestimated the soil water content during dry periods.

Short communication: A new method to determine soil organic carbon equilibrium

This work addresses the dynamical behaviour of the Pasture Simulation Model (PaSim), with respect to the equilibrium conditions for the five carbon (C) pools (structural, metabolic, active, slow, and passive) of soil organic matter (SOM) decomposition, which are modelled according to CENTURY. A novel algebraic approach, based on a sequence of matrices and formulated using the Gauss-Jordan (G-J) elimination algorithm (stable and efficient in memory usage), was proposed and compared to a native iterative procedure using soil C data from 13 European grassland sites. The advantage of the algebraic approach over the iterative method is an enhanced accuracy of C allocation to soil pools and a faster convergence (6-20 times). Its value was discussed in the context of SOM research and modelling.

Sensitivity analysis for models of greenhouse gas emissions at farm level. Case study of N2O emissions simulated by the CERES-EGC model

Modelling complex systems such as farms often requires quantification of a large number of input factors. Sensitivity analyses are useful to reduce the number of input factors that are required to be measured or estimated accurately. Three methods of sensitivity analysis (the Morris method, the rank regression and correlation method and the Extended Fourier Amplitude Sensitivity Test method) were compared in the case of the CERES-EGC model applied to crops of a dairy farm. The qualitative Morris method provided a screening of the input factors. The two other quantitative methods were used to investigate more thoroughly the effects of input factors on output variables. Despite differences in terms of concepts and assumptions, the three methods provided similar results. Among the 44 factors under study, N(2)O emissions were mainly sensitive to the fraction of N(2)O emitted during denitrification, the maximum rate of nitrification, the soil bulk density and the cropland area.

On the Use of Hydrological Models and Satellite Data to Study the Water Budget of River Basins Affected by Human Activities: Examples from the Garonne Basin of France

Natural and anthropogenic forcing factors and their changes significantly impact water resources in many river basins around the world. Information on such changes can be derived from fine scale in situ and satellite observations, used in combination with hydrological models. The latter need to account for hydrological changes caused by human activities to correctly estimate the actual water resource. In this study, we consider the catchment area of the Garonne river (in France) to investigate the capabilities of space-based observations and up-to-date hydrological modeling in estimating water resources of a river basin modified by human activities and a changing climate. Using the ISBA–MODCOU and SWAT hydrological models, we find that the water resources of the Garonne basin display a negative climate trend since 1960. The snow component of the two models is validated using the moderate-resolution imaging spectroradiometer snow cover extent climatology. Crop sowing dates based on remote sensing studies are also considered in the validation procedure. Use of this dataset improves the simulated evapotranspiration and river discharge amounts when compared to conventional data. Finally, we investigate the benefit of using the MAELIA multi-agent model that accounts for a realistic agricultural and management scenario. Among other results, we find that changes in crop systems have significant impacts on water uptake for agriculture. This work constitutes a basis for the construction of a future modeling framework of the sociological and hydrological system of the Garonne river region.

Modelling the transfer of 14C from the atmosphere to grass: A case study in a grass field near AREVA-NC La Hague

Radioactive (14)C is formed as a by-product of nuclear power generation and from operation of nuclear fuel reprocessing plants like AREVA-NC La Hague (North France), which releases about 15 TBq per year of (14)C into the atmosphere. Since the autumn of 2006, (14)C activity concentrations in samples from the terrestrial environment (air, grass and soil) have been monitored monthly on grassland 2 km downwind of the reprocessing plant. The monitoring data provides an opportunity to validate radioecology models used to assess (14)C transfer to grassland ecosystems. This article compares and discusses the ability of two different models to reproduce the observed temporal variability in grass (14)C activity in the vicinity of AREVA-NC La Hague. These two models are the TOCATTA model which is specifically designed for modelling transfer of (14)C and tritium in the terrestrial environment, and PaSim, a pasture model for simulating grassland carbon and nitrogen cycling. Both TOCATTA and PaSim tend to under-estimate the magnitude of observed peaks in grass (14)C activity, although they reproduce the general trends. PaSim simulates (14)C activities in substrate and structural pools of the plant. We define a mean turn-over time for (14)C within the plant, which is based on both experimental data and the frequency of cuts. An adapted PaSim result is presented using the 15 and 20 day moving average results for the (14)C activity in the substrate pool, which shows a good match to the observations. This model reduces the Root Mean Square Error (RMSE) by nearly 40% in comparison to TOCATTA.

The TOCATTA-χ model for assessing 14C transfers to grass: an evaluation for atmospheric operational releases from nuclear facilities

Radioactive (14)C is formed as a by-product of nuclear power generation and from the operation of nuclear fuel reprocessing plants like AREVA-NC La Hague (North France), which releases about 15 TBq per year of (14)C into the atmosphere. This article evaluates a recently improved radioecology model (TOCATTA-χ) to assess (14)C transfers to grassland ecosystems under normal operating conditions. The new version of the TOCATTA model (TOCATTA-χ) includes developments that were derived from PaSiM, a pasture model for simulating grassland carbon and radiocarbon cycling. The TOCATTA-χ model has been tested against observations of (14)C activity concentrations in grass samples collected monthly from six plots which are located around the periphery of the reprocessing plant. Simulated (14)C activities are consistent with observations on both intensively managed and poorly managed grasslands, but an adaptation of the mean turn-over time for (14)C within the plant is necessary in the model to account for different management practices. When atmospheric (14)C activity concentrations are directly inferred from observations, TOCATTA-χ performs better than TOCATTA (the root mean square error is decreased by 45%), but when atmospheric (14)C activity concentrations are not known and must be calculated, the uncertainty associated with the TOCATTA-χ model outcomes is estimated to be larger than the standard deviation of the observations.

The MAELIA Multi-Agent Platform for Integrated Analysis of Interactions Between Agricultural Land-Use and Low-Water Management Strategies

The MAELIA project is developing an agent-based modeling and simulation platform to study the environmental, economic and social impacts of various regulations regarding water use and water management in combination with climate change. It is applied to the case of the French Adour-Garonne Basin, which is the most concerned in France by water scarcity during the low-water period. An integrated approach has been chosen to model this social-ecological system: the model combines spatiotemporal models of ecologic (e.g. rainfall and temperature changes, water flow and plant growth) and socio-economic (e.g. farmer decision-making process, management of low-water flow, demography, land use and land cover changes) processes and sub-models of cognitive sharing among agents (e.g. weather forecast, normative constraints on behaviors)

Vuln-Indices

Vulnerability assessment to climate change is an issue of concern.We develop Java-based software for vulnerability assessment to climate change.We illustrate the software in vulnerability assessments of European grasslands. Vuln-Indices Java-based software was developed on concepts of vulnerability to climate change of agro-ecological systems. It implements the calculation of vulnerability indices on series of state variables for assessments at both site and region levels. The tool is useful because synthetic indices help capturing complex processes and prove effective to identify the factors responsible for vulnerability and their relative importance. It is suggested that the tool may be plausible for use with stakeholders to disseminate information of climate change impacts.