基于FLUS模型的天山山区未来土地利用变化预估

doi:10.13866/j.azr.2019.05.25

Abstract

Abstract: In this paper,the 31 global climate models were integrated from the Phase 5 of Coupled Model Intercomparison Project (CMIP5) to study the overall prediction of land use change in the Tianshan Mountains under three emission scenarios,i.e.,the Representative Concentration Pathways (RCPs) 2.6,4.5 and 8.5.In the study,the observed climatic data from 53 meteorological stations,socioeconomic data and natural geomorphological conditions in the Tianshan Mountains were used.The results showed that: ① FLUS model could be used to simulate the historical land use change in the study area with high accuracy; ② Compared with the base period from 1970 to 1999,the average annual temperature would be increased by 1.67-2.16 ℃ during 2021-2050,and its increase would be low in spring and summer but high in autumn and winter.It was revealed that the average annual precipitation would be increased by 22-25 mm under RCPs 2.6,4.5 and 8.5.Under the three scenarios,precipitation reduction would occur in some years compared to the base period,but there would be holistically an increase trend; ③ Compared with the situation of land use change in 2017,it was estimated that,under the RCP2.6,RCP4.5 and RCP8.5 scenarios,the glacier and snow areas in the Tianshan Mountainous in 2050 would be reduced by 29.2%,34.6% and 38.4% respectively.The enlargement of urban land area in 2050 would not be significant under RCP2.6 and RCP4.5.Some of the cultivated land would be converted into urban land and grassland,and the area of woodland would be slightly enlarged.The urban land area under the RCP8.5 scenario would be nearly three times of that in 2017.Part of woodland would be converted into grassland.The total area of waters would be slightly enlarged,and the area of unused land will be in a reduction trend.

Key words: climate change, scenario simulation, land use change, FLUS model, Tianshan Mountainous

QIN Qi-rui, LI Xue-mei, CHEN Qing-wei, TIAN Ya-lin. Estimation of Future Land Use Change in the Tianshan Mountainous Based on FLUS Model[J].Arid Zone Research, 2019, 36(5): 1270-1279.

References

[1] 姜群鸥,邓祥征,柯新利,等.RCPs气候情景下珠江三角洲地区城市用地扩展的预测与模拟[J].应用生态学报,2014,25(12):3627-3636.
[Jiang Qunou,Deng Xiangzheng,Ke Xinli,et al.Prediction and simulation of urban area expansion in Pearl River Delta Region under the RCPs climate scenarios[J].Chinese Journal of Applied Ecology,2014,25(12):3627-3636.]
[2] Li X,Chen G Z,Liu X P,et al.A new global land-use and land-cover change product at a 1 km resolution for 2010 to 2100 based on human-environment interactions[J].Annals of the Association of American Geographers,2017,107(5):1040-1059.
[3] 刘甲红,徐露洁,潘骁骏,等.土地利用/土地覆盖变化情景模拟研究进展[J].杭州师范大学学报(自然科学版),2017,16(5):551-560.
[Liu Jiahong,Xu Lujie,Pan Xiaojun,et al.Research progress on land-use and land-cover change scenario simulation[J].Journal of Hangzhou Normal University(Natural Science Edition),2017,16(5):551-560.]
[4] IPCC.Climate Change 2013:The Physical Science Basis[M].Cambridge:Cambridge University Press,2013:1535.
[5] 董思言,高学杰.长期气候变化:IPCC第五次评估报告解读[J].气候变化研究进展,2014,10(1):56-59.
[Dong Siyan,Gao Xuejie.Long-term climate change:Interpretation of IPCC Fifth Assessment Report[J].Climate Change Research,2014,10(1):56-59.]
[6] 沈永平,王国亚.IPCC第一工作组第五次评估报告对全球气候变化认知的最新科学要点[J].冰川冻土,2013,30(5):1068-1076.
[Shen Yongping,Wang Guoya.Key findings and assessment results of IPCC WGI Eifth Assessment Report[J].Journal of Glaciology and Geocryology,2013,35(5):1068-1076.]
[7] 徐敏,于庚康,徐经纬,等.不同分辨率下全球气候模式的集合结果检验[J].气象科学,2013,33(2):160-167.
[Xu Min,Yu Gengkang,Xu Jingwei,et al.Assessment of global climate models based on different resolution sets of simulation results[J].Journal of the Meteorological Sciences,2013,33(2):160-167.]
[8] 李少英,刘小平,黎夏,等.土地利用变化模拟模型及应用研究进展[J].遥感学报,2017,21(3):329-340.
[Li Shaoying,Liu Xiaoping,Li Xia,et al.Simulation model of land use dynamics and application:Progress and prospects[J].Journal of Remote Sensing,21(3):329-340.]
[9] Newman G,Hollander J,Lee J,et al.A neighborhood-scaled right sizing strategy based on land use dynamics[J].Journal of Geovisualization and Spatial Studies,2018,2(2):11.
[10] Liu X P,Liang X,Li X,et al.A future land use simulation model (FLUS) for simulating multiple land use scenarios by coupling human and natural effects[J].Landscape and Urban Planning,2017,168:94-116.
[11] 周浩,雷国平,杨雪昕,等.RCPs气候情景下三江平原典型流域耕地动态模拟[J].农业机械学报,2017,48(10):121-132.
[Zhou Hao,Lei Guoping,Yang Xuexin,et al.Simulation of cultivated land under RCPs scenarios in typical basin of Sanjiang Plain[J].Transactions of The Chinese Society of Agricultural,2017,48(10):121-132.]
[12] 张子明,刘平辉,朱寿红.基于FLUS模型的城镇用地增长边界划定研究——以临川区为例[J].江西农业学报,2018,30(5):117-123.
[Zhang Ziming,Liu Pinghui,Zhu Shouhong.Delimitation of urban land growth boundary based on FLUS model:A case study of Linchuan district[J].Acta Agriculturae Jiangxi,2018,30(5):117-123.]
[13] Li X M,Li L H.Temporal and spatial variation of 10-day mean air temperature in Northwestern China[J].Theoretical and Applied Climatology,2015,119(1-2):285-298.
[14] Chen Y N,Li W H,Deng H J,et al.Changes in Central Asia’s water tower:Past,present and future [J].Scientific Report,2016,6:35458.
[15] 李雪梅,高培,李倩,等.中国天山积雪对气候变化响应的多通径分析[J].气候变化研究进展,2016,12(4):303-312.
[Li Xuemei,Gao Pei,Li Qian,et al.Muti-paths impact from climate change on snow cover in Tianshan Mountainous area of China[J].Climate Change Research,2016,12(4):303-312.]
[16] 赵玲.天山山区气候变化及其对草地植被的影响[D].乌鲁木齐:新疆农业大学,2012.
[Zhao Ling.Climate Change and Its Impact on Rangeland Vegetation in Tianshan Mountains[D].Urumqi:Xinjiang Agricultural University,2012.]
[17] 胡汝骥.中国天山自然地理[M].北京:中国环境科学出版社,2004:154-155.
[Hu Ruji.Physical Geography of the Tianshan Mountains in China[M].Beijing:China Environmental Science Press,2004:154-155.]
[18] 成爱芳,冯起,张建恺,等.未来气候情景下气候变化响应过程研究综述[J].地理科学,2015,35(1):84-90.
[Cheng Aifang,Feng Qi,Zhang Jiankai,et al.A review of climate change scenario for impacts process study[J].Scientia Geographica Sinica,2015,35(1):84-90.]
[19] 黎敏,吕海深,欧阳芬.基于Delta方法的淮河流域气候变化预测分析[J].人民长江,2012,43(7):11-14.
[Li Min,Lü Haishen,Ouyang Fen.Analysis and prediction of climate change in Huaihe River Basin based on Delta method[J].Yangtze River,2012,43(7):11-14.]
[20] 李雪梅,李兰海,白磊,等.基于中亚气候数据的开都河流域历史气候资料重建[J].自然资源学报,2012,27(11):1918-1930.
[Li Xuemei,Li Lanhai,Bai Lei,et al.Reconstruction of climatic data in the Kaidu River basin based on climatic data of Central Asia[J].Journal of Natural Resources,2012,27(11):1918-1930.]

Estimation of Future Land Use Change in the Tianshan Mountainous Based on FLUS Model

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Metrics

Comments

Recommended 0

[1]	SHANG Shujing, LIU Danhui, ZHOU Yixin, WU Jiaju, LU Ting, LI Wenjun. Prediction of the suitable distribution areas of Arnebia euchroma (Boraginaceae) in China under change climate conditions [J]. Arid Zone Research, 2025, 42(9): 1628-1639.
[2]	ZHANG Kun, WU Xinping, LIU Yongqiang, ZHANG Lifang, QIN Yan, YANG Yulu, GAN Hui. Spatiotemporal evolution and prediction of carbon storage in Xinjiang using the PLUS-InVEST model [J]. Arid Zone Research, 2025, 42(9): 1715-1725.
[3]	YAN Yingcun, SUN Shujiao, YU Di, GAO Guisheng. Impact and trend estimation of climate change on vegetation greenness in the Qaidam Basin [J]. Arid Zone Research, 2025, 42(7): 1257-1268.
[4]	LIU Xiaoming, ZHENG Shiyan, QIAO Zhanming. Dynamic simulation of land use change and habitat quality in the Three River Source Region based on the PLUS-InVEST models [J]. Arid Zone Research, 2025, 42(6): 1080-1092.
[5]	ZHANG Yan, YANG Weixin, LYU Tao. Driving factors analysis and multi-scenario simulation of land use change based on GeoDetector-Mixed-cell Cellular Automata: A case of the Gansu section in the Yellow River Basin [J]. Arid Zone Research, 2025, 42(4): 668-681.
[6]	WANG Liyuan, ZHANG Yong. Evolution of spatiotemporal patterns and multiscenario simulation of land use transition in Gansu Province [J]. Arid Zone Research, 2025, 42(4): 695-707.
[7]	ZOU Bin, ZOU Shan, YANG Yuhui. Dynamic changes and driving factors of surface water body in Xinjiang from 1990 to 2023 [J]. Arid Zone Research, 2025, 42(1): 40-50.
[8]	LYU Zhuangzhuang, QIAO Qingqing, DONG Sunyi, WANG Dong. Paleoclimatic evolution and driving mechanisms in arid areas of inland Asia during the Middle Miocene Climatic Optimum in the context of global climate warming [J]. Arid Zone Research, 2024, 41(8): 1309-1322.
[9]	ZHOU Jie, WANG Xuhu, DU Weibo, ZHOU Xiaolei, YANG Jie, ZAHNG Xiaowei. Prediction of potential distribution area of Picea schrenkiana under the background of climate change [J]. Arid Zone Research, 2024, 41(7): 1167-1176.
[10]	LI Bingjie, FAN Zhitao, QU Zhicheng, YAO Shunyu, SU Xiashu, LIU Dongwei, WANG Lixin. Evaluation and prediction of ecosystem carbon storage in the Inner Mongolia section of the Yellow River Basin based on the InVEST-PLUS model [J]. Arid Zone Research, 2024, 41(7): 1217-1227.
[11]	ZHANG Shunxin, WU Zihao, YAN Qingwu, LI Gui’e, MU Shouguo. Spatiotemporal changes in the ecosystem carbon storage on the northern slope of the Tianshan Mountains and simulations based on the PLUS-InVEST model [J]. Arid Zone Research, 2024, 41(7): 1228-1237.
[12]	LIANG Shuanghe, NIU Zuirong, JIA Ling. Analysis of runoff changes and attribution in the main stream of Zuli River in the past 65 years [J]. Arid Zone Research, 2024, 41(6): 928-939.
[13]	YAO Xiaochen, GAO Fan, HAN Fanghong, HE Bing. Land use intensity change and its influence on evapotranspiration in Aksu River Basin from 2000 to 2020 [J]. Arid Zone Research, 2024, 41(6): 951-963.
[14]	TANG Kexin, GUO Jianbin, HE Liang, CHEN Lin, WAN Long. Characteristics of the spatial and temporal evolution of Gross Primary Productivity and its influencing factors in China’s drylands [J]. Arid Zone Research, 2024, 41(6): 964-973.
[15]	LI Jiake, SHAO Zhanlin. Spatiotemporal evolution and prediction of carbon stock in Urumqi City based on PLUS and InVEST models [J]. Arid Zone Research, 2024, 41(3): 499-508.