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Abstract. Soil moisture is of primary importance for predicting the evolution of soil carbon stocks and fluxes, both because it strongly controls organic matter decomposition and because it is predicted to change at global scales in the following decades. However, the soil functions used to model the heterotrophic respiration response to moisture have limited empirical support and introduce an uncertainty of at least 4% in global soil carbon stock predictions by 2100. The necessity of improving the representation of this relationship in models has been highlighted in recent studies. Here we present a data-driven analysis of soil moisture-respiration relations based on 90 soils. With the use of linear models we show how the relationship between soil heterotrophic respiration and different measures of soil moisture is consistently affected by soil properties. The empirical models derived include main effects and moisture interaction effects of soil texture, organic carbon content and bulk density. When compared to other functions currently used in different soil biogeochemical models, we observe that our results can correct biases and reconcile differences within and between such functions. Ultimately, accurate predictions of the response of soil carbon to future climate scenarios will require the integration of soil-dependent moisture-respiration functions coupled with realistic representations of soil water dynamics.

The temperature sensitivity of soil respiration (SR) is often estimated from the seasonal changes in the flux relative to those in soil temperature, and subsequently used in models to interpolate or predict soil fluxes. However, temperature sensitivities derived from seasonal changes in SR (from here on denoted seasonal Q 10 ) may not solely reflect the temperature sensitivity of SR, because seasonal changes in SR can also be affected by other seasonally fluctuating conditions and processes. In this manuscript, we present a case study of how the seasonal Q 10 of SR can be decoupled from the temperature sensitivity of SR. In a mixed temperate forest, we measured SR under vegetations with different leaf strategies: pure evergreen, pure deciduous, and mixed. Seasonal Q 10 was much higher under deciduous than under evergreen canopies. However, at a shorter time scale, both vegetation types exhibited very similar Q 10 values, indicating that the large differences in seasonal Q 10 do not represent differences in the temperature sensitivity of the soil metabolism. The seasonal Q 10 depends strongly on the amplitude of the seasonal changes in SR (SR s ), which, under the particular climatic and edaphic conditions of our forest study site, were significantly larger in deciduous forest. In turn, SR s was positively correlated with the seasonal changes in leaf area index (LAI s ), a measure of the deciduousness of the vegetation. Thus, in this temperate maritime forest, seasonal Q 10 of SR was strongly influenced by the deciduousness of the vegetation. We conclude that the large differences in seasonal Q 10 were not entirely due to different temperature sensitivities, but also to different seasonal patterns of plant activity in the evergreen and deciduous plants of this site. Some coniferous forests may be more seasonal than the one we studied, and the deciduous-evergreen differences observed here may not be broadly applicable, but this case study demonstrates that variation of plant phenological process can significantly contribute to the seasonality of SR, and, hence, calculated Q 10 values. Where this occurs, the seasonal Q 10 value for SR does not accurately represent temperature sensitivity. Because the strong seasonal correlation between SR and temperature does not necessarily imply a causal relationship, Q 10 values derived form annual patterns of SR should be used with caution when predicting future responses of SR to climatic change.

This experiment was designed to study three determinant factors in decomposition patterns of soil organic matter (SOM): temperature, water and carbon (C) inputs. The study combined field measurements with soil lab incubations and ends with a modelling framework based on the results obtained. Soil respiration was periodically measured at an oak savanna woodland and a ponderosa pine plantation. Intact soils cores were collected at both ecosystems, including soils with most labile C burnt off, soils with some labile C gone and soils with fresh inputs of labile C. Two treatments, dry-field condition and field capacity, were applied to an incubation that lasted 111 days. Short-term temperature changes were applied to the soils periodically to quantify temperature responses. This was done to prevent confounding results associated with different pools of C that would result by exposing treatments chronically to different temperature regimes. This paper discusses the role of the above-defined environmental factors on the variability of soil C dynamics. At the seasonal scale, temperature and water were, respectively, the main limiting factors controlling soil CO 2 efflux for the ponderosa pine and the oak savanna ecosystems. Spatial and seasonal variations in plant activity (root respiration and exudates production) exerted a strong influence over the seasonal and spatial variation of soil metabolic activity. Mean residence times of bulk SOM were significantly lower at the Nitrogen (N)-rich deciduous savanna than at the N-limited evergreen dominated pine ecosystem. At shorter time scales (daily), SOM decomposition was controlled primarily by temperature during wet periods and by the combined effect of water and temperature during dry periods. Secondary control was provided by the presence/absence of plant derived C inputs (exudation). Further analyses of SOM decomposition suggest that factors such as changes in the decomposer community, stress-induced changes in the metabolic activity of decomposers or SOM stabilization patterns remain unresolved, but should also be considered in future SOM decomposition studies. Observations and confounding factors associated with SOM decomposition patterns and its temperature sensitivity are summarized in the modeling framework.