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The quartz fraction in soils is a key parameter of soil thermal conductivitymodels. Because it is difficult to measure the quartz fraction in soils, thisinformation is usually unavailable. This source of uncertainty impacts thesimulation of sensible heat flux, evapotranspiration and land surfacetemperature in numerical simulations of the Earth system. Improving theestimation of soil quartz fraction is needed for practical applications inmeteorology, hydrology and climate modeling. This paper investigates the useof long time series of routine ground observations made in weather stationsto retrieve the soil quartz fraction. Profile soil temperature and watercontent were monitored at 21 weather stations in southern France. Soilthermal diffusivity was derived from the temperature profiles. Usingobservations of bulk density, soil texture, and fractions of gravel and soilorganic matter, soil heat capacity and thermal conductivity were estimated.The quartz fraction was inversely estimated using an empirical geometric meanthermal conductivity model. Several pedotransfer functions for estimatingquartz 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 soilcharacteristics than the gravimetric fraction of quartz. More than 60 %of the variance of fq could be explained using indicators basedon the sand fraction. It was shown that soil organic matter and/or gravelsmay have a marked impact on thermal conductivity values depending on whichpredictor of fq is used. For the grassland soils examined in thisstudy, the ratio of sand-to-soil organic matter fractions was the bestpredictor of fq, followed by the gravimetric fraction of sand. Anerror propagation analysis and a comparison with independent data from othertested models showed that the gravimetric fraction of sand is the bestpredictor of fq when a larger variety of soil types isconsidered

Deriving pedotransfer functions for soil quartz fraction in southern France from reverse modeling