黄土丘陵区生态系统服务供需匹配研究——以兰州市为例

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刘立程¹^,³, 刘春芳^,²^,³, 王川¹^,³, 李鹏杰¹^,³

1. 西北师范大学地理与环境科学学院,兰州 730070

2. 西北师范大学社会发展与公共管理学院,兰州 730070

3. 甘肃省土地利用与综合整治工程研究中心,兰州 730070

Supply and demand matching of ecosystem services in loess hilly region: A case study of Lanzhou

LIU Licheng¹^,³, LIU Chunfang^,²^,³, WANG Chuan¹^,³, LI Pengjie¹^,³

1. College of Geography and Environmental Science, Northwest Normal University, Lanzhou 730070, China

2. College of Social Development and Public Administration, Northwest Normal University, Lanzhou 730070, China

3. Gansu Engineering Research Center of Land Utilization and Comprehension Consolidation, Lanzhou, 730070, China

通讯作者: 刘春芳(1978-), 女, 甘肃定西人, 副教授, 研究方向为城乡发展与土地利用。E-mail: liuchunfang@nwnu.edu.cn

收稿日期:2019-03-4修回日期:2019-08-1网络出版日期:2019-09-25

基金资助:

国家自然科学基金项目.41861034

Received:2019-03-4Revised:2019-08-1Online:2019-09-25

Fund supported:

National Natural Science Foundation of China.41861034

作者简介 About authors
刘立程(1994-),男,甘肃民勤人,硕士生,研究方向为土地利用与生态评价E-mail:llcnwnu@163.com。

摘要
生态系统服务的持续供给是社会和自然可持续发展的基础,人类通过对生态系统服务的消费来满足需求和提高自身福祉。研究生态系统服务的供给和人类对生态系统服务的需求与消费,分析生态系统服务的供需特征与空间权衡关系,对区域生态系统的管理和资源的有效配置具有重要意义。以兰州市为例,利用全市2017年土地覆被、气象观测和统计年鉴等多源数据,应用InVEST模型、ArcGIS和GeoDA等空间分析工具,计算了研究区产水、食物供给、碳固持和土壤保持等4项服务的供给量及需求量,并对区域内生态系统服务的供需匹配状况进行了分析与评价。结果表明：① 兰州市各项生态系统服务的供给与需求空间异质性显著,各项服务总供给量均大于总需求量,且在不同区域与不同生态系统服务之间存在明显差异;② 兰州市综合生态系统服务供需比为0.039,不同生态系统服务供需匹配状况存在差异,产水服务（0.098）>碳固持服务（0.066）>食物供给服务（0.030）>土壤保持服务（0.001）,且城乡供需匹配差异显著;③ 兰州市生态系统服务供需空间匹配有“高高型空间匹配”、“低低型空间匹配”、“高低型空间错位”和“低高型空间错位”4种类型,且各项服务的主导空间匹配类型有所不同;④ 兰州市各项生态系统服务的供需平衡状况存在明显的协同作用,分别是“高高协同”与“低低协同”。
关键词： 黄土丘陵区;生态系统服务;供需匹配;空间差异

Abstract

The sustainable supply of ecosystem services is the basis of the sustainable development. Human beings satisfy the demand and improve their own well-being through the consumption of ecosystem services. It is of great significance for the management of regional ecosystems and the effective allocation of resources to study the demand and consumption of ecosystem services and to analyze the supply and demand characteristics of ecosystem services and their spatial trade-offs. Based on the multi-source data such as land cover, meteorological observation and statistical yearbook in Lanzhou in 2017, the spatial analysis tools including InVEST model, ArcGIS and GeoDA were used to calculate the supply and demand of water yield, food supply, carbon storage and soil conservation. The supply and demand matching of ecosystem services in the study area was analyzed and evaluated. The results show that: (1) The supply and demand of ecosystem services have obvious spatial heterogeneity. The total supply of services is greater than the total demand, but there are differences between different sub-regions and different kinds of ecosystem services. (2) The supply-demand ratio of comprehensive ecosystem services is 0.039. There are differences in supply-demand matching of different kinds of ecosystem services: water yield service (0.098) > carbon storage service (0.066) > food supply service (0.030) > soil conservation service (0.001), and there are significant differences in supply-demand matching between urban and rural areas. (3) There are different kinds of spatial matching pattern between supply and demand of ecosystem services, including "high-high spatial matching", "low-low spatial matching", "high-low spatial dislocation" and "low-high spatial dislocation". (4) There are obvious synergic effects in the balance of supply and demand of services in Lanzhou, which are "high-high synergy" and "low-low synergy".

Keywords：loess hilly region;ecosystem services;supply and demand matching;spatial differentiation

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本文引用格式
刘立程, 刘春芳, 王川, 李鹏杰. 黄土丘陵区生态系统服务供需匹配研究——以兰州市为例. 地理学报[J], 2019, 74(9): 1921-1937 doi:10.11821/dlxb201909016
LIU Licheng. Supply and demand matching of ecosystem services in loess hilly region: A case study of Lanzhou. Acta Geographica Sinice[J], 2019, 74(9): 1921-1937 doi:10.11821/dlxb201909016

1 引言

快速的工业化和城镇化促使城乡土地利用空间不断转型与重构^[1,2],进而对区域生态环境产生较大冲击并影响着区域生态系统服务供需平衡。生态系统服务是指生态系统所形成和维持的人类赖以生存和发展的环境条件与效用^[3],为人类直接或间接从生态系统得到的所有收益^[4]。生态系统服务供给指生态系统为人类生产产品与服务,需求则是人类对生态系统生产的产品与服务的消费与使用,两者共同构成生态系统服务从自然生态系统流向人类社会系统的动态过程^[5]。区域生态环境问题主要源自城市化和土地利用变化对区域生态系统结构和格局改变,其实质是区域生态系统服务供需关系的空间差异或失衡所致^[6]。因此,研究区域生态系统服务供需状况及数量空间匹配,是制定区域可持续发展战略的前提,也是进行生态系统管理和自然资源有效配置的一项不可或缺的基础性工作,具有重要的学术价值与现实意义。

国外的生态系统服务供需起始于20世纪90年代的生态承载力^[7,8]与生态系统服务的货币价值研究^{[4, 9-10]},早期的生态系统服务研究主要关注的是生态系统服务的结构、功能以及供需概念的界定与研究框架的完善^[11,12]。2000年以来,国外****对生态系统服务供需开展了大量基础研究和应用研究,包括生态系统服务类型及时间、空间和可逆性权衡与协同关系^[13,14],有大量研究集中关注对自然资源的消耗和偏向供给与需求的负荷关系定量化研究^[15],如生态系统服务供需量化^{[16,17,18,19]}、不同时间和空间尺度上的的生态系统服务供需平衡^[20,21]和供需的动态关系^[22,23,24]等方面。近几年来,对生态系统服务供需的量化与空间制图研究日益成为生态系统服务研究的前沿与热点领域^{[19, 25-26]}。目前,由Burkhard提出的半定量半定性的生态系统服务供需矩阵研究方法由于操作简便,适用性强而被广泛的应用在欧洲和北美洲的生态系统服务供需研究中。国外当前的生态系统服务供需研究多以土地利用估计^{[24, 27-28]}、生态过程模拟^[29,30]、数据空间叠置^{[27, 31-32]}、专家经验判别^[33,34,35]、InVEST模型^[36]、ARIES模型^[37,38]等方法为支撑,评估全球和区域尺度上的支持、调节、供给和文化服务的供需状况。国内对生态系统服务供需的研究尚处于起步阶段,主要集中在对供需理论的研究^[39,40,41],亦有部分****对生态系统服务供需进行了案例研究^[42,43,44]与成果应用^{[6, 45]}。综上所述,以往研究受限于理论发展的不完善、研究方法的不健全以及高精度数据的难获取性,多侧重于对供给、调节、支持和文化服务等的综合研究,对具体服务的关注较少;多以案例分析生态系统服务的数量供需关系,而从数量匹配和空间匹配角度的生态系统服务供需研究鲜见;使用生态系统服务供需评估矩阵、生态价值当量等半定量的研究较多,使用模型定量计算供需的研究较少;对经济发达地区的研究较多,对经济落后、生态脆弱地区的研究较少。因此对经济落后的生态脆弱区开展生态服务数量上的供需均衡关系和空间上的关联格局研究,可以为生态系统管理和资源合理有效配置提供决策辅助,对人与自然和谐耦合发展具有重要意义^[15]。

黄土丘陵区作为中国典型的生态脆弱区与集中连片贫困区,地形破碎,干旱少雨,具有保护生态环境与发展社会经济的迫切需要^[1]。因此,本文以地处黄土丘陵区的兰州市为例,选择产水、碳固持、食物供给和土壤保持四项生态服务,识别区域内各项生态系统服务的供给与需求区域,明晰生态系统服务的供需结构与供需空间,说明生态系统服务供需的数量及空间匹配关系,进而揭示生态系统服务的供需差异。这不仅可以丰富生态系统服务相关研究,还可以为生态系统服务付费、生态补偿、优化区域生态服务供需格局、构建西北生态安全屏障等规划管理提供科学依据与理论支撑。

2 数据来源与研究方法

2.1 研究区概况

兰州市位于102°35' E~104°34' E、35°34' N~37°07' N,地处黄土高原西部边缘和青藏高原的交接地带,是中国陆域版图的几何中心。气候属于中温带大陆性气候,年均降水量324 mm,年均蒸发量1676 mm,年均气温10.3 ℃,年均日照时数2447 h,无霜期180 d。总体地势西北高,东南低,海拔1418~3677 m,地貌景观多样,植被群体种类繁多,主要的生态系统类型以草地、耕地和林地为主。黄河自西南向东北横穿全境,切穿山岭,形成峡谷与盆地相间的串珠形河谷。市区南北群山对峙,中心海拔1520 m,南北两山相对高度600 m,形成东西长约35 km,南北宽2~8 km的带状哑铃形河谷盆地。兰州市现辖3县5区。全市土地总面积13085.6 km²,2017年末全市常住人口372.96万人。

2.2 数据来源

研究数据分别为：① 兰州市2017年土地覆被数据来源于美国地质调查局（USGS）LandsatTM 遥感数据（130-131/34-35）,进行解译并参考Google Earth和兰州市土地变更调查数据进行精度判别,总体分类精度为87.58%;② 兰州市周边10个气象站点的降雨、辐射与温度数据来源于中国气象数据网http://data.cma.cn/;③ 兰州市土壤深度及砂粒、粘粒、粉粒和有机质含量百分比数据来源于兰州市第二次土壤普查报告与基于世界土壤数据库（HWSD）的中国土壤数据集;④ 兰州市DEM数据来源于ASTER Global Digital Elevation Model（ASTER GDEM）数据,空间分辨率30 m,来源网址为http://gdem.ersdac.jspacesystems.or.jp/;⑤ 兰州市NDVI数据来源于美国地质调查局（USGS）MODIS13Q1产品,来源网址为https://lpdaac.usgs.gov/;⑥ 兰州市碳密度参数参考刘文辉等的研究^[46];⑦ 兰州市2017年各乡镇人口数据、兰州市食物产量数据、兰州市能源消耗数据和兰州市水资源消耗数据来源于兰州市各县区统计年鉴、兰州市统计年鉴与甘肃省水资源公报。基于统计人口数据,参考闫庆武等^[47]关于将人口数据空间化的方法,最终得到栅格格式的人口密度数据。同时将气象、土壤、植被、人口密度等栅格数据均统一使用Grid格式,栅格大小重采样为100 m,地理坐标系统一采用WGS_1984_Albers。

图1

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图1研究区位置

Fig. 1The location of the research area

2.3 生态系统服务供需评估

2.3.1 产水服务产水服务是指生态系统从降雨中拦截或储存水资源的能力,同时减轻地表径流。黄土丘陵区干旱少雨,因此评估产水服务的供需对黄土丘陵区的水资源合理利用与保护具有十分重要的现实意义。本文采用InVEST模型量化兰州市的产水服务供应量。以人类消耗的生态系统服务数量作为产水服务的需求量,即耗水量。根据《甘肃省2017年水资源公报》中兰州市2017年工业、农业、生活耗水和兰州市2017年常住人口数据得到兰州市2017年人均耗水量,再结合栅格格式的人口密度数据得到兰州市2017年产水服务的需求量图。公式如下：

（1）

供给 : S_{wp} = (1 - \frac{AE T_{xj}}{P_{x}}) \times P_{x}

（2）

\frac{AE T_{(x)}}{P_{(x)}} = 1 + \frac{PE T_{(x)}}{P_{(x)}} - {[1 + {(\frac{PE T_{(x)}}{P_{(x)}})}^{w}]}^{1 / w}

（3）

PE T_{(x)} = K_{C (x)} \times E T_{O (x)}

（4）

W_{(x)} = \frac{AW C_{(x)} \times Z}{P_{(x)}} + 1.25

（5）

需求 : D_{wp} = D_{pcwc} \times ρ_{pop}

式中：S_wp为2017年产水量（mm）;AET_（_x_）为栅格单元的年实际蒸散发量（mm）;P_（_x_）为栅格单元的年降水量（mm）;PET_（_x_）为栅格单元x的潜在蒸散量;Kc_（_x_）为作物蒸散发系数;ETO_（_x_）为参考（作物）蒸散量;AWC_（_x_）为植物可利用含水量;W_（_x_）为经验参数;Z为Zhang系数^[48];Dwp是需水量（m³）;D_pcwc是兰州市2017年人均耗水量;ρ_pop是栅格人口密度（人·km^-2）。

2.3.2 食物供给服务食物供给服务是农业生态系统中较为重要的一项服务,对人类的生存和区域的发展起着至关重要的作用。研究表明农作物和畜产品产量与NDVI之间具有显著的线性关系。基于土地利用/覆被类型,将粮食、油料和蔬菜的总产量按照栅格NDVI值与耕地总NDVI值比值来分配,肉类和奶类的产量按照栅格NDVI值与草地总NDVI值比值来分配,水产品产量按照栅格NDVI值与水域总NDVI值比值来分配,进而表征各栅格的食物供给能力,并由此得到兰州市2017年食物供给服务的供给量。食物需求量采用人均食物需求量乘以人口密度的方法估算,其中人均粮食需求量采用国家统计局（http://www.stats.gov.cn/）公布的甘肃省人均食物需求标准计算,包括的食物类型和重量（表1）。计算公式如下：

（6）

供给 : G_{i} = G_{sum} \times \frac{NDV I_{i}}{NDV I_{sum}}

（7）

需求 : D_{fp} = D_{pcfc} \times ρ_{pop}

式中：G_i为i栅格分配的粮食、肉类、奶类和水产品的产量;G_sum为兰州市粮食总产量、肉类、奶类和水产品总产量;NDVI_i为i栅格的归一化植被指数;NDVI_sum为兰州市耕地、草地或水域的NDVI值之和;D_fp为食物需求量（t）;D_pcfc为人均食物需求量（kg）;ρ_pop为栅格人口密度（人·km^-2）。

Tab. 1
表1
表1兰州市人均食物需求量
Tab. 1Food demand per capita in Lanzhou

食物类型	粮食	油类	蔬菜	肉类	水产品	蛋	奶	瓜果	总计
重量(kg)	151	8.5	75	17	2.2	7.3	14	55	330

2.3.3 碳固持服务碳固持服务是生态系统中一项重要的调节服务,使用InVEST模型中的Carbon Storage（碳固持）模块评估兰州市2017碳储量作为碳固持服务的供给量。以兰州市2017年人均碳排放量作为碳固持服务的需求量。基于兰州市2017年能源消耗总量,乘以碳排放系数得到兰州市2017年碳排放总量,再除以兰州市2017年常住人口得到兰州市2017年人均碳排放量,最后结合栅格化的人口密度数据,得到兰州市碳固持服务的需求空间分布图。公式如下：

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（8）

供给 : C_{tot} = C_{above} + C_{below} + C_{soil} + C_{dead}

（9）

需求 : D_{cp} = D_{pcfc} \times ρ_{pop}

式中：

C_{tot}

为总碳储量（t·hm^-2）,

C_{above}

为地上生物碳（t·hm^-2）,

C_{below}

地下生物碳（t·hm^-2）,

C_{soil}

为土壤有机碳（t·hm^-2）,

C_{dead}

为死亡有机物（t·hm^-2）;D_cp为碳固持需求量（t）;D_pccc为人均碳排放量（t）;ρ_pop为栅格人口密度（人·km^-2）。

2.3.4 土壤保持服务土壤保持是生态系统服务与功能的重要组成。由于地处黄土丘陵地区,兰州市长期以来遭受着较为严重的土壤侵蚀,降雨侵蚀和径流侵蚀是黄土丘陵区土壤侵蚀的两种主要侵蚀驱动形式,降雨（历时短,强度大）和下垫面条件（坡度大、黄绵土、植被覆盖差）则是该区域土壤侵蚀的主要影响因素。《兰州市第一次水利普查公报》统计结果显示,兰州市土壤侵蚀面积为4428.37 km²,主要的侵蚀类型以水力侵蚀为主。由于风力侵蚀和冻融侵蚀面积所占比例较小并且数据获取难度较大,因此本文以水力侵蚀作为土壤保持服务的评估类型。在本研究中采用土壤保持量作为土壤保持服务的供给量。以实际土壤侵蚀量作为土壤保持服务的需求量的依据是将人类期望获得生态系统服务数量即为生态服务需求量,实际的土壤侵蚀量是人类期望能够被治理的,因此以实际的土壤侵蚀量作为土壤保持服务的需求量。使用修正土壤流失通用方程（RUSLE）对区域土壤保持量与土壤侵蚀量进行估算。计算公式：

（10）

供给 : SC = RKLS - USLE = R \times K \times LS - R \times K \times LS \times C \times P

（11）

需求 : USLE = R \times K \times LS \times C \times P

式中：SC为土壤保持量;USLE为土壤侵蚀量（t·hm^-2）;R为降雨侵蚀因子;K为土壤可侵蚀因子;LS为坡长坡度因子;P为水土保持因子;C为植被覆盖因子,具体计算过程详见参考文献^{[49,50,51,52]}。

2.4 生态系统服务供需比

本文使用生态系统服务供需比（ESDR）将生态系统服务的实际供给与人类需求联系起来,可用于揭示盈余或不足的性质^[53]：

（12）

ESDR = \frac{S - D}{(S_{\max} + D_{\max}) / 2}

式中：S和D分别指特定生态系统服务的实际供给与需求;S_max是指经过评估的某项生态系统服务的供给在评价区域内的最高值,即供给的最大值;D_max是指经过评估的某项生态系统服务需求在评价区域内的最高值,即需求的最大值。ESDR正值表示某项生态服务供过于求,零值表示供需平衡,负值表示供不应求。

生态系统服务综合供需比率（CESDR）用于确定整体水平的生态系统服务供需的状态,计算为ESDR的算术平均值：

（13）

CESDR = \frac{1}{n} \overset{n}{\sum_{i = 1}} ESD R_{i}

式中：n是评估的生态系统服务的数量,在这种情况下n = 4;ESDR_i是各项生态系统类型的供需比,其中i = 1指的是产水服务,i = 2指的是食物供给服务,i = 3指的是碳固持服务,i = 4指土壤保持服务。

2.5 生态系统服务供需空间匹配

双变量局部空间自相关指数（Local Indicators of Spatial Association, LISA）可反映某一空间单元的属性值同其邻接空间单元上同一属性值的相关和空间聚集程度^[54]。应用到生态系统服务供需空间匹配的分析中,可以通过可视化LISA图揭示各项生态系统服务供需的空间匹配模式。计算公式如下：

（14）

LIS A_{i} = \frac{1}{n} \frac{(x_{i} - \overset{?}{x})}{\sum_{i} {(x_{i} - \overset{?}{x})}^{2}} \sum_{j} w_{ij} (x_{i} - \overset{?}{x})

式中：w_ij为单元i与单元j之间的空间权重矩阵;x_i为单元i的属性值;

\overset{?}{x}

为所有属性值的平均值;n为区域单元的总数。LISA值> 0,表示空间单元的服务平衡度是高—高值或低—低值的空间聚集;相反,LISA值< 0,表示空间单元的服务平衡度为高—低值或低—高值的空间聚集。

3 结果分析

3.1 生态系统服务供需特征

总体来看,兰州市各项生态系统服务的总供给均大于总需求,但在不同区域与不同生态系统服务之间又存在差异（表2、图2）。产水服务总供给量为33.56×10⁸ m³,单位面积产水量为2570.17 m³·hm^-2,单位面积产水最高的为红古区,最低的为皋兰县,其空间格局主要受区域降水分配与生态系统类型分布的影响,产水服务的需求量为4.52×10⁸ m³,单位面积需水量为343.82 m³·hm^-2,单位面积需水量最高的是城关区,最低的为皋兰县,在兰州市主城区与各县区的中心城区等人口密集,工业集聚区域的需水量明显较高。

Tab. 2
表2
表2兰州市各区县生态系统服务供需特征
Tab. 2Current situation of supply and demand of ecosystem services in Lanzhou

	产水服务(m³·hm^-2)		食物供给服务(t·km^-2)		碳固持服务(t·km^-2)		土壤保持服务(t·hm^-2)
	供给	需求	供给	需求	供给	需求	供给	需求
兰州市	2570.17	343.82	376.41	113.38	4625.65	841.37	24.53	13.19
城关区	2442.38	5903.54	79.62	1946.84	5959.09	14446.41	21.42	9.40
七里河区	2666.76	1555.42	816.52	1147.00	7034.88	3806.24	57.59	8.09
安宁区	2459.80	3228.87	113.25	1693.93	5159.55	7901.30	10.38	9.71
西固区	2617.93	587.88	270.99	209.87	5220.65	1438.59	26.94	16.79
红古区	2824.80	313.02	269.59	103.23	3992.89	766.00	15.63	18.29
榆中县	2574.14	235.00	690.80	77.50	6086.43	575.06	30.24	10.93
永登县	2733.25	169.03	301.32	55.74	3649.31	413.64	25.95	16.20
皋兰县	2054.09	111.61	115.71	36.81	4609.20	273.12	9.06	7.96

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食物供给服务的的总供给为4.72×10⁸ t,单位面积食物供给为376.41 t·km^-2,平均食物供给最高的是七里河区,最低的为城关区,各区县差异显著,供给量较高的区域主要集中在秦王川盆地、庄浪河沿岸和宛川河盆地和七里河区等耕地资源较为集中的区域,食物供给服务的需求量为1.49×10⁸ t,单位面积食物需求为113.38 t·km^-2,单位食物需求最高的为城关区,最低的是皋兰县,高需求的区域主要分布在兰州市主城区与各县区的中心城区等人口密集的区域。

碳固持服务的总供给为60.86×10⁸ t,单位面积碳固持量为4625.65 t·km^-2,平均碳固持量最高为七里河区,最低的为永登县,其空间分布主要受区域生态系统类型分布与土壤类型等因素的影响,因而呈现出较为明显的区域差异,碳固持服务的需求量为11.09×10⁸ t,单位面积碳需求量为841.37 t·km^-2,平均碳固持需求最高是城关区,最低的为皋兰县,在兰州市主城区与各县区的中心城区等区域的需求量较高。

土壤保持服务总供给为0.40×10⁸ t,单位面积土壤保持量为24.53 t·hm^-2,平均土壤保持量最高的是七里河区,最低的为皋兰县,土壤保持量较高的区域集中在榆中县的兴隆山自然保护区、七里河区的石佛沟国家森林公园以及永登县的连城国家自然保护区和奖俊埠林场等植被覆盖较好的区域,土壤保持服务的总需求量为0.21×10⁸ t,单位面积土壤保持需求量为13.19 t·hm^-2,平均土壤保持需求最高是红古区,最低的为皋兰县,需求量较高的的区域主要集中在永登县中部、榆中县北部、皋兰县东部以及七里河区等植被覆盖较差、易遭受土壤侵蚀的黄土丘陵沟壑区。

3.2 生态系统服务供需数量匹配分析

兰州市产水、食物供给、碳固持和土壤保持服务的供需比分别为0.098、0.030、0.066和0.001,生态系统服务综合供需比为0.039（表3）,说明研究区内各项生态系统服务及综合生态系统服务的供给量大于需求量。就数量匹配而言,兰州市各项生态系统服务供给充分。通过对不同区县不同生态系统服务的供需比进行分析发现,兰州市内部生态系统服务的供需分配不均衡,不同区域生态系统服务供需存在供过于求跟供不应求的状况。作为兰州市主城区的城关区和安宁区除了土壤保持服务的其他三项服务以及综合服务食物供需比均小于0,生态服务供给匮乏,出现了明显的供不应求局面;七里河区、西固区、红古区、榆中县、永登县、皋兰县6个区域的生态系统服务供需状况良好,供给充沛,其中又以榆中县为首,综合生态系统服务供需比为0.054,高出兰州市综合供需比近38%;同时除土壤保持服务外,食物供给服务、产水服务和碳固持服务的供需比呈现出显著的城乡差异,在兰州市的主城四区以及各县区的中心城区等城市地区的供需比总是较低的,呈现出供不应求的局面,而在农村地区,由于人口与工业分布较少,再加之其生态环境基底较好,因此其供需比较高,出现了供过于求的情况。

Tab. 3
表3
表3兰州市各区县生态系统服务供需比
Tab. 3ESDR in each district and county of Lanzhou

	产水服务供需比	食物供给服务供需比	碳固持服务供需比	土壤保持服务供需比	综合供需比
兰州市	0.098	0.030	0.066	0.001	0.039
城关区	-0.152	-0.215	-0.148	0.001	-0.102
七里河区	0.048	0.035	0.057	0.005	0.028
安宁区	-0.033	-0.110	-0.047	0.000	-0.038
西固区	0.089	0.009	0.066	0.001	0.033
红古区	0.111	0.020	0.057	0.000	0.037
榆中县	0.103	0.071	0.096	0.002	0.054
永登县	0.086	0.028	0.057	0.001	0.034
皋兰县	0.113	0.009	0.076	0.000	0.040

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图2

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图2兰州市各项生态系统服务供需空间分布

Fig. 2Spatial distribution of supply and demand of various ecosystem services in Lanzhou

产水服务供需比最低的是城关区（-0.152）,安宁区（-0.033）次之,其他县区的供需比均大于0,依次为皋兰县（0.113）>红古区（0.111）>榆中县（0.103）>西固区（0.089）>永登县（0.084）>七里河区（0.048）。从供需比的空间分布来看（图2）,供需比较低的区域主要集中在兰州市的城关区和安宁区等主城区、榆中县的城关镇和三角城镇、红古区的海石湾镇、永登县的庄浪河沿岸、皋兰县大部及榆中县北部黄土丘陵区。这部分地区由于受到降雨梯度分配的影响,年均降雨量较低,且这些地区是兰州市各区县主要的人口聚集区,耗水量较高,因此这些区域的产水服务供需比较低。其中需要特别说明的是,皋兰县虽然产水量较低（2054.09 m³·hm^-2）,但在兰州的三县五区中,皋兰县的需水量最低（116.61 m³·hm^-2）,所以其产水供需比最高。供需比较高的区域主要分布在永登县的西北部与榆中县的南部。由于永登县西北部西接祁连山乌鞘岭,榆中县南部为兴隆山与马啣山高寒山区,这些区域海拔较高,降雨较为充沛且年蒸发量小,且由于工业与人口分布较少,需水量不高,因而这些区域的产水供需比较高。

食物供给服务供需比最低的是城关区（-0.215）,安宁区（-0.110）次之,其他县区的供需比均大于0,依次为榆中县（0.071）>七里河区（0.035）>永登县（0.028）>红古区（0.020）>皋兰县（0.009）=西固区（0.009）。从供需比的空间分布来看（图2）,供需比较低的区域主要集中在兰州市的城关区、安宁区和西固区等主城区。这些区域由于耕地面积较少且人口集聚,食物供给低而需求高,因此这些区域供需比较低。供需比较高的区域主要分布在永登县的庄浪河与“引大入秦”工程沿岸、秦王川盆地、七里河区南部、榆中县中部宛川河盆地及其北部。这些区域是兰州市的粮食主产区,供给了兰州市大部分的食物消费,但当地的食物需求不高,食物大多流向兰州市主城区,因此这部分区域供需比较高。

碳固持服务供需比最低的是城关区（-0.148）,安宁区（-0.047）次之,其他县区的供需比均大于0,依次为榆中县（0.096）>皋兰县（0.076）>西固区（0.066）>永登县（0.057）=七里河区（0.057）=红古区（0.057）。从供需比的空间分布来看（图2）,供需比较低的区域主要分布在兰州市及各县区的主城区。主城区由于集中了区域的人口与工业,碳排放量大,且大型生态源地的缺失导致区域碳固持能力的降低,因此这些区域供需比较低。供需比较高的区域主要集中在永登县的吐鲁沟国家森林公园和奖俊埠林场、七里河区的石佛沟森林公园、榆中县的兴隆山自然保护区和北部的部分退耕还林等生态源地区。这些生态源地作为兰州的生态屏障,森林覆盖率高,碳固持能力强,因此这部分区域供需比较高。

土壤保持服务供需比最高的是七里河区（0.005）,榆中县（0.002）次之,其他县区的供需比均接近或等于0,说明土壤保持服务在兰州市全域基本保持平衡,供给略大于需求。从供需比的空间分布来看（图2）,供需比较低的区域主要分布永登县中部及榆中县北部等黄土丘陵地貌广泛发育的地区。这些区域由于土质疏松、植被覆盖度低、水土防护措施差,因而是水土流失的易发区,所以这些区域供需比较低。供需比较高的区域主要集中在永登县的吐鲁沟国家森林公园和奖俊埠林场、七里河区的石佛沟森林公园、榆中县的兴隆山自然保护区和北部的部分退耕还林等植被覆盖较好的区域。

3.3 生态系统服务供需空间匹配分析

通过对各项生态系统服务的供需进行双变量局部自相关分析,识别出兰州市各项生态系统服务的“高高型空间匹配”（高供给—高需求）、“低低型空间匹配”（低供给—低需求）、“高低型空间错位”（高供给—低需求）和“低高型空间错位”（低供给—高需求区域）四种空间匹配类型（图3）,并统计出各类型区域面积占比（图4）。

图3

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图3兰州市生态系统服务供需空间匹配图

Fig. 3Spatial matching diagram of supply and demand of ecosystem services in Lanzhou

图4

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图4空间匹配类型面积占比雷达图

Fig. 4Radar chart of space matching type area ratio

研究发现,产水服务供需的高高型空间匹配、低低型空间匹配、高低型空间错位和低高型空间错位的面积占比分别为2%、19%、21%和1%。除不显著区域之外,低低型空间匹配与高低型空间错位是产水服务供需的主导匹配类型,合计占全域面积的40%。高高型空间匹配主要分布在永登县的城关镇、红古区海石湾镇、榆中县的城关镇以及西固区的部分地区,这部分区域受降雨分配的影响,产水量相对较多而需水量又较高,因此属于高高型空间匹配;低低型空间匹配大面积的分布在永登县东部,皋兰县大部及榆中县北部,这些区域由于降雨稀少,人口分散且工业不够发达,因此属于低低型空间匹配;高低型空间错位主要分布在永登县中西部与北部、榆中县中部与南部,这部分区域产水量较高而需水量较低,因此产水服务供需出现了明显的空间错位,属于高低型空间错位;低高型空间错位集中分布在兰州市的城关区与安宁区,这一区域集中了兰州市大部分的城市人口,产水量较低而需水量最高,因此属于低高型空间错位。

食物供给服务供需的高高型空间匹配、低低型空间匹配、高低型空间错位和低高型空间错位的面积占比分别为1%、36%、4%和2%。低低型空间匹配是食物供给服务的主导匹配类型。高高型空间匹配零星分布在永登县的城关镇、红古区海石湾镇、榆中县的城关镇以及七里河区部分地区,这部分区域食物供给量与需求量均较高;低低型空间匹配分布在兰州市耕地的集中区域,包括永登县大部,皋兰县部分地区及榆中县中部和北部,这些区域是全市的耕地最为集中的区域,是全市的粮食主产区,同时这一区域人口稀少,食物需求量较低,属于低低型空间匹配;低高型空间错位主要分布在兰州市的主城区,这一区域人口密集,食物供给量低而需求量高;高低型空间错位少量分布在榆中县北部。

碳固持服务供需的高高型空间匹配、低低型空间匹配、高低型空间错位和低高型空间错位的面积占比分别为2%、21%、19%和1%。低低型空间匹配和高低型空间错位是碳固持服务的主导匹配类型。高高型空间匹配主要分布在兰州市主城区的南北两山和榆中县的兴隆山南坡,兰州市自2000年实施“兰州市南北两山绿化工程”以来,南北两山的植被覆盖率得到了较为显著地提升,碳固持能力得以加强,兴隆山南坡植被覆盖度较高,临近榆中县城关镇,碳固持能力强,这些区域碳固持量高且碳排放较高;低低型空间匹配主要分布在永登县中西部,这一区域碳固持能力弱且碳排放量低;高低型空间错位主要分布在永登县西北部与南部、榆中县南部与北部等植被覆盖较好的区域,这些区域主要集中了兰州市的自然保护区、国家森林公园以及部分退耕还林还草地区（榆中县北部）,这些区域碳固持能力强而碳排放量较低;低高型空间错位主要分布在兰州市主要的城市建成区,这一区域人口密集,碳固持能力弱而碳排放量高。

土壤保持服务供需的高高型空间匹配、低低型空间匹配、高低型空间错位和低高型空间错位的面积占比分别为8%、30%、5%和7%。低低型空间匹配是土壤保持服务的主导匹配类型。高高型空间匹配主要分布在永登县西部与榆中县北部,这些区域土壤保持量较高且土壤侵蚀量较高,属于高高型空间匹配;低低型空间匹配大部分的分布在兰州市东北部,城关区与安宁区、榆中县中部与北部的部分地区,这些区域由于土壤保持量较低且土壤侵蚀量较低,属于低低型空间匹配;高低型空间错位主要分布在永登县西北部、榆中县南部与七里河部分地区,这些区域由于植被覆盖较好,土壤不易流失,土壤保持量较高而土壤侵蚀量较低,属于高低型空间错位;低高型空间错位集中分布永登县中部的黄土丘陵区地带,这一区域由于降雨较多,植被覆盖较差,水土防护措施不完善,因此土壤保持量低而土壤侵蚀量高,属于低高型空间错位。

3.4 生态系统服务供需平衡权衡协同分析

基于R语言,运用cor函数与chart.Correlation函数,并结合双变量局部Moran's I指数对兰州市各项生态系统服务的供需平衡状态进行相关性（Pearson相关）可视化分析,说明兰州市各项生态系统服务供需平衡状态之间的相互影响,并探究各项生态服务供需平衡状态的权衡或协同关系。图5为兰州市生态系统服务供需比的散点矩阵图,其中主对角线是4项生态系统服务供需比的核密度曲线与分布直方图,用以说明供需比的分布特征与分布集中程度;主对角线以上是各项服务供需比的相关系数;主对角线以下部分是各项服务供需比的散点图及平滑拟合曲线。

图5

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图5兰州市生态系统服务供需比散点图矩阵

Fig. 5Scatter plot matrix of supply and demand ratio of ecosystem services in Lanzhou

从图5可以看出,4项服务供需比的分布均较为集中。在村级尺度上,兰州市产水服务与食物供给服务和碳固持服务、碳固持服务与土壤保持服务的供需比之间呈显著正相关（协同关系）,其中产水服务与碳固持服务供需比的相关性最高,相关系数为0.97,产水服务与食物供给服务、食物供给服务和碳固持服务供需比的相关性次之,相关系数分别为0.89和0.86,土壤保持服务与碳固持服务的供需比相关性最低,相关系数为0.19,均在0.01的显著性水平上显著。土壤保持服务与产水服务和食物供给服务之间供需比的相关关系不显著。这说明兰州市某项生态系统服务供需平衡状态的改变并不会导致其他生态系统服务供需平衡状态的降低,而是呈现出一荣俱荣、一损俱损的状态,即协同关系。

对兰州市4项生态服务的供需比进行双变量空间自相关分析得到表4和图6,分析结果的显著性均高于95%。由表3可知,各项服务供需比的的全局Moran's I指数由大到小依次为产水服务与碳固持服务（0.657）,产水服务与食物供给服务（0.638）,食物供给服务与碳固持服务（0.630）,土壤保持服务与碳固持服务（0.136）,土壤保持服务与产水服务和食物供给服务之间的供需比的权衡协同关系不明显。从双变量局部LISA图的空间分布来看,兰州市各项服务供需比之间的权衡协同关系总体以“高高协同”与“低低协同”为主,但土壤保持服务与其他三项服务的供需比之间也出现了部分“高低权衡”与“低高权衡”。权衡协同关系与相关分析结果（图5）基本一致。

Tab. 4
表4
表4兰州市生态系统服务供需匹配度双变量局部自相关指数
Tab. 4Bivariate Local Moran's I among supply and demand ratio of ecosystem services in Lanzhou

生态系统服务对	Moran's I指数
产水—食物供给	0.638
产水—碳固持	0.657
产水—土壤保持	0.015
食物供给—碳固持	0.630
食物供给—土壤保持	0.072
土壤保持—碳固持	0.136

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图6

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图6兰州市生态系统服务供需比局部LISA图

Fig. 6LISA cluster map between supply and demand ratio of ecosystem services in Lanzhou

4 结论与讨论

4.1 结论

以中国黄土丘陵区典型区域——甘肃省兰州市为例,采用多源数据并结合InVEST模型、ArcGIS和GeoDA软件定量评估了4项生态服务的供需特征、数量与空间匹配状况,并识别出每种服务的主导空间匹配类型,说明了各项服务供需平衡之间的权衡协同关系。主要结论如下：

（1）兰州市各项生态系统服务的供给与需求具有较为明显的空间异质性,各项服务总供给量均大于总需求量,但在不同区域与不同生态系统服务之间又存在差异。不同的生态系统服务供需空间分布又各具特色。4项生态系统服务的供给总体呈现出由兰州市西北和东南向中部递减的空间分布趋势;食物供给、产水和碳固持服务的需求总体呈现出由丘陵山区向河流谷地等人口聚居区递增分布的态势,土壤保持服务的需求则呈现出由兰州市西部和北部的丘陵沟壑区向东部和南部递减的趋势。

（2）兰州市生态系统服务总体供需匹配较好,在全域呈现出供大于求的空间分布态势。不同生态系统服务供需数量匹配状况存在差异,产水服务>碳固持服务>食物供给服务>土壤保持服务,各项生态系统服务供需比的空间异质性显著,城乡供需匹配差异明显。

（3）兰州市各项生态系统服务供需空间匹配有“高高型空间匹配”“低低型空间匹配”“高低型空间错位”和“低高型空间错位”4种空间匹配类型。各项生态系统服务供需空间匹配的主导类型亦有所差异：① 产水服务的主导空间匹配类型是“高低型空间错位”;② 食物供给服务和土壤保持服务的主导空间匹配类型是“低低型空间匹配”③ 碳固持服务的主导空间匹配类型是“高低型空间错位”和“低低型空间匹配”。

（4）兰州市各项生态服务的供需协同作用显著,又分为“高高协同”与“低低协同”,其中产水服务与碳固持服务、产水服务与食物供给服务、食物供给服务和碳固持服务供需平衡的协同程度较高,其他服务的供需平衡之间的权衡协同关系不显著。

4.2 讨论

生态系统服务与人类福祉息息相关,生态系统服务的供给取决于当地自然环境基底状况,需求则与人口增长和社会经济的发展有关。目前对生态系统服务的供给的研究已较为成熟,主要集中在对生态系统服务供给现状的评估及权衡协同关系等方面,但如何量化人类对生态系统服务的需求成为了难点。生态服务供需评价矩阵和价值当量法在一定程度上可以满足当前的研究需要,但此类半定量的方法存在着较强的主观性与不确定性,不能够完全将较小区域的生态系统服务供需空间格局清晰地刻画出来。本研究则采用人均资源消耗量或某项服务具体的物质量作为各项生态系统服务的需求量,与各项服务的供给量相对应,使服务供给和需求的研究具有统一可比的度量基础,可以较为详实的反映研究区的实际供需状况。

黄土丘陵区作为中国典型的生态脆弱敏感区,具有气候干旱、地形破碎、水土流失严重,经济欠发达等自然社会经济特点。通过与其他研究^{[42-43, 53]}的分析对比发现,相较于东部长江中下游平原等经济发达、自然条件较好的区域,黄土丘陵区的生态系统服务供需状况表现出明显的区域特色,主要有以下几个明显特征：① 由于自然资源禀赋与社会经济发展的差距,黄土丘陵区的生态服务供给与需求均明显低于东部发达地区;② 黄土丘陵区生态系统服务供需匹配水平低于东部,但供需基本平衡,供给略大于需求;③ 由于西部欠发达地区城乡二元结构的存在,因此黄土丘陵区生态服务供需的城乡差异较东部地区更为显著。

生态系统服务作为联系自然生态系统与人类社会的桥梁,其供给和需求反映生态系统和人类社会间复杂的动态关联。生态系统是一个复杂的、动态的复合系统,生态系统服务供给与需求的时空变化受生态系统结构功能、生态过程、人口增长、经济发展、社会进步、技术革新等诸多因素的影响。在研究过程中,仍存在部分数据的获取问题,如最新只获取到2017年的土地利用数据与统计数据,气象站点数量较少,这势必会影响相应的生态系统服务供需及供需平衡状态的评估。本文虽然从数量和空间上对各项生态服务的供需状况进行了研究,但仍无法充分反映生态系统服务供需平衡内部机理与驱动机制。此外,本文仅对供给和调节这两种生态服务进行分析,并未对黄土丘陵区生态系统服务供需平衡进行全面评估。因此未来研究主要应从生态系统服务供需关系的跨时间、多尺度分析、生态系统服务供需平衡驱动因素、生态系统服务流动、生态系统服务管理、生态系统服务需求与人类福祉的耦合机制拓展等方面展开。厘清生态系统服务供给和需求的相互作用机制,分析生态系统服务供需关系在自然、社会、经济影响下的变化过程,探索生态系统服务供需研究结果从理论到实际管理的应用模式,建立考虑多尺度、多重利益相关者需求的生态系统决策机制和调控方法,可以为生态系统服务调控管理和社会的可持续发展提供切实可行的科学依据。

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This paper presents a scenario-based assessment of global future food security. To do that, the socio-economic and climate change scenarios were defined for the future and were linked to an integrated modeling framework. The crop yields simulated by the GIS-based Environmental Policy Integrated Climate (EPIC) model and crop areas simulated by the crop choice decision model were combined to calculate the total food production and per capita food availability, which was used to represent the status of food availability and stability. The per capita Gross Domestic Product (GDP) simulated by IFPSIM model was used to reflect the situation of food accessibility and affordability. Based on these two indicators, the future food security status was assessed at a global scale over a period of approximately 20 years, starting from the year 2000. The results show that certain regions such as South Asia and most African countries will likely remain hotspots of food insecurity in the future as both the per capita food availability and the capacity of being able to import food will decrease between 2000 and 2020. Low food production associated with poverty is the determining factor to starvation in these regions, and more efforts are needed to combat hunger in terms of future actions. Other regions such as China, most Eastern European countries and most South American countries where there is an increase in per capita food availability or an increase in the capacity to import food between 2000 and 2020 might be able to improve their food security situation.

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Landscapes differ in their capacities to provide ecosystem goods and services, which are the benefits humans obtainfrom nature. Structures and functions of ecosystems needed to sustain the provision of ecosystem services are alteredby various human activities. In this paper, a concept for the assessment of multiple ecosystem services is proposedas a basis for discussion and further development of a respective evaluation instrument. Using quantitative andqualitative assessment data in combination with land cover and land use information originated from remote sensingand GIS, impacts of human activities can be evaluated. The results reveal typical patterns of different ecosystems鈥榗apacities to provide ecosystem services. The proposed approach thus delivers useful integrative information forenvironmental management and landscape planning, aiming at a sustainable use of services provided by nature. Theresearch concept and methodological framework presented here for discussion have initially been applied in differentcase studies and shall be developed further to provide a useful tool for the quantification and spatial modelling ofmultiple ecosystem services in different landscapes. An exemplary application of the approach dealing with foodprovision in the Halle-Leipzig region in Germany is presented. It shows typical patterns of ecosystem service distributionaround urban areas. As the approach is new and still rather general, there is great potential for improvement,especially with regard to a data-based quantification of the numerous hypotheses, which were formulated as base forthe assessment. Moreover, the integration of more detailed landscape information on different scales will be neededin future in order to take the heterogeneous distribution of landscape properties and values into account. Therefore,the purpose of this paper is to foster critical discussions on the methodological development presented here.

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Ecosystem services mapping and modeling has focused more on supply than demand, until recently. Whereas the potential provision of economic benefits from ecosystems to people is often quantified through ecological production functions, the use of and demand for ecosystem services has received less attention, as have the spatial flows of services from ecosystems to people. However, new modeling approaches that map and quantify service-specific sources (ecosystem capacity to provide a service), sinks (biophysical or anthropogenic features that deplete or alter service flows), users (user locations and level of demand), and spatial flows can provide a more complete understanding of ecosystem services. Through a case study in Puget Sound, Washington State, USA, we quantify and differentiate between the theoretical or in situ provision of services, i.e., ecosystems capacity to supply services, and their actual provision when accounting for the location of beneficiaries and the spatial connections that mediate service flows between people and ecosystems. Our analysis includes five ecosystem services: carbon sequestration and storage, riverine flood regulation, sediment regulation for reservoirs, open space proximity, and scenic viewsheds. Each ecosystem service is characterized by different beneficiary groups and means of service flow. Using the ARtificial Intelligence for Ecosystem Services (ARIES) methodology we map service supply, demand, and flow, extending on simpler approaches used by past studies to map service provision and use. With the exception of the carbon sequestration service, regions that actually provided services to people, i.e., connected to beneficiaries via flow paths, amounted to 16-66% of those theoretically capable of supplying services, i.e., all ecosystems across the landscape. These results offer a more complete understanding of the spatial dynamics of ecosystem services and their effects, and may provide a sounder basis for economic valuation and policy applications than studies that consider only theoretical service provision and/or use.

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Ecosystem Services (ES) are an established conceptual framework for attributing value to the benefits that nature provides to humans. As the promise of robust ES-driven management is put to the test, shortcomings in our ability to accurately measure, map, and value ES have surfaced. On the research side, mainstream methods for ES assessment still fall short of addressing the complex, multi-scale biophysical and socioeconomic dynamics inherent in ES provision, flow, and use. On the practitioner side, application of methods remains onerous due to data and model parameterization requirements. Further, it is increasingly clear that the dominant "one model fits all" paradigm is often ill-suited to address the diversity of real-world management situations that exist across the broad spectrum of coupled human-natural systems. This article introduces an integrated ES modeling methodology, named ARIES (ARtificial Intelligence for Ecosystem Services), which aims to introduce improvements on these fronts. To improve conceptual detail and representation of ES dynamics, it adopts a uniform conceptualization of ES that gives equal emphasis to their production, flow and use by society, while keeping model complexity low enough to enable rapid and inexpensive assessment in many contexts and for multiple services. To improve fit to diverse application contexts, the methodology is assisted by model integration technologies that allow assembly of customized models from a growing model base. By using computer learning and reasoning, model structure may be specialized for each application context without requiring costly expertise. In this article we discuss the founding principles of ARIES--both its innovative aspects for ES science and as an example of a new strategy to support more accurate decision making in diverse application contexts.

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Huimin

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Shengyan

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[ 刘慧敏, 范玉龙, 丁圣彦 . 生态系统服务流研究进展
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Hao

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Qingxu

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Chunyang

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Pan

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Chunliang

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Yongqiang

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Qingwu

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Soil erosion in terrestrial ecosystems, as an important global environmental problem, significantly impacts on environmental quality and social economy. By protecting soil from wind and water erosion, terrestrial ecosystems supply human beings with soil erosion control service, one of the fundamental ecosystem services that ensure human welfare. The Loess Plateau was one of the regions in the world that suffered from severe soil erosion. In the past decades, restoration projects were implemented to improve soil erosion control in the region. The Grain-to-Green project, converting slope croplands into forest or grasslands, launched in 1999 was the most massive one. It is needed to assess the change of soil erosion control service brought about by the project. This study evaluated the land cover changes from 2000 to 2008 by satellite image interpretation. Universal Soil Loss Equation (USLE) was employed for the soil erosion control assessment for the same period with localized parameters. Soil retention calculated as potential soil erosion (erosion without vegetation cover) minus actual soil erosion was applied as indicator for soil erosion control service. The results indicate that ecosystem soil erosion control service has been improved from 2000 to 2008 as a result of vegetation restoration. Average soil retention rate (the ratio of soil retention to potential soil loss in percentage) was up to 63.3% during 2000-2008. Soil loss rate in 34% of the entire plateau decreased, 48% unchanged and 18% slightly increased. Areas suffering from intense erosion shrank and light erosion areas expanded. Zones with slope gradient of 8 degrees-35 degrees were the main contribution area of soil loss. On average, these zones produced 82% of the total soil loss with 45.5% of the total area in the Loess Plateau. Correspondingly, soil erosion control capacity was significantly improved in these zones. Soil loss rate decreased from 5000 t km(-2) yr(-1) to 3600 t km(-2) yr(-1), 6900 t km(-2) yr(-1) to 4700 t km(-2) yr(-1), and 8500 t km(-2) yr(-1) to 5500 t km(-2) yr(-1) in the zones with slope gradient of 8 degrees-15 degrees, 15 degrees-25 degrees, and 25 degrees-35 degrees respectively. However, the mean soil erosion rate in areas with slope gradient over 8 degrees was still larger than 3600 t km(-2) yr(-1), which is far beyond the tolerable erosion rate of 1000 t km(-2) yr(-1). Thus, soil erosion is still one of the top environmental problems that need more ecological restoration efforts. (C) 2011 Elsevier B.V. All rights reserved.

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Gangqiang

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Xueqiang

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