流域陆地生态系统水体净化服务表征及驱动力分析

doi:10.18306/dlkxjz.2019.04.011

Abstract

Abstract:

Water purification services of the terrestrial ecosystem can filter out pollutants in surface runoff, which helps to reduce emissions into the water body. Therefore, improving these services is an effective way to control non-point source pollution in watersheds, and accurately quantifying the spatiotemporal characteristics and driving factors of change of the services is the precondition for such improvement. Purification services for nitrogen and phosphorus are two typical water purification services, which can be quantified by nitrogen and phosphorus exports as the reverse proxy indicators. Taking a representative area of the Taihu Lake Basin in 2000-2010 as a case, we used the nutrient purification model in the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) tool to quantify nitrogen and phosphorus indicators of terrestrial ecosystem. The spatial pattern and temporal variation of purification services for nitrogen and phosphorus were characterized by the spatial analysis method in ArcGIS. In order to quantify the driving factors of change of the services, we developed a panel data model based on 31 selected factors and GIS spatial statistics methods. The results show that there were obvious spatial heterogeneities between nitrogen and phosphorus indicators and change over time. Both nitrogen and phosphorus purification services showed a spatial characteristic of widely weak decrease from 2000 to 2010, with the areal ratios of 59.19% and 58.27%, respectively. With regard to the temporal variation of service amount, nitrogen purification service of the study area first reduced then increased slightly during 2000-2005 and 2005-2010, and the increase in Suzhou urban district, Wuxi urban district, and Kunshan City were obvious. Phosphorus purification service was always in decline during 2000-2005 and 2005-2010, and the urban districts of Wuxi and Suzhou experienced the largest reduction. Water purification services are influenced by multiple driving factors in the physical, social, and economic domains. Climatic factors and water network density had significant positive influences on these two services, while the negative driving factors differed. Arable land proportion, village density, and agricultural population density had major negative effects on nitrogen purification service; while urban land density mainly impacted negatively on phosphorus purification service. Therefore, appropriate measures should be implemented to improve these two water purification services according to the driving factor analysis. Climate change adaptation policy can contribute to synergistically manage nitrogen and phosphorus purification services. Meanwhile, different control measures should be taken because of the varied driving factors between nitrogen and phosphorus purification services. Improving production and living conditions in rural areas and guiding individual behaviors of farmers can help to reduce the impact of agricultural activities on nitrogen purification service. Adjusting production and daily living activities in urban areas can reduce phosphorus emissions and enhance the phosphorus purification service of the terrestrial ecosystem. This study can provide support for non-point source pollution control and water management in watersheds.

Key words: terrestrial ecosystem, water purification services, spatiotemporal variation, driving factor, Taihu Lake Basin

Yang LIU, Jianshu LV, Jun BI. Characterizing water purification services and quantifying their driving factors in watershed terrestrial ecosystems[J].PROGRESS IN GEOGRAPHY, 2019, 38(4): 588-599.

Figures/Tables 9

Fig.1

Fig.2

Tab.1

Tab.2

Fig.3

Fig.4

Fig.5

Fig.6

Tab.3

Regression analysis results of driving factors of water purification services variation in the study area"

驱动力类型	驱动力	氮输出量变化的回归结果				磷输出量变化的回归结果
驱动力类型	驱动力	回归系数	标准差	t值		回归系数	标准差	t值
气候因子	日照百分率	-3.935^***	0.513	-7.660		-2.258^***	0.419	-5.390
	相对湿度	-7.022^***	0.531	-13.220		-6.017^***	0.435	-13.840
	年均气温	-4.313^***	0.840	-5.130		-2.365^***	0.687	-3.440
地形因子	高程	-0.582^***	0.025	-22.970		-0.537^***	0.021	-25.930
地形因子	坡度	0.003	0.004	0.760		0.000		0.005	-0.060
水文因子	水网密度	-6.382^***	0.476	-13.420		-4.144^***	0.480	-8.630
土壤因子	土壤表层总氮含量	0.149^***	0.028	5.410		0.123^***	0.023	5.410
	土壤表层总磷含量	-0.202^***	0.029	-7.050		0.164^***		0.023	7.010
	土壤表层总钾含量	-0.205^***	0.043	-4.740		-0.153^***		0.036	-4.300
	土壤表层有机质含量	0.148^***	0.038	3.890		0.193^***		0.053	3.650
	土壤容重	-0.320^*	0.185	1.730		0.131		0.084	1.570
	土壤表层砂粒含量比	0.217^***	0.072	-3.020		0.163^***		0.059	-2.750
	土壤表层粉砂含量比	-0.360^***	0.052	6.910		-0.253^***		0.043	5.910
	土壤表层粘粒含量比	-0.097^**	0.043	-2.230	-0.128^***		0.036	-3.510
植被因子	植被覆盖指数	-0.281^**	0.126	-2.220	-0.012^**	0.006	-2.080
人口经济因子	单位面积GDP	0.009	0.010	0.860	0.004	0.009	0.430
人口经济因子	人口密度	0.001	0.005	0.140	0.002		0.002	1.220
建设用地密度	城镇用地密度	-3.008	1.936	-1.550	3.444^**	1.584	2.170
	农村居民点密度	2.198^***	0.514	4.270	2.242^***		0.526	4.260
	道路密度	-0.005	0.004	-1.270	-0.006^**		0.003	-2.030
农业发展因子	农业人口密度	0.194^**	0.101	2.930	0.162^**	0.082	-1.970
	农业GDP	0.050	0.061	0.820	0.031		0.045	0.690
	农业机械总动力	0.022	0.044	0.500	0.022		0.036	0.610
	农民年收入	0.041	0.069	0.590	0.016		0.055	0.280
植被用地因子	耕地比例	1.324^***	0.212	6.240	1.159^***	0.353	3.280
	林地比例	-0.375^***	0.065	2.890	-0.123^**		0.217	-2.030
	草地比例	-2.343	1.499	-1.560	-1.972		1.221	-1.610
	湿地比例	-0.411	0.428	-0.960	-0.831^**		0.467	-2.100
区位可达性因子	离水体的距离	0.459^***	0.006	77.680	0.081^***	0.019	4.350
	离城镇的距离	0.016^***	0.004	4.160	-0.039^***		0.003	-12.140
	离农村的距离	-0.025^***	0.003	-7.830	-0.065^***		0.003	-23.850
常量		4.402	11.333	0.390	-3.238	9.212	-0.350

Tab.3

References 48

[1]	白杨, 郑华, 庄长伟, 等. 2013. 白洋淀流域生态系统服务评估及其调控[J]. 生态学报, 33(3): 711-717. doi: 10.5846/stxb201203270417
	[Bai Y, Zheng H, Zhuang C W, et al.2013. Ecosystem services valuation and its regulation in Baiyangdian Basin: Based on InVEST model. Acta Ecologica Sinica, 33(3): 711-717. ] doi: 10.5846/stxb201203270417
[2]	陈晓红, 周宏浩. 2018. 城市化与生态环境关系研究热点与前沿的图谱分析[J]. 地理科学进展, 37(9): 1171-1185.
	[Chen X H, Zhou H H.2018. Research hotspots and prospects of urbanization and ecological environment relationship based on visual knowledge mapping. Progress in Geography, 37(9): 1171-1185. ]
[3]	段亮, 段增强, 夏四清. 2006. 太湖旱地非点源污染定量化研究[J]. 水土保持通报, 26(6): 40-43. doi: 10.3969/j.issn.1000-288X.2006.06.010
	[Duan L, Duan Z Q, Xia S Q.2006. Quantification of non-point pollution from uplands in Taihu Lake Catchment. Bulletin of Soil and Water Conservation, 26(6): 40-43. ] doi: 10.3969/j.issn.1000-288X.2006.06.010
[4]	黄林, 王峰, 周立江, 等. 2012. 不同森林类型根系分布与土壤性质的关系[J]. 生态学报, 32(19): 6110-6119. doi: 10.5846/stxb201108281254
	[Huang L, Wang F, Zhou L J, et al.2012. Root distribution in the different forest types and their relationship to soil properties. Acta Ecologica Sinica, 32(19): 6110-6119. ] doi: 10.5846/stxb201108281254
[5]	纪迪, 张慧, 沈渭寿, 等. 2013. 太湖流域下垫面改变与气候变化的响应关系[J]. 自然资源学报, 28(1): 51-62. doi: 10.11849/zrzyxb.2013.01.006
	[Ji D, Zhang H, Shen W S, et al.2013. The response relationship between underlying surface changing and climate change in the Taihu Basin. Journal of Natural Resources, 28(1): 51-62. ] doi: 10.11849/zrzyxb.2013.01.006
[6]	李恒鹏, 杨桂山, 黄文钰, 等. 2007. 太湖上游地区面源污染氮素入湖量模拟研究[J]. 土壤学报, 44(6): 1063-1069. doi: 10.11766/trxb200608240615
	[Li H P, Yang G S, Huang W Y, et al.2007. Simulating fluxes of non-point source nitrogen from upriver region of Taihu Basin. Acta Peologica Sinica, 44(6): 1063-1069. ] doi: 10.11766/trxb200608240615
[7]	吕刚, 魏忠平, 高英旭, 等. 2013. 不同土地利用类型植物根系与土壤抗蚀性关系研究[J]. 干旱地区农业研究, 31(2): 111-115. doi: 10.3969/j.issn.1000-7601.2013.02.021
	[Lv G, Wei Z P, Gao Y X, et al.2013. Study on relationship between plant roots and soil anti-erodibility of different land utilization types. Agricultural Research in the Arid Areas, 31(2): 111-115. ] doi: 10.3969/j.issn.1000-7601.2013.02.021
[8]	吕文, 杨桂山, 万荣荣, 等. 2013. 太湖流域春季不同土地利用类型蒸散速率的比较[J]. 水土保持通报, 33(5): 202-209.
	[Lv W, Yang G S, Wan R R, et al.2013. Comparison study on evapotranspiation characteristics of different landuse types in Taihu Lake Wastershed. Bulletin of Soil and Water Conservation, 33(5): 202-209. ]
[9]	王宁, 郭红岩, 王晓蓉, 等. 2008. 太湖河网地区农村非点源氮负荷: 以宜兴市大浦镇为例[J]. 生态学杂志, 27(4): 557-562.
	[Wang N, Guo H Y, Wang X R, et al.2008. Nitrogen load from ruralnon-point source in river network region, Taihu Lake: A case study from Dapu Town in Yixing City. Chinese Jurnal of Eology, 27(4): 557-562. ]
[10]	王小治, 王爱礼, 尹微琴, 等. 2009. 太湖流域农业非点源污染优先识别区研究: 以昆山为例[J]. 农业环境科学学报, 28(9): 1874-1879. doi: 10.3321/j.issn:1672-2043.2009.09.017
	[Wang X Z, Wang A L, Yin W Q, et al.2009. Application of agricultural non-point source pollution potential index in typical area of Taihu: A case study in Kunshan City. Journal of Agro-Environment Science, 28(9): 1874-1879. ] doi: 10.3321/j.issn:1672-2043.2009.09.017
[11]	吴攀, 秦伯强, 于革, 等. 2015. 太湖上游流域经济发展对废水排放及入湖总磷的影响[J]. 湖泊科学, 27(6): 1107-1114. doi: 10.18307/2015.0616
	[Wu P, Qin B Q, Yu G, et al.2015. Effects of economic development on wastewater discharge and influent total phosphorus load in the upstream of Lake Taihu Basin. Journal of Lake Science, 27(6): 1107-1114. ] doi: 10.18307/2015.0616
[12]	夏敏, 班伟, 赵冰雪. 2013. 太湖流域非点源污染负荷估算系统的设计与应用[J]. 水土保持通报, 33(3): 197-201.
	[Xia M, Ban W, Zhao B X.2013. Design and application of norrpoint source pollution load estimating system in Taihu Lake Basin. Bulletin of Soil and Water Conservation, 33(3): 197-201. ]
[13]	闫丽珍, 石敏俊, 王磊. 2010. 太湖流域农业面源污染及控制研究进展[J]. 中国人口·资源与环境, 20(1): 99-107. doi: 10.3969/j.issn.1002-2104.2010.01.018
	[Yan L Z, Shi M J, Wang L.2010. Review of agricultural non-point pollution in Taihu Lake and Taihu Basin. China Population, Resources and Environment, 20(1): 99-107. ] doi: 10.3969/j.issn.1002-2104.2010.01.018
[14]	闫庆武, 卞正富, 赵华. 2005. 人口密度空间化的一种方法[J]. 地理与地理信息科学, 21(5): 45-48. doi: 10.3969/j.issn.1672-0504.2005.05.011
	[Yan Q W, Bian Z F, Zhao H.2005. A method of spatialization of population density. Geography and Geo-Information Science, 21(5): 45-48. ] doi: 10.3969/j.issn.1672-0504.2005.05.011
[15]	赵文武, 刘月, 冯强, 等. 2018. 人地系统耦合框架下的生态系统服务[J]. 地理科学进展, 37(1): 139-151. doi: 10.18306/dlkxjz.2018.01.015
	[Zhao W W, Liu Y, Feng Q, et al.2018. Ecosystem services for coupled human and environment systems. Progress in Geography, 37(1): 139-151. ] doi: 10.18306/dlkxjz.2018.01.015
[16]	Andrew M E, Wulder M A, Nelson T A, et al.2015. Spatial data, analysis approaches, and information needs for spatial ecosystem service assessments: A review[J]. GIScience & Remote Sensing, 52(3): 344-373.
[17]	Chen J, Cui T, Wang H, et al.2018. Spatio-temporal evolution of water-related ecosystem services: Taihu Basin, China[J]. PeerJ, 6: e5041. doi: 10.7717/peerj.5041. doi: 10.7717/peerj.5041
[18]	Gao J, Li F, Gao H, et al.2017. The impact of land-use change on water-related ecosystem services: A study of the Guishui River Basin, Beijing, China[J]. Journal of Cleaner Production, 163: S148-S155. doi: 10.1016/j.jclepro.2016.01.049
[19]	Grizzetti B, Lanzanova D, Liquete C, et al.2016. Assessing water ecosystem services for water resource management[J]. Environmental Science & Policy, 61: 194-203. doi: 10.1016/j.envsci.2016.04.008
[20]	Guswa A J, Brauman K A, Brown C, et al.2014. Ecosystem services: Challenges and opportunities for hydrologic modeling to support decision making[J]. Water Resources Research, 50(5): 4535-4544. doi: 10.1002/2014WR015497
[21]	Hou Y, Li B, Müller F, et al.2016. Ecosystem services of human-dominated watersheds and land use influences: A case study from the Dianchi Lake Watershed in China[J]. Environmental Monitoring & Assessment, 188(11): 1-19. doi: 10.1007/s10661-016-5629-0 pmid: 27822787
[22]	Hu Y, Peng J, Liu Y, et al.2018. Integrating ecosystem services trade-offs with paddy land-to-dry land decisions: A scenario approach in Erhai Lake Basin, Southwest China[J]. Science of the Total Environment, 625: 849-860. doi: 10.1016/j.scitotenv.2017.12.340 pmid: 29306828
[23]	Huang L, Ban J, Huang Y, et al.2013. Multi-angle indicators system of non-point pollution source assessment in rural areas: A case study near Taihu Lake[J]. Environmental Management, 51(4): 939-950. doi: 10.1007/s00267-013-0024-x pmid: 23456193
[24]	Jorda-Capdevila D, Gampe D, Huber V G, et al.2019. Impact and mitigation of global change on freshwater-related ecosystem services in southern Europe[J]. Science of the Total Environment, 651: 895-908. doi: 10.1016/j.scitotenv.2018.09.228
[25]	Jose-Manuel A M, Susana S S, Calabuig E D L.2011. Modelling the risk of land cover change from environmental and socio-economic drivers in heterogeneous and changing landscapes: The role of uncertainty[J]. Landscape & Urban Planning, 101(2): 108-119. doi: 10.1016/j.landurbplan.2011.01.009
[26]	Keeler B, Dalzell B J, Pennington D, et al.2013. Comparing SWAT and InVEST models for water yield and nutrient export: When is a simple model good enough for decision support? [R]. American Geophysical Union, Fall Meeting 2013. San Francisico: American Geophysical Union: 1135.
[27]	Kirsten H, Michel L, Stefano A.2018. How ecological feedbacks between human population and land cover influence sustainability[J]. PLOS Computational Biology, 14(8): e1006389. doi: 10.1371/journal.pcbi.1006389
[28]	La Notte A, Dalmazzone S.2018. Sustainability assessment and causality nexus through ecosystem service accounting: The case of water purification in Europe[J]. Journal of Environmental Management, 223: 964-974. doi: 10.1016/j.jenvman.2018.06.072
[29]	Li J, Jiang H, Bai Y, et al.2016. Indicators for spatial-temporal comparisons of ecosystem service status between regions: A case study of the Taihu River Basin, China[J]. Ecological Indicators, 60: 1008-1016. doi: 10.1016/j.ecolind.2015.09.002
[30]	Liu M, Huang G H, Liao R F, et al.2013. Fuzzy two-stage non-point source pollution management model for agricultural systems: A case study for the Lake Tai Basin, China[J]. Agricultural Water Management, 121: 27-41. doi: 10.1016/j.agwat.2013.01.006
[31]	Mekonnen M M, Hoekstra A Y.2018. Global anthropogenic phosphorus loads to freshwater and associated grey water footprints and waterpollution levels: A high-resolution global study[J]. Water Resources Research, 54(1): 345-358. doi: 10.1002/2017WR020448
[32]	Nazmul H, Antje B, Lars R.2019. Interactions between freshwater ecosystem services and land cover changes in southern bangladesh: A perspective from short-term (seasonal) and long-term (1973-2014) scale[J]. Science of The Total Environment, 650: 132-143. doi: 10.1016/j.scitotenv.2018.08.430
[33]	Ochoa V, Urbina-Cardona N.2017. Tools for spatially modeling ecosystem services: Publication trends, conceptual reflections and future challenges[J]. Ecosystem Services, 26: 155-169. doi: 10.1016/j.ecoser.2017.06.011
[34]	Ouyang Z Y, Zhang H, Xiao Y, et al.2016. Improvements in ecosystem services from investments in natural capital[J]. Science, 352: 1455-1459. doi: 10.1126/science.aaf2295 pmid: 27313045
[35]	Pennington D N, Dalzell B, Nelson E, et al.2017. Cost-effective land use planning: Optimizing land use and land management patterns to maximize social benefits[J]. Ecological Economics, 139: 75-90. doi: 10.1016/j.ecolecon.2017.04.024
[36]	Qi W, Li H, Zhang Q, et al.2019. Forest restoration efforts drive changes in land-use/land-cover and water-related ecosystem services in China's Han River Basin[J]. Ecological Engineering, 126: 64-73. doi: 10.1016/j.ecoleng.2018.11.001
[37]	Redhead J W, May L, Oliver T H, et al.2017. National scale evaluation of the InVEST nutrient retention model in the United Kingdom[J]. Science of the Total Environment, 610-611: 666-677.
[38]	Reidsma P, Feng S Y, Van Loon M, et al.2012. Integrated assessment of agricultural land use policies on nutrient pollution and sustainable development in Taihu Basin, China[J]. Environmental Science & Policy, 18(4): 66-76.
[39]	Salvia-Castellvi M, Iffly J F, Borght P V, et al.2005. Dissolved and particulate nutrient export from rural catchments: A case study from Luxembourg[J]. Science of the Total Environment, 344(1-3): 51-65. doi: 10.1016/j.scitotenv.2005.02.005 pmid: 15907510
[40]	Seppelt R, Dormann C, Eppink F V, et al.2011. A quantitative review of ecosystem service studies: Approaches, shortcomings and the road ahead[J]. Journal of Applied Ecology, 48(3): 630-636. doi: 10.1111/j.1365-2664.2010.01952.x
[41]	Sharps K, Masante D, Thomas A, et al.2017. Comparing strengths and weaknesses of three ecosystem services modelling tools in a diverse UK river catchment[J]. Science of the Total Environment, 584-585: 118-130. doi: 10.1016/j.scitotenv.2016.12.160 pmid: 28147292
[42]	Shi X Z, Wang H J, Warner E D, et al.2010. Cross-reference for relating Genetic Soil Classification of China with WRB at different scales[J]. Geoderma, 154(1): 344-350. doi: 10.1016/j.geoderma.2009.12.017
[43]	Sun X, Crittenden J C, Li F, et al.2018. Urban expansion simulation and the spatio-temporal changes of ecosystem services, a case study in Atlanta metropolitan area, USA[J]. Science of the Total Environment, 622-623: 974-987. doi: 10.1016/j.scitotenv.2017.12.062 pmid: 29890614
[44]	Wong C P, Jiang B, Kinzig A P, et al.2015. Linking ecosystem characteristics to final ecosystem services for public policy[J]. Ecology Letters, 18(1): 108-118. doi: 10.1111/ele.12389 pmid: 25394857
[45]	Xiao S, Lu Z, Feng L, et al.2018. Analyzing spatio-temporal changes and trade-offs to support the supply of multiple ecosystem services in Beijing, China[J]. Ecological Indicators, 94: 117-129. doi: 10.1016/j.ecolind.2018.06.049
[46]	Xu X, Yang G, Tan Y, et al.2016. Ecological risk assessment of ecosystem services in the Taihu Lake Basin of China from 1985 to 2020[J]. Science of the Total Environment, 554-555: 7-16. doi: 10.1016/j.scitotenv.2016.02.120
[47]	Yan Y, Guan Q, Wang M, et al.2018. Assessment of nitrogen reduction by constructed wetland based on InVEST: A case study of the Jiulong River Watershed, China[J]. Marine Pollution Bulletin, 133: 349-356. doi: 10.1016/j.marpolbul.2018.05.050
[48]	Zhou Y, Ma J, Zhang Y, et al.2017. Improving water quality in China: Environmental investment pays dividends[J]. Water Research, 118: 152-159. doi: 10.1016/j.watres.2017.04.035 pmid: 28431347

驱动力类别	驱动力简称	驱动力指标	单位
气候因素	SUN	日照百分率	%
	ARH	年均相对湿度	%
	TEM	年均气温	℃
地形因素	ALT	高程	m
地形因素	SLOP	坡度	°
水文因素	WATER	水网密度	hm²/km²
土壤因素	TN	土壤表层(0~20 cm)的总氮含量	mg/m³
	TP	土壤表层(0~20 cm)的总磷含量	mg/m³
	TK	土壤表层(0~20 cm)的总钾含量	mg/m³
	SOM	土壤表层(0~20 cm)的有机质含量	mg/m³
	BULK	土壤容重	g/cm³
	SAND	土壤表层(0~20 cm)的砂粒(0.05~2.0 mm)含量百分比	%
	SILT	土壤表层(0~20 cm)的粉砂(0.002~0.05 mm)含量百分比	%
	CLAY	土壤表层(0~20 cm)的粘粒(<0.002 mm)含量百分比	%
植被覆盖度	NDVI	植被覆盖指数	[-1, 1]
人口经济情况	POP	人口密度	人/km²
人口经济情况	GDP	单位面积国内生产总值	万元/km²
建设用地密度	URBDENS	城镇建设用地密度	hm²/km²
	VILLDENS	农村居民点密度	hm²/km²
	ROADENS	道路密度	km/km²
农业发达程度	AGRIPOP	农业人口密度	人/km²
	AGRIGDP	单位面积农业生产总值	万元/km²
	AGRPOWER	农业机械总动力	kW/hm²
	FARINCM	农民年收入	元/人
植被用地状况	ARABLE	单位面积耕地比例	%
	FOREST	单位面积林地比例	%
	GRASS	单位面积草地比例	%
	WETLAND	单位面积湿地比例	%
区位可达性	DISWAT	离水体的距离	m
	DISURB	离城镇的距离	m
	DISVILL	离农村的距离	m

Characterizing water purification services and quantifying their driving factors in watershed terrestrial ecosystems

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 9

References 48

Related Articles 15

Metrics

Comments

Recommended 0

地类	编码	根系长度/mm	蒸散系数	氮输出系数/(kg/hm²)	氮滤除率/%	磷输出系数/(kg/hm²)	磷滤除率/%
水田	1	700	0.85	19.40	30	1.22	30
旱地	2	800	0.60	14.70	40	0.59	40
有林地	3	7000	1.00	2.12	80	0.15	80
灌木林	4	6000	1.00	2.12	70	0.15	70
疏林地	5	5500	0.90	3.12	60	0.18	60
其他林地	6	3000	0.85	2.62	50	0.16	50
草地	7	2000	0.65	3.20	48	0.20	40
河渠	8	800	0.95	0.001	5	0.001	5
湖泊	9	1000	1.00	0.001	5	0.001	5
水库坑塘	10	1000	1.00	0.001	5	0.001	5
湿地	11	6000	0.95	2.00	80	0.05	80
城镇用地	12	1	0.65	12.00	5	2.10	5
农村居民点	13	1	0.30	20.00	10	2.50	5
其他建设用地	14	1	0.20	6.00	0.01	1.50	0.01
未利用地	15	1	0.22	1.45	5	0.045	5

[1]	SUN Yijie, LIU Xianfeng, REN Zhiyuan, DUAN Yifang. Spatiotemporal changes of droughts and heatwaves on the Loess Plateau during 1960-2016 [J]. PROGRESS IN GEOGRAPHY, 2020, 39(4): 591-601.
[2]	CAO Zhihong, HAO Jinmin, XING Hongping. Spatial-temporal change of Chinese resident food consumption carbon emissions and its driving mechanism [J]. PROGRESS IN GEOGRAPHY, 2020, 39(1): 91-99.
[3]	Yanxi ZHAO, Dengpan XIAO, Huizi BAI, Fulu TAO. Research progress on the response and adaptation of crop phenology to climate change in China [J]. PROGRESS IN GEOGRAPHY, 2019, 38(2): 224-235.
[4]	Kaihua LIAO, Ligang LV. Advances in research of hillslope soil hydrological processes in the humid region of Southeast China [J]. PROGRESS IN GEOGRAPHY, 2018, 37(4): 476-484.
[5]	Yaolin LIU, Yang ZHANG, Yan ZHANG, Yi LIU, Haofeng WANG, Yanfang LIU. Conflicts between three land management red lines in Wuhan City: Spatial patterns and driving factors [J]. PROGRESS IN GEOGRAPHY, 2018, 37(12): 1672-1681.
[6]	Shen ZHAO, Shaohui CHEN. Spatiotemporal variations of evapotranspiration and potential evapotranspiration in Shandong Province based on station observations and MOD16 [J]. PROGRESS IN GEOGRAPHY, 2017, 36(8): 1040-1047.
[7]	Yuanzheng LI, Ke YIN, Hongxuan ZHOU, Xiaolin WANG, Dan HU. Progress in urban heat island monitoring by remote sensing [J]. PROGRESS IN GEOGRAPHY, 2016, 35(9): 1062-1074.
[8]	Guoqing LI, Xiaobing LI. Research progress of wind farm impact on the environment [J]. PROGRESS IN GEOGRAPHY, 2016, 35(8): 1017-1026.
[9]	Shengyun WANG. Driving factors and spatiotemporal differentiation of human well-being change in China [J]. PROGRESS IN GEOGRAPHY, 2016, 35(5): 632-643.
[10]	Xi HUANG, Zhonggen WANG, Yanfang SANG, Moyuan YANG, Xiaocong LIU, Tongliang GONG. Precision of data in three precipitation datasets of the Yarlung Zangbo River Basin [J]. PROGRESS IN GEOGRAPHY, 2016, 35(3): 339-348.
[11]	rrJiangbin YIN. Advances in research on driving factors of return migration and employment behavior of migrants [J]. PROGRESS IN GEOGRAPHY, 2015, 34(9): 1084-1095.
[12]	Jian HU, Yihe LÜ. Research progress on stochastic soil moisture dynamic model [J]. PROGRESS IN GEOGRAPHY, 2015, 34(3): 389-400.
[13]	Haoming XIA, Ainong LI, Wei ZHAO, Jinhu BIAN, Guangbin LEI. Spatiotemporal variations of forest phenology in the Qinling zone based on remote sensing monitoring, 2001-2010 [J]. PROGRESS IN GEOGRAPHY, 2015, 34(10): 1297-1305.
[14]	NIU Pinyi, LU Yuqi, PENG Qian. Driving factors of urbanization in Jiangsu Province based on quantile regression [J]. PROGRESS IN GEOGRAPHY, 2013, 32(3): 372-380.
[15]	ZHANG Leqin, CHEN Suping, WANG Wenqin, XU Xinwang. Research on the Driving Factor Measurement of the Construction Land Expansion in Chizhou City, Anhui Province: Based on the STIRPAT Model [J]. PROGRESS IN GEOGRAPHY, 2012, 31(9): 1235-1242.