Extended Abstract
Introduction
Carbon is a crucial element in the global biogeochemical cycle, regulating ecological processes and influencing the Earth’s climate. It is stored in various ecosystem components, including vegetation, litter, and soil, with soil organic carbon forming the largest terrestrial reservoir. This pool not only mitigates atmospheric CO₂ but also supports soil fertility, biodiversity, and long-term ecosystem stability. Carbon distribution and sequestration are controlled by multiple environmental factors, among which topography—including elevation, slope, and aspect plays a key role. Topographic variations affect microclimatic conditions such as temperature, soil moisture, and solar radiation, thereby influencing vegetation growth, litter decomposition, and soil carbon dynamics. While many studies have focused on the effects of land use and vegetation type, the impact of topographic variables on the spatial heterogeneity of carbon remains underexplored. The Kojur watershed in Mazandaran Province, northern Iran, offers a representative case study due to its complex terrain, Hyrcanian forests, and diverse land uses, resulting in high carbon storage potential. This study aims to quantify the effects of elevation and slope on carbon sequestration in both soil and vegetation, and to identify spatial patterns of carbon storage. The findings provide insights for sustainable resource management and support strategies for climate change mitigation in topographically diverse landscapes.
Materials and Methods
This study was conducted in the Kojur watershed, Mazandaran Province, northern Iran, covering 432.85 km². The watershed exhibits diverse topography, with elevation ranging from 135 to 3,432 m above sea level and an average slope of 22.3°. A high-resolution digital elevation model (DEM, 12.5 m) was used to derive elevation, slope, and aspect. Processing and hydrological corrections were conducted in ArcGIS to improve accuracy and eliminate surface depressions. Carbon storage was assessed in four pools: aboveground biomass, belowground biomass, litter, and soil. Soil samples were systematically collected from multiple locations to a depth of 30 cm, representing the active layer of organic carbon. Laboratory analyses were carried out using standardized dry combustion methods. Vegetation biomass was harvested, dried, weighed, and analyzed to estimate carbon content. These datasets were integrated into the InVEST Carbon Storage and Sequestration model to generate spatially explicit estimates of carbon distribution. To explore the role of topography, statistical analyses were performed. Correlation analysis quantified the relationships between carbon storage and topographic factors, while geographically weighted regression (GWR) was applied to account for spatial heterogeneity. Unlike global regression models, GWR captures locally varying relationships, making it possible to identify areas where elevation and slope exert the greatest influence on carbon storage. This approach not only improved spatial prediction but also provided a basis for developing management strategies tailored to the ecological characteristics of the watershed.
Results and Discussion
Topographic analysis revealed distinct patterns in the spatial distribution of carbon storage within the watershed. Elevations between 1,750 and 2,150 m, along with moderate slopes of 5–12°, were associated with the highest carbon storage across all pools, including aboveground and belowground biomass, litter, and soil. These conditions likely provide favorable microclimatic factors, such as optimal temperature, sufficient soil moisture, and dense vegetation, enhancing carbon sequestration. Beyond these ranges, particularly at higher elevations and steeper slopes, carbon storage declined due to reduced vegetation, lower biological activity, and limited soil fertility. Correlation analysis indicated that slope had a stronger influence on carbon accumulation than elevation, showing a moderate positive relationship, whereas elevation effects were weak and inconsistent. This highlights that slope-mediated processes such as soil depth variability, water retention, and root distribution play a more critical role in carbon dynamics than altitude alone. Geographically weighted regression (GWR) demonstrated spatial heterogeneity in topographic influence on carbon sequestration. Standardized residuals were mostly within acceptable ranges, indicating good model performance, although some localized areas showed higher residuals, reflecting unmeasured environmental or anthropogenic factors such as land-use change, grazing, or microclimatic variations. These findings underscore the importance of considering local topography in carbon assessments and suggest that management strategies should address fine-scale differences to optimize carbon storage and strengthen ecosystem resilience.
Conclusion
The findings of this study highlight the central role of topographic factors in shaping spatial patterns of carbon storage within the Kojur watershed. Among the examined variables, slope exerted a stronger and more consistent influence on carbon sequestration than elevation. Maximum carbon accumulation occurred at moderate elevations and slopes, where favorable conditions such as adequate soil depth, water availability, and vegetation density enhanced carbon storage. These results suggest that slope-driven processes should be prioritized in spatial assessments of carbon dynamics. The application of geographically weighted regression (GWR) proved effective in capturing local variations in topographic influence. By addressing spatial heterogeneity, GWR identified both areas of high reliability and zones with elevated residuals, pointing to potential impacts of unmeasured factors, including land use, microclimatic variability, or anthropogenic disturbance. Such findings emphasize the importance of incorporating spatially explicit modeling techniques into carbon research and management. From a practical perspective, combining detailed topographic data with advanced spatial models provides a strong framework for predicting carbon distribution. This approach supports the identification of high-potential sequestration areas, thereby informing conservation planning and sustainable resource management. Moreover, recognition of areas with high residuals highlights opportunities for targeted research and model refinement, ultimately improving the reliability of carbon management strategies under changing climate conditions.
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