防风固沙型重点生态功能区防风固沙服务的评估与受益区识别
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摘要
防风固沙型重点生态功能区是我国主要的防沙屏障带,对保障全国的生态环境安全具有重要意义。基于RWEQ模型评估了防风固沙型重点生态功能区防风固沙服务的空间格局,利用HYSPLIT模型模拟了防风固沙型重点生态功能区防风固沙服务的空间流动路径,从生态系统服务流动的角度建立了防风固沙型重点生态功能区及其防风固沙服务受益区之间的时空联系。研究表明,2010年防风固沙型重点生态功能区的防风固沙总量为5.55×10~(12) kg,受益区总面积为32.16×10~6 km~2,涉及防风固沙服务流动路径755条。受益区主要位于中国的西北、华北、东北的广大区域,朝鲜半岛,日本,俄罗斯远东地区和北太平洋的广大海域,其中中国境内的受益区占比24.12%,受益草地面积最大,受益建设用地占中国建设用地总面积的比例最高,受益效益更为明显。在空间分布上,防风固沙服务流动效益以各防风固沙型重点生态功能区为中心呈现明显的圈层式递减特征。防风固沙型重点生态功能区的防风固沙服务流动对下风向受益区的生产生活具有重要的保障作用,研究能够为防风固沙型重点生态功能区的区域间生态补偿政策制定提供科学的参考依据,从而进一步提升防风固沙型重点生态功能区的屏障作用,保障国家生态安全。
The national key ecological function zone of windbreak and sand fixation type(NKEFZ-WSF) is the main sand-proof barrier belt in China, which is of great significance for protecting the ecological environment security of the whole country. This study simulated the spatio-temporal patterns and flow trajectories of the wind erosion prevention service(WEP) in NKEFZ-WSF based on the Revised Wind Erosion Equation(RWEQ) and the Hybrid Single-Particle Lagrangian Integrated Trajectory(HYSPLIT) model, respectively, and constructed the spatio-temporal paths connection between NKEFZ-WSF and its service beneficiary areas(SBAs) of WEP. The results indicated that the total amount of WEP in the NKEFZ-WSF was 5.55×10~(12) kg in 2010 and can benefit the SBAs of 32.16×10~6 km~2, involving 755 flow paths of the WEP. The SBAs were mainly located in the vast areas of Northwest, North and Northeast China, the Korean Peninsula, Japan, Russian Far East and the vast sea areas of the North Pacific Ocean, of which the SBAs within China accounted for 24.12% and the benefiting grassland was the largest. Besides, the proportion of benefiting construction land in China′s total construction land was the highest with more obvious benefits transferred. In terms of spatial distribution, the flow benefits of the WEP had the obvious feature of decreasing in the circle type centering on each NKEFZ-WSF. The WEP flows of NKEFZ-WSF play an important role in guaranteeing the production and living quality of the downwind SBAs. This study can provide a scientific reference for the formulation of interregional ecological compensation policies for NKEFZ-WSF, so as to further enhance its barrier effect of wind prevention and sand fixation and safeguard the national ecological security.
The national key ecological function zone of windbreak and sand fixation type(NKEFZ-WSF) is the main sand-proof barrier belt in China, which is of great significance for protecting the ecological environment security of the whole country. This study simulated the spatio-temporal patterns and flow trajectories of the wind erosion prevention service(WEP) in NKEFZ-WSF based on the Revised Wind Erosion Equation(RWEQ) and the Hybrid Single-Particle Lagrangian Integrated Trajectory(HYSPLIT) model, respectively, and constructed the spatio-temporal paths connection between NKEFZ-WSF and its service beneficiary areas(SBAs) of WEP. The results indicated that the total amount of WEP in the NKEFZ-WSF was 5.55×10~(12) kg in 2010 and can benefit the SBAs of 32.16×10~6 km~2, involving 755 flow paths of the WEP. The SBAs were mainly located in the vast areas of Northwest, North and Northeast China, the Korean Peninsula, Japan, Russian Far East and the vast sea areas of the North Pacific Ocean, of which the SBAs within China accounted for 24.12% and the benefiting grassland was the largest. Besides, the proportion of benefiting construction land in China′s total construction land was the highest with more obvious benefits transferred. In terms of spatial distribution, the flow benefits of the WEP had the obvious feature of decreasing in the circle type centering on each NKEFZ-WSF. The WEP flows of NKEFZ-WSF play an important role in guaranteeing the production and living quality of the downwind SBAs. This study can provide a scientific reference for the formulation of interregional ecological compensation policies for NKEFZ-WSF, so as to further enhance its barrier effect of wind prevention and sand fixation and safeguard the national ecological security.
引文
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[2] Mikami M,Shi G Y,Uno I,Yabuki S,Iwasaka Y,Yasui M,Aoki T,Tanaka T Y,Kurosaki Y,Masuda K,Uchiyama A,Matsuki A,Sakai T,Takemi T,Nakawo M,Seino N,Ishizuka M,Satake S,Fujita K,Hara Y,Kai K,Kanayama S,Hayashi M,Du M,Kanai Y,Yamada Y,Zhang X Y,Shen Z,Zhou H,Abe O,Nagai T,Tsutsumi Y,Chiba M,Suzuki J.Aeolian dust experiment on climate impact:an overview of JapaneChina joint project ADEC.Global Planetary Change,2006,52(1- 4):142- 172.
[3] Chen L,Zhao H,Han B,Bai Z P.Combined use of WEPS and Models- 3/CMAQ for simulating wind erosion source emission and its environmental impact.Science of The Total Environment,2014,466- 467(1):762- 769.
[4] Roels B,Donders S,Werger M J A,Dong M.Relation of wind-induced sand displacement to plant biomass and plant sand-binding capacity.Acta Botanica Sinica,2001,43(9):979- 982.
[5] 王俊枝,常屹冉,匡文慧,迟文峰,张润科,付粉娥,嘎毕日.浑善达克沙漠化防治重点生态系统功能区防风固沙功能动态特征分析.北京师范大学学报(自然科学版),2018,54(3):348- 356.
[6] 刘璐璐,曹巍,吴丹,黄麟.国家重点生态功能区生态系统服务时空格局及其变化特征.地理科学,2018,38(9):1508- 1515.
[7] 黄麟,曹巍,吴丹,巩国丽,赵国松.2000—2010年我国重点生态功能区生态系统变化状况.应用生态学报,2015,26(9):2758- 2766.
[8] 王丹君.基于MODIS的中国重要生态功能区生态功能评估[D].北京林业大学,2011.
[9] 吴丹,邹长新,高吉喜.我国重点生态功能区生态状况变化.生态与农村环境学报,2016,32(5):703- 707.
[10] 张燕婷.北方防沙带土地利用格局演变特征及防风固沙功能变化评估研究[D].江西财经大学,2014.
[11] 刁兆岩.呼伦贝尔草地防风固沙功能区优先生态用地识别研究[D].北京林业大学,2015.
[12] Fryrear D W,Saleh A,Bilbro J D,Schomberg H M,Stout J E,Zobeck T M.Revised Wind Erosion Equation.USDA,ARS,Technical Bulletin No.1,June 1998.
[13] 江凌,肖燚,饶恩明,王莉雁,欧阳志云.内蒙古土地利用变化对生态系统防风固沙功能的影响.生态学报,2016,36(12):3734- 3747.
[14] 巩国丽,刘纪远,邵全琴.基于RWEQ的20世纪90年代以来内蒙古锡林郭勒盟土壤风蚀研究.地理科学进展,2014,33(6):825- 834.
[15] 巩国丽,黄麟.RWEQ模型中土壤结皮和可蚀性因子的改进和应用.水土保持通报,2018,38(2):271- 274.
[16] Du H Q,Dou S T,Deng X H,Xue X,Wang T.Assessment of wind and water erosion risk in the watershed of the Ningxia-Inner Mongolia Reach of the Yellow River,China.Ecological Indicators,2016,67:117- 131.
[17] Du H W,Xue X,Wang T,Deng X H.Assessment of wind-erosion risk in the watershed of the Ningxia-Inner Mongolia Reach of the Yellow River,northern China.Aeolian Research,2015,17:193- 204.
[18] Grell G A,Peckham S E,Schmitz R,Mckeen S A,Frost G,Skamarock W C,Eder B.Fully coupled “online” chemistry within the WRF model.Atmospheric Environment,2005,39(37):6957- 6975.
[19] Gong S L,Lavoué D,Zhao T L,Huang P,Kaminski J W.GEM-AQ/EC,an on-line global multi-scale chemical weather modelling system:Model development and evaluation of global aerosol climatology.Atmospheric Chemistry and Physics,2012,12:8237- 8256.
[20] Dennis R L,Byun D W,Novak J H,Galluppi K J,Coats C J,Vouk,M A.The next generation of integrated air quality modeling:EPA′s models- 3.Atmospheric Environment,1996,30(12):1925- 1938.
[21] Stein A F,Draxler R R,Rolph G D,Stunder B J B,Cohen M D,Ngan F.NOAA′s HYSPLIT Atmospheric Transport and Dispersion Modeling System.Bulletin of the American Meteorological Society,2015,96(12),2059- 2077.
[22] Engling G,He J,Betha R,Balasubramanian R.Assessing the regional impact of Indonesian biomass burning emissions based on organic molecular tracers and chemical mass balance modeling.Atmospheric Chemistry and Physics,2014,14(2):8043- 8054.
[23] Li W,Wang C,Wang H Q J,Chen J W,Yuan C Y,Li T C,Wang W T,Shen HZ,Huang Y,Wang R,Wang B,Zhang Y Y,Chen H,Chen Y C,Tang J H,Wang X L,Liu J F,Coveney Jr.R M,Tao S.Distribution of atmospheric particulate matter (PM) in rural field,rural village and urban areas of northern China.Environmental Pollution,2014,185(4):134- 140.
[24] Lv B L,Zhang B,Bai Y Q.A systematic analysis of PM2.5 in Beijing and its sources from 2000 to 2012.Atmospheric Environment,2015,124(B):98- 108.
[25] Draxler R R,Gillette D A,Kirkpatrick J S,Heller J.Estimating PM10 air concentrations from dust storms in Iraq,Kuwait and Saudi Arabia.Atmospheric Environment,2001,35(25):4315- 4330.
[26] Escudero M,Stein A,Draxler R R,Querol X,Alastuey A,Castillo S,Avila A.Determination of the contribution of northern Africa dust source areas to PM10 concentrations over the central Iberian Peninsula using the Hybrid Single-Particle Lagrangian Integrated Trajectory model (HYSPLIT) model.Journal of Geophysical Research Atmospheres,2006,111(D6).
[27] Rashki A,Kaskaoutis D G,Francois P,Kosmopoulos P G,Legrand M.Dust-storm dynamics over Sistan region,Iran:Seasonality,transport characteristics and affected areas.Aeolian Research,2015,16:35- 48.
[28] Draxler R R.Meteorological factors of ozone predictability at Houston,Texas.Journal of the Air & Waste Management Association,2000,50(2):259.
[29] Stein A F,Isakov V,Godowitch J,Draxler R R.A hybrid modeling approach to resolve pollutant concentrations in an urban area.Atmospheric Environment,2007,41(40):9410- 9426.
[30] Stunder B J B,Heffter J L,Draxler R R.Airborne Volcanic Ash Forecast Area Reliability.Weather & Forecasting,2007,22(5):1132- 1139.
[31] Zhang Y Y,Lang J L,Cheng S Y,Li S Y,Zhou Y,Chen D S,Zhang H Y,Wang H Y.Chemical composition and sources of PM1 and PM2.5 in Beijing in autumn.Science of The Total Environment,2018,630(15):72- 82.
[32] Zong Z,Wang X P,Tian C G,Chen Y J,Fu S F,Qu L,Ji L,Li J,Zhang G.PMF and PSCF based source apportionment of PM2.5 at a regional background site in North China.Atmospheric Research,2018,203(1):207- 215.
[33] Li T Y,Deng X J,Li Y,Song Y S,Li L Y,Tan H B,Wang C L.Transport paths and vertical exchange characteristics of haze pollution in Southern China.Science of The Total Environment,2018,625:1074- 1087.
[34] 巩国丽.中国北方土壤风蚀时空变化特征及影响因素分析[D].北京:中国科学院大学,2014.
[35] 申陆,田美荣,高吉喜,钱金平.浑善达克沙漠化防治生态功能区防风固沙功能的时空变化及驱动力.应用生态学报,2016,27(1):73- 82.
[36] Xiao Y,Xie G D,Zhen L,Lu C X,Xu J.Identifying the Areas Benefitting from the Prevention of Wind Erosion by the Key Ecological Function Area for the Protection of Desertification in Hunshandake,China.Sustainability,2017,9(10):1820.
[37] 黄富祥,牛海山,王明星,王跃思,丁国栋.毛乌素沙地植被覆盖率与风蚀输沙率定量关系.地理学报,2001,56(6):700- 710.
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