Document Type : Research Article
Authors
Department of Geography, Faculty of Humanities, University of Mazandaran, Babolsar, Iran
Abstract
Landslides are significant geomorphic hazards that contribute to both terrain instability and increased erosion and sediment production. In Iran, most studies have focused on landslide hazard zonation, with relatively little attention given to their role in sediment dynamics. This study aims to assess the relationship between landslide susceptibility and erosion intensity, as well as their effect on sediment yield in the Darabkola watershed, Mazandaran Province. A landslide susceptibility map was produced using the Frequency Ratio (FR) model, by integrating factors such as lithology, elevation, slope, aspect, land use, and distance from roads and streams. The model showed high predictive accuracy (p = 0.99). Erosion intensity was estimated using the MPSIAC and RUSLE models, with results validated by the BLM model and statistical indicators. The MPSIAC model, due to its higher accuracy, was used to quantify sediment yield across landslide susceptibility classes. ANOVA results revealed significant differences in sediment yield among classes, with the highest sediment production (61.35 tons/ha/year) observed in areas of very high landslide susceptibility. These findings highlight the importance of integrating landslide impacts into erosion and sediment yield models for improved prediction and effective watershed management.
Extended Abstract
Introduction
Two of the eight main soil degradation processes worldwide are soil erosion and landslides. Landslides, as environmental hazards, have significant negative impacts on ecosystems. In Iran, studies on landslides have primarily focused on hazard zonation, with limited attention given to their role in exacerbating erosion and increasing sediment yield in basins. Furthermore, most methods and models developed for assessing erosion and sediment yield do not account for the role of landslides. Additionally, studies evaluating the impact of landslides on sediment yield have often relied on data from hydrometric stations. However, not all material displaced by landslides is transported into the drainage network; some of it remains on slopes. Moreover, sediment measurement data is often unavailable for many basins.
The study area, the Darabkola watershed in Mazandaran Province, is highly susceptible to landslides due to its weak and tectonically active formations, as well as significant land-use changes. Therefore, landslide hazard zonation and its relationship with sediment yield are essential for better watershed management. The aim of this study is to evaluate the relationship between landslides and erosion intensity, as well as their impact on sediment yield in the Darabkola watershed.
Material and Methods
In this study, landslides in the Darabkola watershed were first investigated, and their frequency was determined by integrating factor maps including lithology, elevation, slope, aspect, land use, distance from roads, and distance from streams. For landslide zonation and determining the weight of influencing factors, the Area Density Model was used, and its evaluation was performed using the Empirical Probability (P) equation.
To determine the sediment yield from landslides, erosion class maps of the watershed were prepared using the MPSIAC and RUSLE models. The accuracy of these models was evaluated using the BLM model, which is based on field observations, as a reference map. A point layer with a regular grid was created for sampling at 1,204 points from the maps generated by these models. Based on these sampling points, statistical indices such as RMSE, MAE, MSE, and NSEC were calculated.
Following model evaluation, the landslide zonation map was overlaid with the sediment yield map, and the relevant data were extracted for analysis. The collected data were then analyzed using one-way ANOVA.
Results and Discussion
The results of overlaying the landslide distribution map with the factor maps indicate that conglomerate, marl, sandstone, and siltstone (Plcm), with a landslide area density of 0.634; slopes of 15–25% (0.127); southwest aspect (0.117); elevation of 150–300 meters (0.228); distance of 150–300 meters from streams (0.165); distance of 150–300 meters from roads (0.273); and rainfed agricultural land use (0.304) are associated with the highest landslide susceptibility.
After calculating the weight of each factor class influencing landslides, the weighted maps were combined, and a landslide susceptibility map was prepared using the Area Density Model. Its accuracy evaluation using the Empirical Probability equation confirmed the high accuracy of the zonation map (p = 0.99).
To determine the sediment yield related to landslides, erosion class maps were created using the MPSIAC and RUSLE models. The accuracy of these maps was assessed using the BLM model and the statistical indices mentioned. The results showed that the MPSIAC model had superior performance. Consequently, the sediment yield map derived from the MPSIAC model was overlaid with the landslide susceptibility map to estimate sediment yield for each susceptibility class.
The highest sediment yield in the Darabkola watershed was observed in the very high and high landslide susceptibility classes, with values of 61.35 and 53.42 tons/ha/year, respectively. To examine the significance of variations in sediment yield across different landslide susceptibility classes, one-way ANOVA was used. The results indicated a statistically significant relationship between sediment yield and landslide susceptibility, confirming that erosion and sediment yield vary substantially across the landslide susceptibility classes.
Conclusion
In this study, landslide distribution in the Darabkola watershed was evaluated by integrating maps of lithology, elevation, slope, aspect, land use, and distances from roads and streams with existing landslide data. The Area Density Model was used to calculate the weighted values of each factor class, and the evaluation confirmed the high accuracy of the resulting landslide susceptibility map.
Subsequently, the erosion intensity map was produced using the MPSIAC and RUSLE models, and validated against the BLM model. Due to its higher accuracy, the MPSIAC model was used to assess sediment yield across landslide susceptibility classes. The ANOVA results confirmed that sediment yield increases significantly with landslide susceptibility, and areas with very high susceptibility exhibited the greatest sediment production.
The findings underscore the importance of integrating landslide impacts into erosion and sediment yield models. The significant contribution of high-susceptibility areas to sediment yield highlights the urgent need for targeted conservation strategies in the study area. It is also recommended that future sediment and erosion modeling efforts explicitly include landslide dynamics to improve prediction and watershed management outcomes.
Keywords
)
Send comment about this article