Soil water availability strongly modulates soil CO2 efflux in different Mediterranean ecosystems: Model calibration using the Bayesian approach

Soil respiration in drought prone regions is highly dependent on the precipitation regime and soil moisture conditions, which are expected to change in a global warming context. In the present study we used an extensive collection of field chamber measurements of soil respiration (Rs) from forest and grassland sites of centre and south of Portugal distributed over a 10 year period. This data were summarized and analysed with the objective to describe seasonal variability of Rs as affected by soil moisture (Hs) and soil temperature (Ts). A Bayesian framework was used to test the effectiveness of soil bioclimatic models in estimating Rs on a daily and monthly time step. Rs seasonality was similar between sites, reaching a maximum in spring and autumn and a minimum in the dry season (July–September). No differences were observed for Rs between sites with different standing biomass or soil carbon stocks either on an annual or seasonal timescale. Hs, and not Ts, was the driving factor of Rs during most of the year. Ts drove Rs response only above certain Hs limits: 10% for forest sites and 15% for grassland sites leading to a Q10 of 2.01, 1.61 and 1.31 for closed forests, open forests and grasslands, respectively. The Bayesian analysis showed that models using Hs as an independent variable performed better than models driven by Ts alone. Monthly estimates of Rs in grasslands can be predicted by simple climatic models based on Hs but none of them was suitable for forest ecosystems, stressing the need for a process-based approach. This study adds to the evidence that Hs controls Rs fluxes for Mediterranean ecosystems and should always be taken into account for extrapolation purposes.

► Soil respiration was measured in contrasting Mediterranean ecosystems.
► No annual or seasonal differences were found between forests and grasslands.
► Soil respiration climatic-driven models were tested on a daily and monthly time step.
► Models are accurate for monthly estimates on grasslands but not on forests.