Abstract. The MUREX (monitoring the usable soil reservoir experimentally) experiment was designed to provide continuous time series of field data over a long period, in order to improve and validate the Soil-vegetation-Atmosphere Transfer (SVAT) parameterisations employed in meteorological models. Intensive measurements were performed for more than three years over fallow farmland in southwestern France. To capture the main processes controlling land-atmosphere exchanges, the local climate was fully characterised, and surface water and energy fluxes, vegetation biomass, soil moisture profiles, surface soil moisture and surface and soil temperature were monitored. Additional physiological measurements were carried out during selected periods to describe the biological control of the fluxes. The MUREX data of 1995, 1996, and 1997 are presented. Four SVAT models are applied to the annual cycle of 1995. In general, they succeed in simulating the main features of the fallow functioning, although some shortcomings are revealed.Key words. Hydrology (evapotranspiration; soil moisture; water-energy interactions).
Abstract. In this work, the rich dataset acquired at the SMOSREX experimental site is used to enhance the A-gs version of the Interactions between Soil, Biosphere and Atmosphere (ISBA) model. A simple representation of the soil moisture effect on the ecosystem respiration is implemented in the ISBA-A-gs model. It results in an improvement of the modelled CO 2 flux over a grassland in southwestern France. The former temperature-only dependent respiration formulation used in ISBA-A-gs is not able to model the limitation of the respiration under dry conditions. In addition to soil moisture and soil temperature, the only parameter required in this formulation is the ecosystem respiration parameter Re 25 . It can be estimated by means of eddy covariance measurements of turbulent nighttime CO 2 flux (i.e. ecosystem respiration). The resulting correlation between observed and modelled net ecosystem exchange is r 2 =0.63 with a bias of −2.18 µmol m −2 s −1 . It is shown that when CO 2 observations are not available, it is possible to use a more complex model, able to represent the heterotrophic respiration and all the components of the autotrophic respiration, to estimate Re 25 with similar results. The modelled ecosystem respiration estimates are provided by the Carbon Cycle (CC) version of ISBA (ISBA-CC). ISBA-CC is a version of ISBA able to simulate all the respiration components, whereas ISBA-A-gs uses a single equation for ecosystem respiration. ISBA-A-gs is easier to handle and more convenient than ISBA-CC for the practical use in atmospheric orCorrespondence to: J.-C. Calvet (calvet@meteo.fr) hydrological models. Surface water and energy flux observations, as well as Gross Primary Production (GPP) estimates, are compared with model outputs. The dependence of GPP to air temperature is investigated. The observed GPP is less sensitive to temperature than the modelled GPP. Finally, the simulations of the ISBA-A-gs model are analysed over a seven year period (2001)(2002)(2003)(2004)(2005)(2006)(2007). Modelled soil moisture and Leaf Area Index (LAI) are confronted with the observed surface and root-zone soil moisture content (m 3 m −3 ), and with LAI estimates derived from surface reflectance measurements.
Abstract. The quartz fraction in soils is a key parameter of soil thermal conductivity models. Because it is difficult to measure the quartz fraction in soils, this information is usually unavailable. This source of uncertainty impacts the simulation of sensible heat flux, evapotranspiration and land surface temperature in numerical simulations of the Earth system. Improving the estimation of soil quartz fraction is needed for practical applications in meteorology, hydrology and climate modeling. This paper investigates the use of long time series of routine ground observations made in weather stations to retrieve the soil quartz fraction. Profile soil temperature and water content were monitored at 21 weather stations in southern France. Soil thermal diffusivity was derived from the temperature profiles. Using observations of bulk density, soil texture, and fractions of gravel and soil organic matter, soil heat capacity and thermal conductivity were estimated. The quartz fraction was inversely estimated using an empirical geometric mean thermal conductivity model. Several pedotransfer functions for estimating quartz content from gravimetric or volumetric fractions of soil particles (e.g., sand) were analyzed. The soil volumetric fraction of quartz (fq) was systematically better correlated with soil characteristics than the gravimetric fraction of quartz. More than 60 % of the variance of fq could be explained using indicators based on the sand fraction. It was shown that soil organic matter and/or gravels may have a marked impact on thermal conductivity values depending on which predictor of fq is used. For the grassland soils examined in this study, the ratio of sand-to-soil organic matter fractions was the best predictor of fq, followed by the gravimetric fraction of sand. An error propagation analysis and a comparison with independent data from other tested models showed that the gravimetric fraction of sand is the best predictor of fq when a larger variety of soil types is considered.
Abstract. In this work, a simple representation of the soil moisture effect on the ecosystem respiration is implemented into the A-gs version of the Interactions between Soil, Biosphere, and Atmosphere (ISBA) model. It results in an improvement of the modelled CO2 flux over a grassland, in southwestern France. The former temperature-only dependent respiration formulation used in ISBA-A-gs is not able to model the limitation of the respiration under dry conditions. In addition to soil moisture and soil temperature, the only parameter required in this formulation is the ecosystem respiration parameter Re25. It can be estimated by the mean of eddy covariance measurements of turbulent nighttime CO2 flux (i.e. ecosystem respiration). The resulting correlation between observed and modelled net ecosystem exchange is r2=0.63 with a bias of −2.18 μmol m−2 s−1. It is shown that when CO2 observations are not available, it is possible to use a more complex model, able to represent the heterotrophic respiration and all the components of the autotrophic respiration, to estimate Re25 with similar results. The modelled ecosystem respiration estimates are provided by the Carbon Cycle (CC) version of ISBA (ISBA-CC). ISBA-CC is a version of ISBA able to simulate all the respiration components whereas ISBA-A-gs uses a single equation for ecosystem respiration. ISBA-A-gs is easier to handle and more convenient than ISBA-CC for practical use in atmospheric or hydrological models. Surface water and energy flux observations as well as gross primary production (GPP) estimates are compared with model outputs. The dependence of GPP to air temperature is investigated. The observed GPP is less sensitive to temperature than the modelled GPP. Finally, the simulations of the ISBA-A-gs model are analysed over a seven year period (2001–2007). Modelled soil moisture and leaf area index (LAI) are confronted with the observed root-zone soil moisture content (m3 m−3), and with LAI estimates derived from surface reflectance measurements.
Abstract. Soil moisture is the main driver of temporal changes in values of the soil thermal conductivity. The latter is a key variable in land surface models (LSMs) used in hydrometeorology, for the simulation of the vertical profile of soil temperature in relation to soil moisture. Shortcomings in soil thermal conductivity models tend to limit the impact of improving the simulation of soil moisture in LSMs. Models of the thermal conductivity of soils are affected by uncertainties, especially in the representation of the impact of soil properties such as the volumetric fraction of quartz (q), soil organic matter, and gravels. As soil organic matter and gravels are often neglected in LSMs, the soil thermal conductivity models used in most LSMs represent the mineral fine earth, only. Moreover, there is no map of q and it is often assumed that this quantity is equal to the volumetric fraction of sand. In this study, q values are derived by reverse modelling from the continuous soil moisture and soil temperature sub-hourly observations of the Soil Moisture Observing System – Meteorological Automatic Network Integrated Application (SMOSMANIA) network at 21 grassland sites in southern France, from 2008 to 2015. The soil temperature observations are used to retrieve the soil thermal diffusivity (Dh) at a depth of 0.10 m in unfrozen conditions, solving the thermal diffusion equation. The soil moisture and Dh values are then used together with the measured soil properties to retrieve soil thermal conductivity (λ) values. For ten sites, the obtained λ value at saturation (λsat) cannot be retrieved or is lower than the value corresponding to a null value of q, probably in relation to a high density of grass roots at these sites or to the presence of stones. For the remaining eleven sites, q is negatively correlated with the volumetric fraction of solids other than sand. The impact of neglecting gravels and organic matter on λsat is assessed. It is shown that these factors have a major impact on λsat.
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