Effect of temperature and moisture on rates of carbon mineralization in a Mediterranean oak forest soil under controlled and field conditions

Abstract

We examined the relationship between carbon mineralization (C min ), moisture and temperature in a Mediterranean forest soil under controlled and field conditions. We studied the following. 1 The temperature sensitivity at three soil depths: soil samples were incubated at 4, 10, 20 and 30 C at optimal water content of 60% of water holding capacity (WHC). Values of C min of the top layer were more than 12 times faster than those measured in deeper layers. We found a temperature sensitivity factor (Q 10 ) of 3.3, 2.7 and 2.2 for the 0-5 cm, 5-10 cm and 10-20 cm layers, respectively. 2 The relationship between C min , moisture and temperature (top layer). The sensitivity of C min to fluctuating moisture depended on temperature. However, the Q 10 was not significantly affected by soil moisture. We fitted a multiple polynomial model that predicted C min as a multiplicative function of temperature and moisture (R 2 > 0.99).3 The response of C min of soil to rewetting after 1 and 24 hours. In all cases, the response was rapid. The soil incubated at 60% WHC or less responded positively to a sudden increase in water content, with the largest increase in the 20% WHC treatment. The model predicted C min in the field well when rewetting effects were taken into account (R 2 > 0.81). These results indicate that sudden changes in soil moisture can lead to increased carbon mineralization during the dry summer. It is necessary to include such responses in models as they may represent a substantial loss of carbon in the overall carbon balance of Mediterranean ecosystems.

Diffusion based modelling of temperature and moisture interactive effects on carbon fluxes of mineral soils

“…Simulation of the incubated soil from the study of Rey et al (2005) resulted in a very high fit to the validation data, with an RMSE of 0.09 in fluxes that were almost an order of magnitude higher than those used for calibration, and a model R 2 of 0.99 ( Figure 6). This was reflected in a generally good agreement between the relationships of model and observations with moisture 15 ( Figure 7) and temperature (Figure 8).…”

“…Moisture and temperature interactions have been observed in a number of experimental studies (Craine and Gelderman, 2011;Rey et al, 2005;Suseela et al, 2012;Wickland and Neff, 2008), but neither consistent trends 30 nor general explanatory theories have been identified. Improving our understanding of these interactions is a crucial step in increasing confidence in models and for interpreting modelling and experimental results (Crowther et al, 2016;Tang and Riley, 2014).…”

“…For model validation, we used soil respiration data from the study by Rey et al (2005) where a Mediterranean oak forest soil 20 was incubated for one month in a full factorial design at 100, 80, 60, 40 and 20 % of water holding capacity and at 30,20,10 and 4 °C. This soil differed from the one used for model calibration in at least 3 aspects: the amount of organic C (7 %), soil pore space (65 %), and texture (classified as silty clay loam).…”

Diffusion limitations and Michaelis–Menten kinetics as drivers of combined temperature and moisture effects on carbon fluxes of mineral soils

“…For model validation, we used soil respiration data from the study by Rey et al (2005), where a Mediterranean oak forest soil was incubated for 1 month in a full factorial design at 100 %, 80 %, 60 %, 40 %, and 20 % of water holding capacity and at 30, 20, 10, and 4 • C. This soil differed from the one used for model calibration in at least three aspects: the amount of organic C (7 %), soil pore space (65 %), and texture (classified as silty clay loam). The optimized set of parameters from the model M2-dif was used with the exception of the initial fraction of C pools (f P , f D , f M ) and the percolation threshold (θ th ), which we calibrated against the new data (Nelder-Mead calibration).…”

“…Moisture and temperature interactions have been observed in a number of experimental studies (Craine and Gelderman, 2011;Rey et al, 2005;Suseela et al, 2012;Wickland and Neff, 2008), but neither consistent trends nor general explanatory theories have been identified. Improving our understanding of these interactions is a crucial step towards increasing confidence in models and important for interpreting modelling and experimental results (Crowther et al, 2016;Tang and Riley, 2014).…”