Blue Carbon Storage Variability in Hyper-arid Evaporitic Environment: A Comparative Study of Inland and Coastal Sabkhas Ecosystem

مقال فى مجلة

2025

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Abstract

Background: Sabkhas, or salt marshes, are important wetland habitats with a crucial function in capturing and storing carbon. This research examines the capacity of six salt marshes to sequester carbon, with a focus on comparing inland and coastal salt marshes. Six Sabkhas sites were sampled for soil and plants, three of which were inland (Aushazia, Ghuwaymid, Al Qasab) and three of which were coastal (Asfar Lake, Al Uqayr, Al Dnan). Four stands were selected per each Sabkha site and three plots of soil cores were collected at each sampling stand. The depth of the corer's penetration was 25 cm. After extraction, each core was divided into five layers spaced five centimeters apart, spanning the 0–25 cm depth range, i.e., 6 sites × 4 stands × 3 soil plots × 5 sections = 360 soil samples. Across these sites, measurements were made of the soil bulk density: SBD, soil organic carbon: SOC concentration, SOC stock, and plant organic carbon POC stock.

Results: The results revealed significant spatial variability in SBD and SOC across the marshes (P ≤ 0.001). Inland Aushazia presented the highest SOC concentration (43.62 ± 2.09 g C /kg) and SOC stock (0.97 ± 0.06 t C /ha), as well as the highest POC stock (8.53 ± 0.97 t C /ha), indicating superior carbon sequestration capacity. Conversely, coastal Al Dnan presented the lowest SOC concentration (5.15 ± 1.34 g C /kg) and SOC stock (0.15 ± 0.05 t C /ha). Between 0.6 and 1.5 g/cm3 was the range of the (SBD), with inland-Al Qasab and coastal-Al Dnan exhibiting the highest values (1.22 ± 0.02 g/cm3  and 1.24 ± 0.28 g/cm3, respectively). Application of the principal component analysis (PCA) biplot on the data reveals distinct spatial separation between coastal and inland localities on the basis of environmental and/or biological variables. Moreover, in the most studied sites, the association between SBD and the SOC concentration was explored through nonlinear regression analysis.