基于检索1950-2016年发表在中国知网、Google Scholar、Web of Science等数据库的文章,筛选出关于我国退耕还草工程与土壤有机碳的文献123篇(126个研究地点)。基于筛选文献,提取了农田和恢复草地的土壤有机碳含量、土壤有效养分含量、...基于检索1950-2016年发表在中国知网、Google Scholar、Web of Science等数据库的文章,筛选出关于我国退耕还草工程与土壤有机碳的文献123篇(126个研究地点)。基于筛选文献,提取了农田和恢复草地的土壤有机碳含量、土壤有效养分含量、恢复时间和气候等数据735条,旨在探究我国退耕还草工程对土壤有机碳含量的影响及其主导因素。结果表明:1)农田恢复为草地后,土壤有机碳含量呈先下降后上升趋势(时间拐点约为第6年),整体上退耕还草工程使土壤有机碳含量提高了19.8%。2)土壤有机碳的正响应随土壤深度增加而减弱,且深度超过1 m时其正响应不明显。3)土壤有效氮是影响土壤有机碳恢复的主要因素,而土壤有效磷和植物功能群对其影响不大。4)沿着增加的水分梯度,土壤有机碳的恢复效果由负变正,转变阈值为25.15 (干旱指数)。总体而言,我国退耕还草工程对土壤有机碳含量的影响为正效应,此效应受环境梯度和恢复时间的共同影响。本研究能够为我国土地利用变化背景下土壤碳库管理的相关决策提供科学依据。展开更多
Understanding the trajectories and driving mechanisms behind land use/land cover(LULC)changes is essential for effective watershed planning and management.This study quantified the net change,exchange,total change,and...Understanding the trajectories and driving mechanisms behind land use/land cover(LULC)changes is essential for effective watershed planning and management.This study quantified the net change,exchange,total change,and transfer rate of LULC in the Jinghe River Basin(JRB),China using LULC data from 2000 to 2020.Through trajectory analysis,knowledge maps,chord diagrams,and standard deviation ellipse method,we examined the spatiotemporal characteristics of LULC changes.We further established an index system encompassing natural factors(digital elevation model(DEM),slope,aspect,and curvature),socio-economic factors(gross domestic product(GDP)and population),and accessibility factors(distance from railways,distance from highways,distance from water,and distance from residents)to investigate the driving mechanisms of LULC changes using factor detector and interaction detector in the geographical detector(Geodetector).The key findings indicate that from 2000 to 2020,the JRB experienced significant LULC changes,particularly for farmland,forest,and grassland.During the study period,LULC change trajectories were categorized into stable,early-stage,late-stage,repeated,and continuous change types.Besides the stable change type,the late-stage change type predominated the LULC change trajectories,comprising 83.31% of the total change area.The period 2010-2020 witnessed more active LULC changes compared to the period 2000-2010.The LULC changes exhibited a discrete spatial expansion trend during 2000-2020,predominantly extending from southeast to northwest of the JRB.Influential driving factors on LULC changes included slope,GDP,and distance from highways.The interaction detection results imply either bilinear or nonlinear enhancement for any two driving factors impacting the LULC changes from 2000 to 2020.This comprehensive understanding of the spatiotemporal characteristics and driving mechanisms of LULC changes offers valuable insights for the planning and sustainable management of LULC in the JRB.展开更多
基金partly funded by the National Key Research and Development Program of China(NK2023190801)the National Foreign Experts Program of China(G2023041024L)the Key Scientific Research Program of Shaanxi Provincial Education Department,China(21JT028)。
文摘Understanding the trajectories and driving mechanisms behind land use/land cover(LULC)changes is essential for effective watershed planning and management.This study quantified the net change,exchange,total change,and transfer rate of LULC in the Jinghe River Basin(JRB),China using LULC data from 2000 to 2020.Through trajectory analysis,knowledge maps,chord diagrams,and standard deviation ellipse method,we examined the spatiotemporal characteristics of LULC changes.We further established an index system encompassing natural factors(digital elevation model(DEM),slope,aspect,and curvature),socio-economic factors(gross domestic product(GDP)and population),and accessibility factors(distance from railways,distance from highways,distance from water,and distance from residents)to investigate the driving mechanisms of LULC changes using factor detector and interaction detector in the geographical detector(Geodetector).The key findings indicate that from 2000 to 2020,the JRB experienced significant LULC changes,particularly for farmland,forest,and grassland.During the study period,LULC change trajectories were categorized into stable,early-stage,late-stage,repeated,and continuous change types.Besides the stable change type,the late-stage change type predominated the LULC change trajectories,comprising 83.31% of the total change area.The period 2010-2020 witnessed more active LULC changes compared to the period 2000-2010.The LULC changes exhibited a discrete spatial expansion trend during 2000-2020,predominantly extending from southeast to northwest of the JRB.Influential driving factors on LULC changes included slope,GDP,and distance from highways.The interaction detection results imply either bilinear or nonlinear enhancement for any two driving factors impacting the LULC changes from 2000 to 2020.This comprehensive understanding of the spatiotemporal characteristics and driving mechanisms of LULC changes offers valuable insights for the planning and sustainable management of LULC in the JRB.
