3 research outputs found
Modelling soil organic carbon stocks in global change scenarios: a CarboSOIL application
39 pages, 7 figures, 5 tables, 67 references.Global climate change, as a consequence of the increasing levels of atmospheric CO2
concentration, may significantly affect both soil organic C storage and soil capacity
for C sequestration. In this research we develop a methodology to predict soil organic C (SOC) contents and changes under global change scenarios. CarboSOIL model is
a new component of the land evaluation decision support system MicroLEIS, which
was designed to assist decision makers to face specific agro-ecological problems. CarboSOIL,
developed as a GIS tool to predict SOC contents at different depths, was
previously trained and tested in two Mediterranean areas: Andalusia (SW Spain) and
Valencia (E Spain). The model was applied under different IPPC scenarios (A1B, A2
and B1) according to different global climate models (BCCR-BCM2, CNRMCM3 and
ECHAM5) and output data were linked to spatial datasets (soil and land use) to quantify
SOC stocks. CarboSOIL model has proved its ability to predict the short-, medium and
long-term trends (2040s, 2070s and 2100s) of SOC dynamics and sequestration
under projected future scenarios of climate change. Results showed an overall trend
towards decreasing of SOC stocks in the upper soil sections (0–25cm and 25–50 cm)
for most soil types and land uses, but predicted SOC stocks tend to increase in the
deeper soil section (50–75 cm). Soil types as Arenosols, Planosols and Solonchaks
and land uses as “permanent crops” and “open spaces with little or no vegetation”
would be severely affected by climate change with large decreases of SOC stocks, in
particular under the medium-high emission scenario A2 by 2100. The information developed
in this study might support decision-making in land management and climate
adaptation strategies in Mediterranean regions and the methodology could be applied
to other Mediterranean areas with available soil, land use and climate data.Peer reviewe