Modeling the distribution of soil organic carbon in two hyper-arid inland lakes of the Wadi El-Rayan Protected Area, Egypt

This study examines the patterns and storage of soil organic carbon (SOC) in the Upper and Lower Lakes of Egypt’s Wadi El-Rayan Protected Area, a hyper-arid inland lake system. The objective was to develop predictive models for estimating volumetric SOC density (SOCv, kg C/m³) and cumulative SOC stocks (SOCc, kg C/m²) at varying soil depths across both lakes. Using data from 40 soil cores comprising 400 individual samples, three mathematical models (allometric, exponential, and sigmoid) were applied to analyze SOC. Significant differences in soil bulk density (SBD), SOC content, SOCv, and SOCc were observed between the two lakes (p < 0.05–0.001). The Lower Lake exhibited the highest mean SBD (1.01 ± 0.03 g/cm³) compared with the Upper Lake (0.83 ± 0.03 g/cm³), whereas the Upper Lake showed the highest mean SOC content (29.4 ± 2.0 g C/kg), SOCv (20.3 ± 1.5 kg C/m³), and SOCc (10.2 ± 2.0 kg C/m²), with the corresponding lowest values recorded in the Lower Lake (7.0 ± 0.6 g C/kg, 6.4 ± 0.6 kg C/m³, and 3.2 ± 0.5 kg C/m², respectively). A negative correlation between SOC content and SBD was observed in both lakes (r = − 0.344 in the Upper Lake and r = − 0.188 in the Lower Lake). For SOCv predictions in the Lower Lake, the allometric and exponential models provided the best fit, with R² values of 0.989 and 0.980, respectively. In contrast, the exponential, allometric, and sigmoid models performed well for the Upper Lake, with R² values of 0.972, 0.963, and 0.958, respectively. All three modeling approaches achieved high accuracy (R² > 0.99) in simulating SOCc. The allometric and exponential models provided the best fit for the Lower Lake (R² = 0.999), while the exponential and sigmoid models yielded the best fit for the Upper Lake (R² = 0.999). These findings enhance our understanding of SOC distribution in the hyper-arid inland lakes of the Wadi El-Rayan Protected Area and provide a foundation for more accurate predictions of future carbon stocks, thereby supporting conservation, restoration, and preservation efforts.