As a critical ecological barrier in China,the Qinling Mountains see their ecological functions significantly impaired by frequent shallow landslides.However,existing research on the distribution characteristics and dr...As a critical ecological barrier in China,the Qinling Mountains see their ecological functions significantly impaired by frequent shallow landslides.However,existing research on the distribution characteristics and driving mechanisms of such landslides remains relatively limited.To address this knowledge gap,the present study integrated data analysis,field investigations,and remote sensing interpretation to construct a landslide database for the core area of the Qinling Mountains,and systematically analyzed the spatial patterns,development characteristics,and environmental driving factors of shallow landslides.The results reveal that shallow landslides are predominantly small-to-medium in scale,concentrated in regions with an altitude of 800–1000 m and a slope gradient of approximately 30°,with a distinct tendency to develop on sunny(southfacing)slopes.The occurrence frequency of these landslides exhibits a significant positive correlation with the soil moisture content of the weathered layer and the degree of groundwater enrichment in the study area.Specifically,these landslides are mainly developed in bedrock fissure water zones and karst fissure water zones with favorable water-bearing capacity,indicating that rainfall and surface hydrological processes are the key triggering factors for shallow landslides.Notably,vegetation exerts a mediating role in the"vegetation-hydrology-landslide"system:shallow landslides occur most frequently in areas with artificial or shrub-grass vegetation,peaking at a moderate coverage of 50%–60%.This peak suggests that vegetation within this range is ineffective at regulating soil moisture,while the interaction between specific vegetation types and hydrological enrichment further exacerbates landslide risk.By prioritizing the weights of vegetation and hydrological factors,we enhanced the information quantity model,which significantly improved its performance and increased the AUC value to 0.83.These findings confirm the pivotal roles of vegetation and hydrological factors,thereby providing a robust scientific basis for targeted landslide prevention and control in this region.展开更多
Rising global change intensifies water scarcity in China’s vital Yellow River Basin grain region,which mounts the need for precise spatial water management.In this study,we investigated the irrigation water demand fo...Rising global change intensifies water scarcity in China’s vital Yellow River Basin grain region,which mounts the need for precise spatial water management.In this study,we investigated the irrigation water demand for seven major crops in cities at the prefecture level between 2000 and 2019.Using Logarithmic Mean Divisia Index(LMDI)decomposition and k-means clustering,we quantified how yield,area,water use efficiency,and cropping patterns affect water demand and identified five irrigation development clusters.Key water-saving areas were identified by tracking transitions among clusters,and NSGA-II was applied to optimize crop structure.The results revealed that the total irrigation demand in the Yellow River Basin averaged 50.09 billion m3/year,with wheat accounting for 54.7%.The increase in yield and area increased demand by 15.2 and 5.5 billion m3,respectively,which was partly offset by changes in water use efficiency and cropping pattern(−7.0 and−1.8 billion m^(3),respectively).Regions in the upper reaches,particularly within the Lanzhou-Toudaoguai section,were identified as critical for water conservation.Optimization of the cropping structure in key regions can reduce annual irrigation water demand by 280 million m3,which accounts for 4.9%of the total demand in these areas,with minimal impact on crop production.This study provides a spatially explicit basis for targeted water conservation strategies in water-scarce agricultural regions.展开更多
基金supported by the National Key R&D Program of China(No.2024YFF1306502)three Special Programs of the National Natural Science Foundation of China(Nos.42341101,42442045,42307220)the Basic Scientific Research Business Funds of Central Universities(Nos.300102263401,300102265501,300102264103)。
文摘As a critical ecological barrier in China,the Qinling Mountains see their ecological functions significantly impaired by frequent shallow landslides.However,existing research on the distribution characteristics and driving mechanisms of such landslides remains relatively limited.To address this knowledge gap,the present study integrated data analysis,field investigations,and remote sensing interpretation to construct a landslide database for the core area of the Qinling Mountains,and systematically analyzed the spatial patterns,development characteristics,and environmental driving factors of shallow landslides.The results reveal that shallow landslides are predominantly small-to-medium in scale,concentrated in regions with an altitude of 800–1000 m and a slope gradient of approximately 30°,with a distinct tendency to develop on sunny(southfacing)slopes.The occurrence frequency of these landslides exhibits a significant positive correlation with the soil moisture content of the weathered layer and the degree of groundwater enrichment in the study area.Specifically,these landslides are mainly developed in bedrock fissure water zones and karst fissure water zones with favorable water-bearing capacity,indicating that rainfall and surface hydrological processes are the key triggering factors for shallow landslides.Notably,vegetation exerts a mediating role in the"vegetation-hydrology-landslide"system:shallow landslides occur most frequently in areas with artificial or shrub-grass vegetation,peaking at a moderate coverage of 50%–60%.This peak suggests that vegetation within this range is ineffective at regulating soil moisture,while the interaction between specific vegetation types and hydrological enrichment further exacerbates landslide risk.By prioritizing the weights of vegetation and hydrological factors,we enhanced the information quantity model,which significantly improved its performance and increased the AUC value to 0.83.These findings confirm the pivotal roles of vegetation and hydrological factors,thereby providing a robust scientific basis for targeted landslide prevention and control in this region.
基金National Natural Science Foundation of China,No.42041007。
文摘Rising global change intensifies water scarcity in China’s vital Yellow River Basin grain region,which mounts the need for precise spatial water management.In this study,we investigated the irrigation water demand for seven major crops in cities at the prefecture level between 2000 and 2019.Using Logarithmic Mean Divisia Index(LMDI)decomposition and k-means clustering,we quantified how yield,area,water use efficiency,and cropping patterns affect water demand and identified five irrigation development clusters.Key water-saving areas were identified by tracking transitions among clusters,and NSGA-II was applied to optimize crop structure.The results revealed that the total irrigation demand in the Yellow River Basin averaged 50.09 billion m3/year,with wheat accounting for 54.7%.The increase in yield and area increased demand by 15.2 and 5.5 billion m3,respectively,which was partly offset by changes in water use efficiency and cropping pattern(−7.0 and−1.8 billion m^(3),respectively).Regions in the upper reaches,particularly within the Lanzhou-Toudaoguai section,were identified as critical for water conservation.Optimization of the cropping structure in key regions can reduce annual irrigation water demand by 280 million m3,which accounts for 4.9%of the total demand in these areas,with minimal impact on crop production.This study provides a spatially explicit basis for targeted water conservation strategies in water-scarce agricultural regions.
