Search Results (4)

The northern foothills of the Qinling Mountains, a critical ecological barrier and urban–rural transition zone in China, face intensifying rainstorm–flood disasters under climate extremes and rapid urbanization. This study pioneers a remote sensing-driven, dynamically coupled framework by integrating multi-source satellite data, system resilience theory, and spatial modeling to develop a novel “risk identification–resilience assessment–scenario simulation” chain. This framework quantitatively evaluates the nonlinear response mechanisms of town–village systems to flood disasters, emphasizing the synergistic effects of spatial scale, morphology, and functional organization. The proposed framework uniquely integrates three innovative modules: (1) a hybrid risk identification engine combining normalized difference vegetation index (NDVI) temporal anomaly detection and spatiotemporal hotspot analysis; (2) a morpho-functional resilience quantification model featuring a newly developed spatial morphological resilience index (SMRI) that synergizes landscape compactness, land-use diversity, and ecological connectivity through the entropy-weighted analytic hierarchy process (AHP); and (3) a dynamic scenario simulator embedding rainfall projections into a coupled hydrodynamic model. Key advancements over existing methods include the multi-temporal SMRI and the introduction of a nonlinear threshold response function to quantify “safe-fail” adaptation capacities. Scenario simulations reveal a reduction in flood losses under ecological priority strategies, outperforming conventional engineering-based solutions by resilience gain. The proposed zoning strategy prioritizing ecological restoration, infrastructure hardening, and community-based resilience units provides a scalable framework for disaster-adaptive spatial planning, underpinned by remote sensing-driven dynamic risk mapping. This work advances the application of satellite-aided geospatial analytics in balancing ecological security and socioeconomic resilience across complex terrains. Full article

The Qinling Mountains serve as the main water source for the Weihe River and Hanjiang River. However, the lack of sufficient observational data limits a deeper understanding and the utilization of its water resources. In this study, the Variable Infiltration Capacity (VIC) hydrological model is used to quantitatively analyze runoff changes and their impacts on these rivers, based on meteorological, land use, and elevation data. By using the hydrological parameter transplantation method, a parameterized system was established to simulate runoff variations from 1970 to 2020. Results showed that the total runoff of the Qinling Mountains in Shaanxi Province ranged between 13.26 and 44.47 billion m³/year, with an average perennial runoff of 25.05 billion m³/year. Over the past 51 years, the runoff volume has exhibited a slightly decreasing trend. The average runoff at the northern foothills is 3.56 billion m³/year, which accounts for 62.4% of the natural average runoff of the Weihe River (Huaxian Station). In contrast, the average runoff at the southern foothills is 21.49 billion m³/year, which accounts for 68.1% of the natural average runoff of the Hanjiang River (Huangjiagang Station). The significant variation in water vapor transport from the western equatorial Pacific to the region via the South China Sea has been identified as the primary reason for the changes in runoff. This quantitative study of runoff changes in the Qinling Mountains clarifies their influence on the Weihe River and the Hanjiang River and will provide a basis for the rational usage of ecological water. Full article

The Qinling region in central China, known as the ‘Dragon Vein of China’, is a vital ecological barrier facing significant soil erosion challenges. This study aims to enhance soil erosion management and analyse the spatiotemporal changes of soil erosion in the northern foothills of the Qinling Mountains. We collected data on precipitation, terrain, land use types, and soil in the designated region. Using GIS technology and the Revised Universal Soil Loss Equation (RUSLE) model, we created a detailed soil erosion map and analysed its evolution from 2018 to 2022. Results show a significant reduction in soil erosion in 2020–2021 despite a general upward trend in other years. Innovation includes integrating remote sensing with RUSLE for high-precision mapping and introducing a hierarchical approach for erosion risk assessment. The study found erosion peaks in summer and autumn, with higher levels in the southern parts compared to the northern parts. Influential factors include climate variables, human activities, soil, and vegetation types. The average soil erosion modulus in 2023 is 233.515 t/(km²·a), with total soil erosion of 85,233.046 t/a, mainly concentrated in the valley and mountain basin areas. This research provides a theoretical basis for improving the natural environment and implementing comprehensive soil and water conservation measures in the Qinling region, offering a model for similar ecological regions globally. Full article

Landslides cause significant disturbances to mountainous ecosystems and human activities. Due to climate change, the frequency of landslides as secondary disasters has notably increased compared to the past. Further exploration is needed to understand the effects of different restoration methods on post-landslide plant communities and soil properties over different periods of time. In this regard, we selected Lantian County in the northern foothills of the Qinling Mountains as our study area. We conducted surveys on artificially restored and naturally recovered plots at 1, 6, and 11 years after landslide events. Undamaged areas were chosen nearby as control plots. We identified vegetation types and species diversity after artificial and natural recovery and further analyzed the impact of different restoration strategies on vegetation patterns and soil properties. The research results indicate that, compared with natural recovery, artificial restoration can more quickly improve vegetation and soil. With the increasing time gradient, the average ground cover of the herbaceous layer in natural recovery decreased gradually from 47% at year one to 34% at year eleven. In contrast, in artificial restoration, the average ground cover of the herbaceous layer increased from 27% at year one to 44% at year eleven. For the shrub layer, in natural recovery, the average ground cover gradually increased to 39% over eleven years. While in artificial restoration, the average ground cover for the shrub layer gradually increased to 46% over the same period. In the artificial restoration plots, soil pH gradually increased (from 6.2 to 8.2), while TN content gradually decreased (from 1.7 g/kg to 0.9 g/kg). Similarly, TK content decreased (from 22.4 g/kg to 14.5 g/kg), and AP content showed a decreasing trend (from 20.7 mg/kg to 11.4 mg/kg). In the natural recovery plots, DNA content gradually increased (from 3.2 μg/g/d to 142.6 μg/g/d), and SC content gradually increased as well (from 2.4 mg/d/g to 23.1 mg/d/g). In contrast, on sites undergoing natural recovery, the short-term restoration rates of vegetation and soil are lower, but they show greater stability over a longer time. This study provides a new perspective on vegetation restoration strategies and is expected to offer insights for the optimization of post-landslide recovery in the future. Full article