国土资源遥感, 2020, 32(2): 251-258 doi: 10.6046/gtzyyg.2020.02.32

技术应用

基于PSR模型的四川生态系统健康时空动态研究

徐洲洋

西南石油大学土木工程与建筑学院,成都 610500

Spatial-temporal dynamics of ecosystem health in Sichuan Province based on PSR model

XU Zhouyang

School of Civil Engineering and Architecture, Southwest Petroleum University, Chengdu 610500,China

责任编辑: 张仙

收稿日期: 2019-05-17   修回日期: 2019-08-15   网络出版日期: 2020-06-15

基金资助: 国家自然科学基金项目“基于人类动力学的面向震后救援的人员在地理建筑空间的分布规律研究”.  51774250
四川省科技计划项目“基于移动终端的室内定位技术的面向地震救援的人群在地理空间分布规律研究”.  2019JDR0112

Received: 2019-05-17   Revised: 2019-08-15   Online: 2020-06-15

作者简介 About authors

徐洲洋(1994-),男,硕士研究生,研究方向为地理信息技术的应用。Email:787090191@qq.com。 。

摘要

四川省快速的经济发展和城镇化进程,引发了许多生态环境问题,评价其生态健康状况对区域可持续发展具有重要的意义。基于2000—2016年间四川省社会、经济、农业、生态等市级数据,以及降尺度模型获取的县级数据,建立压力-状态-响应模型,分市级和县级2种尺度对四川省区域的生态系统健康进行综合评价和分析。结果表明: 在2000—2016年间,四川省生态系统健康状况整体得分从0.610上升到0.687,整体的生态状况越来越好,但区域之间的发展极不平衡,东部地区优于西部地区,成都市的优势突出,与其他地区的差距明显; 对各县的生态系统健康状况评价等级为“不健康”及“病态”的县占总数的27.9%,一半左右的县在“正常”等级以上, 等级为“病态”的县主要集中在甘孜藏族自治州和阿坝藏族羌族自治州,应对川西北部的生态健康状况引起足够的重视。

关键词： 生态系统健康 ; 压力 ; 状态 ; 响应 ; 四川省

Abstract

A great many ecological and environmental problems have been caused in the course of rapid economic development and urbanization in Sichuan Province. Evaluating ecosystem health condition is of significant importance for regional sustainable development. Based on relevant city-level data in social, economic, agricultural and ecological aspects from 2000 to 2016 as well as county-level data obtained through the downscaling model, the authors built the pressure-status-response model to make comprehensive evaluation and analysis of ecosystem health of studied regions in Sichuan Province on city-level and county-level scales. The results show that the overall score of ecosystem health in Sichuan increased from 0.610 to 0.687 in 2000—2016, but the spatial distribution shows that the eastern part of Sichuan was better than the western part. The development between studied regions is extremely unbalanced. Chengdu had outstanding advantages in comparison other regions. The results of ecosystem health assessment in each county shows that 27.9% of the total counties are “unhealthy” and “sick”, while about half of the counties are above the “normal” level. The counties whose evaluation results are “sick” are mainly concentrated in Garze and Aba prefectures. The ecological health status in northwest Sichuan deserves much more attention.

Keywords： ecosystem health ; pressure ; state ; response ; Sichuan Province

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本文引用格式

徐洲洋. 基于PSR模型的四川生态系统健康时空动态研究. 国土资源遥感[J], 2020, 32(2): 251-258 doi:10.6046/gtzyyg.2020.02.32

XU Zhouyang. Spatial-temporal dynamics of ecosystem health in Sichuan Province based on PSR model. REMOTE SENSING FOR LAND & RESOURCES[J], 2020, 32(2): 251-258 doi:10.6046/gtzyyg.2020.02.32

0 引言

随着社会的发展,人类活动所带来的空气污染、水土流失和生态系统退化等问题对周边的生态系统产生了深远的影响,并已经成为了限制地区发展的巨大障碍[1]。生态系统的健康通常是指某生态系统拥有长时间维持其良好组织结构的稳定性和可持续性的能力,以及满足人类必要的物质和生态需求的能力,同时能够进行自我调节和具有对胁迫的恢复能力[2]。目前,生态系统健康已成为社会所关注的热点问题,区域生态系统健康是综合评价生态系统健康的核心,也是规范区域开发、环境保护和可持续发展的重要基础[3]。因此,对区域生态系统健康状况进行合理有效的评价,可及时识别区域内环境或经济危机,对可持续发展具有十分重要的作用。

自生态系统健康的概念[4]提出以来,国内外的学者围绕生态系统健康的问题开展了一系列的研究,都取得了显著的效果。目前,针对城市[5]、湿地[6]、森林[7]、海洋[8]和农业[9]等不同类型的生态系统,已有不同的模型来对其生态系统健康状况进行评价。1979年,Murray等[10]对海洋生态系统受放射性辐射影响进行评估,并研究出了一种可行的方法; Spiegel等[11]在对城市生态系统健康评估的过程中使用了驱动力-压力-状态-响应模型来进行研究,取得了不错的效果; 江香梅等[12]对鄱阳湖湿地生态系统进行了研究分析,并根据目前所面临的问题(如水域面积减小、生物多样性降低、水质水量下降等),对鄱阳湖湿地生态系统恢复从多个方面提出了可行的建议。遥感(remote sensing, RS)和地理信息系统(geographic information system,GIS)技术在生态系统健康研究方面的运用[13]推动了生态监测的发展。汪阳等[14]基于GIS系统对洪泽湖生态系统健康状况进行了综合评估; 陈鹏[15]利用压力-状态-响应(pressure-state-response,PSR)模型,在RS和GIS技术支持下获取厦门新区(翔安区)的生态健康评价指标,定量地评价了城市新区生态健康状况。

生态系统健康评价中常用的PSR模型是一种多变量的方法,它不仅关注生态系统健康的自然属性,而且在评价过程中还将这些自然属性与人类属性相结合[16]。目前,四川省经济发展较快但省内区域发展极不平衡,需要对其生态状况进行细致的评价和分析,做出有利于可持续发展的选择。本文利用PSR模型对四川省2000—2016年间的相关数据进行整理,对四川省的市级生态系统健康状况进行了评价,运用空间分析模型,对生态系统健康状况变化趋势进行了深入剖析,进而利用降尺度模型开展了更加精细化的县级尺度评价,旨在为四川省的可持续发展状态评估提供决策支持。

1 研究区概况及数据源

1.1 研究区概况

本文研究区四川省位于我国西南部,地理范围在N26°03'~34°19',E92°21'~108°12'之间(图1),面积约为48.6万km2; 共包含21个地市级行政区划单位和183个县级行政区划单位。区域内环境复杂,物种多样性高; 地势呈西高东低特点,包括山地、丘陵、平原盆地和高原等不同的地形,不同地区之间的气候、自然环境和社会经济差异较大。省会成都市地处成都平原,自然资源和环境条件优越,人口众多,在省内经济发展状况一直处于领先地位。

图1

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图1   研究区地理位置

Fig.1   Location of the study area


1.2 数据源及其预处理

四川省2000—2016年间市县级相关社会经济数据来源于《四川省统计年鉴》和《区域经济统计年鉴》,少量缺失的市县级数据从《中国县级统计年鉴》中补充,最后通过拟合各指标时间序列趋势对异常和遗漏数据(其占比仅为0.72%)进行了矫正和补充。气象站站点数据从中国气象局网站下载(http://data.cma.cn/)。为获取平面上的栅格数据,在ArcGIS10.4软件中利用克里金法对站点数据进行了插值,空间分辨率为5 km,并统计各区域的平均值,与统计数据做散点图验证,拟合决定系数R2>0.93。此外,归一化差分植被指数(normalized differential vegetation index,NDVI)数据为从美国国家航空航天局网站(http://modis.gsfc.nasa.gov)下载的MODIS产品,数据间隔为16 d,空间分辨率为1 km。30 m空间分辨率土地覆盖数据集由国家综合地球观测数据共享平台(http://www.chinageoss.org/dsp/home/index.jsp)提供。

2 研究方法

2.1 PSR评价模型与框架

由于生态系统的复杂性,有效地评估生态系统健康需要结合自然和人为因素来综合构建评估生态系统健康的体系和模型 [17,18]。PSR模型不仅考虑生态系统的自然因素,还充分考虑人类活动对生态系统的影响,从压力、状态和响应3个方面进行评估,更加综合、动态及信息化[16,19]。本文根据大量的相关研究成果[16,20-22]和指标体系的选择原则,构建了应用于研究区的PSR模型指标体系(如表1)。

表1   生态系统健康评价指标

Tab.1  Ecosystem health assessment indicators

一级指标二级指标三级指标权重
压力自然气温距平0.065
降雨距平0.094
农业农作物播种面积0.263
化肥施用量0.138
人口人口密度0.216
人口自然增长率0.223
状态经济人均国内生产总值(gross domestic product,GDP)0.197
农民人均纯收入0.224
农业单位面积粮食产量0.183
单位面积粮食产量0.232
生态NDVI0.163
响应农业农业机械总动力0.222
有效灌溉面积0.179
社会公路总里程0.174
第三产业占比0.085
人均地方财政一般预算支出0.168
人均全社会固定资产投资0.171

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本文指标数据的归一化采用极值差分法[23],各参数的权重用熵权法确定[24]。首先分别计算区域生态系统压力、状态、响应和健康得分,再计算区域生态系统健康综合得分,计算公式分别为:

Si= ∑j=1ki(WijFij)2,
Q= ∑i=13Si2,

式中: ki为第i种一级指标(压力、状态、响应)内的三级指标总个数; Si为压力、状态和响应的得分值; Wij为第i种一级指标内第j个三级指标的权重; Fij为第i种一级指标内第j个三级指标的归一化值; Q为区域生态系统健康综合得分。

2.2 空间演变分析

2.2.1 区域生态系统健康的全局特征

本文使用变异系数法和首位度法[25,26],对2000—2016年间四川省市级生态系统健康的总体状况进行分析,并用于揭示区域生态系统健康的平衡程度。首位度主要反映了城镇发展过程中最大城市的集中程度,通常取排名前2位城市的值进行比较,首位度越大则2个城市之间的差距越大。

变异系数是衡量指标中各观测值变异程度的统计量。本文中则主要反映了各区域之间发展的平均程度,变异系数越大区域之间发展越不平衡。当需要比较2个或多个指标的变异程度时,如果度量单位与平均数相同,可以直接利用标准差来比较。变异系数Cv的计算公式为:

Cv=σ/u ,

式中: σ为各地区生态系统健康得分的标准差; u为各地区生态系统健康得分的平均值。

2.2.2 区域生态系统健康的局部演化特征

本文中生态系统健康状况在空间中的迁移过程可以利用重力模型进行分析[27]。设大区域由n个小区域组成,而(Xi,Yi)为第i个小区域的重心坐标,Hi为其属性值,则大区域的重心坐标( X̅, Y̅)为:

X̅=( ∑i=1nHiXi)/∑i=1nHi ,
Y̅=( ∑i=1nHiYi)/∑i=1nHi 。

标准差椭圆由旋转角θ、沿x轴的标准差δx和沿y轴的标准差δy这3个要素组成。θ可以看作是笛卡尔坐标系下x轴和y轴按照点集分布的地理方位沿一定角度旋转后,正北方向与顺时针旋转的主轴之间的夹角[28]。各要素计算公式为:

tanθ= ∑i=1nHi'2Xi'2-∑i=1nHi2Yi'2+∑i=1nHi2Xi'2-∑i=1nHi2Yi'22+4∑i=1nHi2Xi'2Yi'222∑i=1nHi2Xi'2Yi'2,
δx= ∑i=1nHiXi'cosθ-HiYi'sinθ2∑i=1nHi2,
δy= ∑i=1nHiXi'sinθ-HiYi'cosθ2∑i=1nHi2,

式中 Xi'和 Yi'分别为各点距离区域重心的相对坐标, Xi'= X̅- Xi2, Yi'= Y̅- Yi2。

2.3 县级指标数据获取方法

县级评价能更加细致地对区域生态系统健康状况进行评价,为了使评价结果之间的关联性更强,评价的指标应该确保统一。但农业机械总动力和农林牧渔业总产值县级的数据不够完整,需要运用数学方法进行推算。

农业机械总动力是指用于第一产业的所有动力机械的动力的总和。农林牧渔业总产值反映了一定时期内农业生产的规模。县级统计数据获取是在市级数据的基础上通过已有的该指标数据与土地利用类型和其他社会经济农业数据的相关性分析,建立线性回归方程实现的,即

Y=a1x1+a2x2+…+amxm ,
YX=(Y·YS)/∑i=1nY ,

式中: Y为推算农业机械总动力和农林牧渔业总产值的县级指标系数; ai(i=1,2,…,m)为系数; xm为第m个与该指标极其相关的推算因子,例如,Y表示农业机械总动力时,通过与已有数据的相关性分析,得相关因子分别为不透水面面积、耕地面积、荒地面积和第一产业产值; YX为推算的某指标各个县的值; YS为各个市的实际值; n为该市的县级行政区的个数。

将实际数据与推算出的数据的散点图进行拟合的情况见表2。虽然农业机械总动力拟合精度偏低,但是由于数据的特殊性,只有约23.5%的县的数据缺失。部分缺失的县的数据可以用市级数据直接准确推算出。

表2   推算数据与原始数据拟合结果

Tab.2  Fitting results between calculated data and original data

指标拟合方程决定系数R2
农业机械总动力y = 0.787x+5.2500.82
农林牧渔总产值y = 0.989x+0.3490.98

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3 结果与讨论

3.1 市级生态系统健康的整体特征

四川省市级生态系统健康的总体趋势如图2所示。结果表明,整体上看2000—2016年期间四川省的生态系统健康总体在上升,在2000年整个研究区平均健康评分为0.610,而2016年提高到了0.687。压力得分整体变化不大,整个研究时间段内的得分都在0.72~0.73内变化。另外,研究区的状态和响应得分平均下来分别以每年0.009和0.008的速度增加。状态得分除在2001年和2005年有所下降外,整体趋势是一直在增长。响应得分在整个研究时间段内一直在增长,在2007年之前增长较为缓慢,2007年以后增长速率持续加快。

图2

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图2   2000—2016年四川省生态系统健康状况

Fig.2   Ecosystem health in Sichuan Province from 2000 to 2016


图3显示了生态系统健康的首位度在2005年以后呈现出增长的趋势,而在2005年之前呈现下降趋势。2005年以后,首位度平均每年增长0.001 5,且2011—2012年间增长最快,2012年的首位度较2011年增长了56.3%。变异系数在2005年之前略有下降,之后整体上也呈现平稳向上增长的状态,个别年份有所波动。

图3

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图3   生态系统首位度和变异系数变化

Fig.3   Change of ecosystem primacy and coefficient of variation


从图2中可以看出,2000—2016年间,四川省整体的生态系统的压力得分变化不大,状态和响应得分持续增长。进入21世纪后,西部大开发政策为四川引入了大量的国有资本投资,促进了地区经济发展,使得状态得分和反应得分都取得了显著进步。因此,即使整体的压力得分变化不大,四川省生态系统的综合健康得分也稳步增加。另一方面,由于2006 年《四川省“十一五”规划纲要(草案)》的出台,规划重点将成都发展成为带动四川经济发展的龙头[29]。这使得成都市在2006年以后迅速拉开与其他城市之间的差距,首位度值大幅提升(图3),区域之间的发展越来越不平均(变异系数不断增大)。

3.2 市级生态健康的空间格局演化

通过计算2000—2016年间各市级区域的压力得分、状态得分、响应得分和健康得分,采用自然断点法[30]将数据分为5类。如图4所示,压力得分在整个研究区域的空间分布为东部、南部较高,西北部得分较低,绵阳、南充、达州和凉山彝族自治州的压力得分最高; 状态得分也主要表现为东部高西部低的状况,成都、德阳和攀枝花得分较高,甘孜藏族自治州和阿坝藏族羌族自治州的得分较低; 响应得分没有明显的空间分布特征,除个别地区(内江、自贡、遂宁、广安和巴中)得分较低外,整体得分较好。

图4

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图4   四川省市级生态系统健康评价压力,状态和响应得分

Fig.4   City ecosystem health assessment pressure, state and response scores in Sichuan Province


参考前人的相关研究[31,32]把市级生态系统健康状况评价结果分为病态、不健康、临界健康、良好和健康5类(图5)。从图5中可以看出,2000年四川省内“不健康”等级和“病态”等级的城市共有7个,到2016年,“不健康”等级和“病态”等级的城市数量有5个,下降了28.6%。总体上来讲,区域内的生态健康状况呈上升趋势,成都地区一直处于健康状态,对比其他地区差距明显。但是,也应该注意到个别城市(如遂宁、德阳)的评分出现下降的状况。

图5

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图5   2000年与2016年四川省市级生态系统健康状况

Fig.5   Ecosystem health status of Sichuan municipal level in 2000 and 2016


综合图4—5,压力、状态和综合健康分值基本显示四川东部优于西部。由于恶劣的自然地理环境[33],西部区域生态系统压力得分和状态得分较低。四川盆地地势相对平坦,气候宜人有利于社会经济的发展。但由于巨大的人口压力和飞速的经济发展,该地区的生态系统面临着许多生态环境问题。所以盆地内虽然自然条件较好,但部分地区压力得分和状态得分偏低。社会应对能力和对环境变化的关注程度决定了响应得分[9]。政策会给予重点城市和一些受到关注的城市更多的支持,这些区域的响应得分会更高。四川西部地区一直是社会重点关注的区域,所以其响应得分与东部地区差距不大。

图6为四川省生态系统健康标准差椭圆及重心分布。

图6

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图6   生态系统健康标准差椭圆及重心分布

Fig.6   Standard deviation ellipse and center of gravity distribution of ecosystem health


由图6可知,四川省生态系统健康重心位于资阳市(简阳市、雁江区)与眉山市(仁寿县)交界处,并在一定范围内波动(简阳市在2016年5月由成都市代管,而本文研究时间为2000—2016年,所以文中按照资阳市进行描述)。状态得分整体重心大致向西南方偏移,而压力得分则没有明显的变化趋势。响应得分的重心逆时针向西北方向移动了3 km左右。图6(e)显示每年的标准偏差椭圆都以该年的重心为中心,标准差椭圆呈NE-SW分布,西部比其他地区变化更为明显。

3.3 县级生态系统健康

首先运用降尺度模型[34]推算出2016年的县级数据,进行标准化处理,并计算权重。研究区内共有183个县级行政区划单位,考虑到县(区)个数较多,为了针对每个县得出更细化的结果,避免由于类别较少造成各个县对比不明显,故将县级生态系统的评价结果分为6类(图7)。如图7所示,研究区生态系统压力得分在0.61~0.93之间。压力得分最高的县有8个,占比4.4%,最差的县有29个,占比15.8%。四川省由SE向NW压力得分呈逐渐降低的趋势。状态得分在0.58~0.90之间,空间上从NW向SE递增。状态得分最高的县有13个,占比7.1%,近2/3的区县得分都在0.68以上,总体状态得分较好。响应得分在0.45~0.66之间,响应得分整体都比较高,得分最低的区县有16个,占比8.7%。绝大部分地区的响应得分都较好,空间分布上没有明显的规律,得分较高的区县在川东和川西都有分布。

图7

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图7   县级生态系统压力、状态和响应得分

Fig.7   Scores of pressure, state and response of county-level ecosystem


结合前人的研究结果[31,32] 和实际状况,在市级评价等级的基础上,加上“正常”等级,用以细化区分各个县之间的差距。由图8可知,四川东部盆地内的各县健康状况普遍较好,大部分健康等级都在“正常”以上。评价等级为“健康”的区县共有8个,占总数的4.4%。四川西部山区得分普遍不高,绝大多数的区县得分都在“临界健康”等级以下。

图8

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图8   县级生态系统健康状况

Fig.8   Ecosystem health status at county level


由于丰富的自然资源和良好的经济基础,成都平原的区县基本保持着较为健康的经济生态环境状态。虽然这些区域面临着密集的人口、过度的资源消耗和环境污染等问题,但整体上健康状况较好(图8)。与之相反,四川西部地区虽然在人口、资源消耗和环境污染等方面的压力较小,但该地区的生态系统较为脆弱,自然环境恶劣,导致其整体的得分偏低。总的来说,虽然自然环境较差的地区会受到较多的社会关注,但是由于自身生态的脆弱,整体生态系统得分一直不高; 四川东部地区虽然面临人口密集、环境污染和资源消耗等压力,但由于自身良好的自然环境,整体的生态系统健康状况依然较好。

4 结论

1)对2000—2016年间四川省区域生态系统健康状况的时空格局进行了深入分析,生态系统健康整体得分在16 a间从0.610提高至0.687,得分年平均增长0.005左右,大部分地区生态系统状况随时间推移越来越健康。

2)对区域首位度和变异系数的研究分析表明,在2005年以后各四川省地区间的差距在逐渐扩大,成都市的领先优势越来越明显,各市(州)之间的发展不平衡性在逐渐扩大。

3)四川省区域生态系统健康重心一直位于资阳市与眉山市交界处,东部的生态健康状况优于西部地区,西部地区恶劣的自然环境导致了生态环境脆弱,经济发展落后。

4)从全省县级评价结果来看,评价等级为“健康”的地区主要集中在东部; 而西部甘孜藏族自治州和阿坝藏族羌族自治州各区县的整体生态系统健康状况都较差,这些地区的生态系统健康状况需要重点的关注和保护。

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In order to reveal the response of mountain ecosystem to climate change, the spatial-temporal distribution of vegetation variation in the Changbai Mountains was investigated by using the 10-day SPOT/VGT NDVI data from 2000 to 2009. Combining the meteorological data, we discussed the relationship between NDVI and climatic factors and time lags of vegetation variation response to climate change. The results are shown as follows. 1) NDVI increased from 2000 to 2009 in Changbai Mountains. The NDVI increased and decreased area covered about 83.91% and 16.09% of the whole study area respectively. The increased area was mainly distributed on the northern and western slopes, while the decreased area was distributed on the southern slope. The growth rate of NDVI centralized 0 - 0.006 /a. 2) The change rate of NDVI varied by seasons and vegetation types. The peak of NDVI slope appeared in May and September, but no increase, even a little decrease was observed in July; 3) There was a significantly positive correlation between NDVI and climatic factors (temperature and precipitation), and NDVI had a closer correlation with temperature than with precipitation for the three vegetation types. The results also revealed that a correlation between NDVI and temperature in tundra zone was stronger than that in the Korean pine-broadleaved mixed forest (700-1100 m) and coniferous forest (1100-1700 m), which indicated that vegetation at higher elevation is more sensitive to temperature change; 4) The correspondence between NDVI and climatic factors had a marked time lag for 10-20 days for the whole study area. Different vegetation types had different time lags. The response of NDVI in tundra zone to climatic factors had a time lag of about 10 days, while in the two forests mentioned above, the response had a time lag of about 20 days.

汪佳程.

空间经济学视角下成都经济区经济引力模型的构建与运用

[D]. 成都:西南交通大学, 2018.

[本文引用: 1]

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The construction and application of economic gravity model in Chengdu economic zone from the perspective of spatial economics

[D]. Chengdu:Southwest Jiaotong University, 2018.

[本文引用: 1]

Raskie J.

Geospatial analysis of fisheries to improve federal enforcement

[D]. Durham:Duke Vniversity, 2013.

[本文引用: 1]

何新, 姜广辉, 张瑞娟, 等.

基于PSR模型的土地生态系统健康时空变化分析——以北京市平谷区为例

[J]. 自然资源学报, 2015,30(12):2057-2068.

[本文引用: 2]

He X, Jiang G H, Zhang R J, et al.

Temporal and spatial variation of land ecosystem health based on the pressure-state-response model:A case study of Pinggu District,Beijing

[J]. Journal of Natural Resources, 2015,30(12):2057-2068.

[本文引用: 2]

洪惠坤, 廖和平, 魏朝富, 等.

基于改进TOPSIS方法的三峡库区生态敏感区土地利用系统健康评价

[J]. 生态学报, 2015,35(24):8016-8027.

DOI:10.5846/stxb201406301353      URL     [本文引用: 2]

土地利用系统健康评价研究能够有效引导土地合理利用,协调城市发展与自然生态保护之间的矛盾。构建基于PSR 模型的土地利用系统健康评价指标体系,并采用改进TOPSIS方法对三峡库区生态敏感区的典型区域-重庆市进行实证分析。结果表明:1)研究区土地利用系统健康综合分值整体呈现T型带状分布格局,可分为四个健康等级,即健康、临界健康、不健康、病态。2)渝东北、渝东南和重庆市西南片区部分地区因其土地生态系统脆弱敏感,土地利用风险性大和生态系统稳定性差,土地生态系统呈现病态和不健康状态,属于高风险-高压力区域;重庆市主城区环线区域因其属于城市核心拓展区和人类活动频繁区域,人口压力指数和土地利用压力指数较大,土地利用风险性较小,健康度较为良好,是低风险-中度压力区域。3)PSR模型能够较好地改变现有研究主要关注自然资源环境的状况,更准确地反映土地利用系统健康的各要素之间的关系和影响土地生态系统健康的关键因素,为三峡库区生态敏感区土地利用系统健康状态起到一定的预警作用。4)以改进TOPsis方法计算土地利用系统健康指数,消除了不同指标量纲的影响,并能充分利用原始数据的信息,能充分反映各方案之间的差距,客观真实的反映实际情况。5)为保障三峡库区生态敏感区土地利用系统的健康发展,应加强土地利用规划与调整,控制人类过度开发,维持生态系统正常功能。

Hong H K, Liao H P, Wei C F, et al.

Health assessment of a land use system used in the ecologically sensitive area of the Three Gorges reservoir area,based on the improved TOPSIS Method

[J]. Acta Ecologica Sinica, 2015,35(24):8016-8027.

[本文引用: 2]

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Changes in aridity index and reference evapotranspiration over the central and eastern Tibetan Plateau in China during 1960—2012

[J]. Quaternary International, 2014,349:280-286.

DOI:10.1016/j.quaint.2014.07.030      URL     [本文引用: 1]

Based on climate data from 68 meteorological stations over the Tibetan Plateau (TP) observed by the China Meteorological Administration in 1960-2012, temporal and spatial variations in aridity index (AI) and reference evapotranspiration (ET0) were comprehensively investigated. The abrupt change and the period in AI and ET0 were characterized using a comprehensive time series analysis conducted with Mann-Kendall test and Morlet wavelet. The results indicated that the regionally averaged value of AI significantly decreased by 0.04/decade during 1960-2012 period, with the maximum observed in 1972. Similarly, the regional trend for ET0 was at the rate of -9.6 mm/decade with statistically significant at the 0.01 level. Most of these stations with positive value for AI were primarily distributed at the northern southwestern TP. Moreover, a majority of stations with low values of ET0 were substantially distributed at the central and eastern TP, and amounts of the stations with high values of ET0 were mainly located at a lower elevation. Abrupt changes of both AI and ET0 primarily happened in 1980s. The major cycles of AI and ET0 were 15 y and 17 y scale over the study period with apparent periodic oscillation characteristics, respectively, and together with other different scale cycles co-existing. The significant correlations between AI and East Asian Summer Monsoon Index (EASMI) indicated that AI over the TP was related to the EASMI. (C) 2014 Elsevier Ltd and INQUA.

Vuuren D P V, Smith S J, Riahi K.

Downscaling socioeconomic and emissions scenarios for global environmental change research:A review

[J]. Wiley Interdisciplinary Reviews Climate Change, 2010,1(3):393-404.

DOI:10.1002/wcc.50      URL     [本文引用: 1]

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