摘要
为深入探讨渭河流域土地利用演变过程对蓝绿水时空变化的影响,该研究基于渭河流域1995、2015年土地利用数据,利用CA-Markov(Cellular Automata-Markov)模型预测渭河流域2035年的土地利用格局;结合SWAT(Soil and Water Assessment Tool)水文模型,设置多种土地利用变化情景,定量分析流域内土地利用变化与蓝绿水量的时空响应关系。结果表明:1)CA-Markov模型模拟2015年土地利用格局效果较好,Kappa系数为0.89,可用于模拟预测;1995-2035年间,耕地主要向建筑用地和草地转移,草地主要向林地和耕地转出,流域内耕地呈减少趋势,单一动态度为-0.27%,建筑用地增速最大,单一动态度达到3.75%。2)1995-2015年间,蓝水量增加了2.38 mm/a,绿水量减少了18.74 mm/a,绿水量减少幅度较大,2015-2035年蓝水量增加了14.82 mm/a,绿水量减少了15.23 mm/a,蓝水量的增加幅度较大;在极端土地利用情景下,退耕还草、还林对蓝水量和绿水量均起减少作用,蓝水量分别减少了9.27、11.37 mm/a,绿水量分别减少了32.94、21.13 mm/a。因此需要合理规划渭河流域耕地、林地和草地,防治水土流失问题,促进流域生态环境的改善。研究结果将对渭河流域土地利用规划和水资源管理提供科学参考。
Land use change is one of the key factors that directly dominate the hydrological process of the watershed.Human activities have changed land-use patterns,thereby indirectly posing an impact on surface infiltration,evapotranspiration,and soil water in the water cycle process.The water resources were also evolved for the water balance in the basin.This study aims to further explore the impact of land-use evolution on the spatiotemporal changes of blue and green water in the Weihe River Basin.A Cellular Automata-Markov(CA-Markov)model was selected to predict the land-use pattern in 2035 using land-use data in 1995 and 2015.Firstly,a standardized preprocessing was performed on the constraint data using a FUZZY module,including elevation,slope,highway,and railway.Secondly,the Markov module was superimposed onto the land use data to obtain the land transfer probability matrix from 1995 to 2015.Thirdly,the multi-criteria evaluation(MCE)module was used to make land-use maps suitability.The CA-Markov model was used to predict land-use structure in 2035.A variety of land-use change scenarios were set to quantitatively analyze the temporal and spatial responses of land-use change and blue-green water using the soil and water assessment tool(SWAT).The results showed that:1)The CA-Markov model was effective in simulating the land-use pattern in 2015,where the Kappa coefficient was 0.89,suitable for the prediction.2)During 1995-2035,the cropland was mainly transferred to building land and grassland,while the grassland was mainly transferred to woodland and cropland.Meanwhile,the cropland showed a decreasing trend,with the single attitude of-0.27%,and the building land had the largest growth rate,with the dynamic attitude of 3.75%.3)The model calibration showed that the evaluation indicators of five hydrological stations all met the standards of R2>0.6 and ENS>0.5,indicating that the SWAT model presented good applicability in each basin of the Weihe River Basin.4)The blue water increased by 2.38 mm/a,and green water decreased by 18.74 mm/a from 1995 to 2035.By contrast,the blue water increased by 14.82 mm/a,and the green water decreased by 15.23 mm/a from 2015 to 2035.As such,the evapotranspiration was reduced in the basin,but the runoff was increased.5)Returning cropland to woodlands and grasslands contributed to reducing the blue and green water,where the blue water volume decreased by 9.27,11.37 mm/a,while the green water volume decreased by 32.94,21.13 mm/a,respectively,under the extreme land-use scenarios.The main reason was that the cropland increased the runoff and evapotranspiration in the basin.The amount of blue and green water decreased,after a large area of cropland was converted into woodland and grassland.Therefore,it is necessary to reasonably plan the cropland,woodland,and grassland for the control of soil erosion,thereby promoting the improvement of the ecological environment in the study area.These findings can be expected to provide a sound scientific reference for land-use planning and water resources management in the Weihe River Basin.
作者
杨肖丽
李文婷
任立良
高甜
马慧君
Yang Xiaoli;Li Wenting;Ren Liliang;Gao Tian;Ma Huijun(State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering,Hohai University,Nanjing 210098,China;College of Hydrology and Water Resources,Hohai University,Nanjing 210098,China)
出处
《农业工程学报》
EI
CAS
CSCD
北大核心
2021年第11期268-276,共9页
Transactions of the Chinese Society of Agricultural Engineering
基金
国家重点研发计划项目(2016YFA0601504)
国家自然科学基金面上项目(52079036)。