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五华河流域非点源污染风险区和风险路径识别
陈裕婵1,, 张正栋1,, 万露文2, 张杰1, 杨传训3, 叶晨1, 李青圃1
1. 华南师范大学地理科学学院,广州 510631
2. 密歇根州立大学地球与环境科学系,美国 东兰辛 48823
3. 广州地理研究所,广州 510070

作者简介:陈裕婵(1993-), 广西钦州人, 硕士研究生, 主要从事景观格局与水文过程耦合研究。E-mail: 2016022026@m.scnu.edu.cn

通讯作者:张正栋(1968-), 甘肃榆中人, 教授, 主要从事景观格局与水文过程耦合研究。E-mail: zhangzdedu@163.com
摘要

非点源污染是亟待解决的水环境问题之一,确定非点源污染过程中的潜在风险区和风险路径是解决非点源污染问题的关键。引入景观生态学中的最小累积阻力模型,以高污染负荷的耕地、建设用地为“源”,运用表示下垫面产流性质的地形湿度指数和CN值构建阻力面,对五华河流域的非点源污染高风险区和风险路径进行可视化识别和分析。结果显示:① 在下垫面产流作用下,五华河流域低产流区主要分布在流域西南部,高产流区呈“人”形贯穿全流域。② 最小累积阻力模型能够有效识别流域内非点源污染风险区和风险路径,五华河流域非点源污染高风险区分布于迥龙、田心、龙母、铁场、登云、通衢、鹤市、紫市、岐岭、华城、转水、潭下、水寨等地的河道两岸,以耕地为“源”的非点源污染风险路径与建设用地为“源”的非点源污染风险路径在空间分布上差异较大。③ 耕地对五华河水质的影响大于建设用地对五华河水质的影响,耕地中的富营养物质和沉积物更容易随地表径流进入受纳水体。④ 流域尺度上治理以耕地为“源”的非点源污染应在邻近耕地的河流两岸建立一定宽度的植被缓冲区,治理以建设用地为“源”的非点源污染宜围绕关键源区进行治理。本研究为非点源污染风险区和风险路径的识别提供一种新的思路,为进一步开展非点源污染治理提供理论依据。

关键词: 非点源污染; 风险区识别; 风险路径识别; 最小累积阻力模型; 五华河流域;
Identifying risk areas and risk paths of non-point source pollution in Wuhua River Basin
CHEN Yuchan1,, ZHANG Zhengdong1,, WAN Luwen2, ZHANG Jie1, YANG Chuanxun3, YE Chen1, LI Qingpu1
1. School of Geography, South China Normal University, Guangzhou 510631, China
2. Department of Earth and Environmental Sciences, Michigan State University, East Lansing, MI 48823, USA
3. Guangzhou Institute of Geography,Guangzhou 510070,China
Abstract

Non-point source pollution is one of the most severe problems impacting water environments. Identifying potential risk areas and risk paths contributing to non-point source pollution is the soution to this problem. This study introduces the minimum cumulative resistance model of landscape ecology, which is based on land use and soil mapping at a scale of 1∶100000 and DEM data with a resolution of 30 m. The model takes high pollution-loaded cultivated land and construction land as the main sources and uses the Topographic Wetness Index and Runoff Curve Numbers, which can describe the underlying resistance surface runoff yield characteristics, to visually identify and analyze the risk areas and risk paths of the Wuhua River Basin. The results show that underlying surface runoff production results in low-yield flow areas that are mainly concentrated in the southwest of the basin, while high-yield flow areas herringbone throughout the study area. The minimum cumulative resistance model can effectively identify the risk areas and risk paths in this basin. The high-risk areas of non-point source pollution are mainly distributed in Jionglong, Tianxin, Longmu, Tiechang, Dengyun, Tongqu, Heshi, Zishi, Qiling, Huacheng, Zhuanshui, Tanxia and Shuizai, which are located along both sides of the river. The spatial distributions of the risk paths of cultivated land and construction land are significantly different. The effects of cultivated land on water quality of the river are greater than those of construction land on it, and the nutrients and sediments from cultivated land are more likely to run into the receiving water via surface runoff. Vegetation buffer zones should be set up on both sides of the river adjacent to cultivated land when we deal with non-point source pollution that originates from cultivated land, and the harnessment of non-point source pollution originating from construction land should be monitored around major source areas. This study provides a novel method for the identification of source areas and risk paths of non-point source pollution and a theoretical basis to formulate future management strategies.

Keyword: non-point source pollution; risk areas identification; risk paths identification; minimum cumulative resistance model; Wuhua River Basin;
1 引言

随着点源污染得到有效控制,非点源污染对水体污染的贡献日益显著[1,2,3]。非点源污染具有随机性、模糊性、分布广泛等特点,导致其治理点位难以确定[4,5]。研究发现流域的某些小范围区域上会产生流域内绝大多数的污染负荷[6],如果这些区域还易于产生地表径流,就会使大量富营养物质和沉积物随地表径流快速迁移进入受纳水体,从而对水体造成严重污染,这部分对水体污染有决定性作用的区域被定义为关键源区(Critical Source Areas, CSAs)[7]。关键源区是严重威胁水体安全的风险区,治理关键源区能以最小成本达到最大程度遏制非点源污染的目的,这成为当前治理非点源污染的重要思路之一[8,9,10]。以关键源区为代表的非点源污染风险区的识别有效划定了非点源污染治理的面状范围,但在非点源污染发生的过程中,富营养物质和沉积物随地表径流发生迁移是呈线状流动的。要进一步精确判断非点源污染的治理地点和治理方式,就必须探究富营养物质随径流迁移形成的线状风险路径。目前,对于非点源污染风险区识别的研究较多[11,12,13],却缺乏非点源污染风险路径识别的研究。

为此本文引入景观生态学中的最小累积阻力模型,用以识别流域非点源污染风险区及污染物迁移的风险路径。最小累积阻力模型是基于“源”和阻力表面研究物质可达性的一种方法,能有效提取区域内的线状生态流,在识别构建线状生态廊道方面得到了广泛运用[14,15,16,17]。近年来最小累积阻力模型逐渐被用于非点源污染研究中,如Wang等用相对高程、相对坡度等7个自然影响因子构建阻力基面,运用最小累积阻力模型对三峡库区的耕地面源污染源—汇风险格局进行了识别[18];付永虎根据不同土地利用类型对营养物质的迁移能力构建阻力面,用最小累积模型划设非点源污染重点阻控区,从而实现对非点源污染控制[19]。但是现有研究仍然局限在面状非点源风险区的识别上[20,21],缺乏线状风险路径识别研究。

1978年以来,五华河流域在社会经济飞速发展的同时,水环境问题日益突出,非点源污染成为亟待解决的严峻问题之一。本文以五华河流域为例,以流域下垫面产流性质构建阻力表面,基于最小累积阻力模型对五华河流域的非点源污染高风险区和风险路径进行识别和分析,以期为非点源污染风险区和风险路径的识别提供一种新的思路,为进一步开展非点源污染治理提供理论依据。

2 研究区概况与数据来源
2.1 研究区域

五华河为韩江一级支流,发源于广东省龙川县回龙乡亚鸡寨,经龙川县与桥头水河汇合后流入五华县,在岐岭镇与岐岭河汇合,流经华城镇、转水镇,在大坝镇与琴江河相汇流入梅江,是五华县境内主要河道之一(图1)。五华河全长105 km,流域面积为184335 hm2,流域内地势西南高东北低,地形以丘陵为主,为亚热带季风气候,年均温20 ℃,年平均降水量1500 mm。五华河流域土壤类型以赤红壤为主,因常年种植水稻,于河流两岸分布着较大范围的水稻土。流域内土地利用类型以林地为主,耕地沿河分布,建设用地主要分布在流域南部。随着区域内经济的迅速发展,城镇化水平的不断提高,五华河流域的环境问题也日益突出,尤其是流域水体遭受不同程度的污染,使非点源污染成为亟待解决的严峻问题之一。识别研究五华河流域的非点源污染风险区和风险路径具有十分重要的现实意义。

图1 五华河流域区位图 Fig. 1 Location of Wuhua River Basin

2.2 数据来源

研究数据包括土地利用数据、土壤数据和DEM数据。① 土地利用数据:选取2015年的土地利用类型数据,比例尺为1∶10万,来源于中国科学院的遥感解译数据。② 土壤数据:土壤类型数据为2010年广东省土壤类型数据,比例尺为1∶10万,来源于中国科学院。土壤属性数据主要来源于广东省第二次土壤普查成果《广东土种志》[22]。③ DEM数据:采用GDEM V2数字高程数据,分辨率为30 m,来源于中国科学院计算机网络信息中心地理空间数据云平台(http://www.gscloud.cn)。

3 研究方法
3.1 最小累积阻力模型

最小累积阻力模型由“源”和阻力表面构成[23]。在景观生态学“源—汇”的理论中,“源”是一个过程的源头,通过对景观类型与非点源污染负荷相关性研究发现,氮、磷污染高负荷区主要集中在农业用地和城镇用地[24,25,26,27],这两类景观是发生非点源污染的源头,即非点源污染中的“源”景观。大量的富营养物质和沉积物质随地表径流从“源”景观发生迁移,下垫面越容易产流,污染物迁移所遇下垫面阻力就越小[28]。用下垫面产流性质构建最小累积阻力模型的阻力表面,能够反映污染物从“源”到目标地的迁移力度和迁移方向。最小累积阻力模型中的“源”可以反映风险区定义中的高负荷源区,下垫面产流性质构建的阻力表面能够反映风险区定义中的污染物迁移难易程度,其中污染物迁移累积阻力最小的路径为风险路径。因此,用最小累积阻力模型来识别区域非点源污染风险区和风险路径是合理可行的。最小累积阻力模型的数学表达式为[29,30,31]

MCR = f min ( D ij × R i ) (1)

式中:MCR为最小累积阻力;f表示累积阻力值与DijRi间的函数关系,表达从源到空间某一点的相对易达性;min表示取函数的最小值;Dij是源j到景观单元i的实地距离;Ri是景观单元i对运动过程的阻力值。

3.1.1 源 在流域生态过程中,“源”斑块直接影响区域中生态流的大小[32,33],携带污染物质的地表径流作为生态流之一,大小同样受到“源”斑块的制约。如果“源”斑块面积小污染负荷不足,污染物质在迁移过程中极有可能被下垫面截留和转化,最终不会进入到受纳水体中。所以要根据流域的实际情况对“源”斑块设定面积阈值,选取大于阈值的耕地和建设用地作为“源”。结合已有的研究[34,35]和五华河流域的实际情况,研究提取流域中所有大于1000 hm2的耕地和大于50 hm2的建设用地为“源”。

3.1.2 阻力表面 用下垫面产流性质来构建最小累积阻力模型的阻力表面。下垫面产流性质用地形湿度指数和CN值来表示。

(1)地形湿度指数

地形湿度指数能够准确反映地形变化对地表径流的影响,其大小由区域的坡面汇流面积和水力坡降两要素决定,地形湿度指数越大表示坡面汇流面积越大、水力坡降越 低[36],则该区域的饱和带发展潜力越大,土壤就越容易达到饱和而产生地表径流。地形湿度指数的表达式为[37,38]

TWI = ln α tan β (2)

式中:TWI为地形湿度指数;α为等高线长度的汇流面积;β为该点的坡度。对于以栅格形式DEM表示的流域,α为网格单元汇水面积与DEM栅格尺寸的比值;β为对单元网格起作用的局部坡度角。

(2)CN值

CN值是表示土地利用类型和土壤类型对地表产流影响的函数,可以较好的反映下垫面土地利用类型和土壤类型差异对汇流过程的影响。CN值越大,径流深越大,降雨量转为径流量越多[39]。美国农业部土壤保持局总结了常见的土地利用类型和土壤水文分组条件下的CN值,对CN表进行查找即可得到相应的CN值(表1)。

表1 五华河流域土地利用类型及相应的CN值 Tab. 1 CN value of each landuse type in Wuhua River Basin

但国内缺乏相应的土壤水文分组数据,需要使用美国农业部开发的SPAW软件对现有的土壤质地数据进行处理。SPAW中输入的土壤粒径数据采用的是美国制标准,而中国第二次土壤普查采用的是国际制标准,因此必须将国际制标准土壤粒径转为美制标准土壤粒径。两种标准土壤粒径的差异如表2所示。

表2 国际制和美制土壤粒径对比 Tab. 2 Contrast of International and USA standard in soil texture systems

在进行土壤质地转换时,三次样条插值的结果相对误差最小[40]。基于Matlab平台,对研究区的土壤质地 进行三次样条插值转换。将转换后的各土壤类型中的Sand和Clay的百分比按要求依次输入到SPAW软件中,得到各土壤的饱和导水率,根据陈腊娇[41]和贺维[42]所修订的水文分组标准(表3),最终获得土壤水文分组数据。

表3 水文分组标准及流域土壤水文分组 Tab. 3 Hydrologic groups criteria and soil classification in Wuhua River Basin
3.2 非点源污染风险区和风险路径识别方法

本文提取五华河流域中所有大于1000 hm2的耕地和大于50 hm2的建设用地为“源”,将TWI栅格数据和CN值栅格数据进行叠加作为阻力表面,基于ArcGIS的cost weight工具和cost path工具分别获得污染物从“源”到河道的累积阻力距离和最小累积阻力路径,由此得到流域非点源污染的风险区和风险路径。

4 结果分析
4.1 下垫面对地表产流影响

本文选取坡度、土地利用类型和土壤类型3个要素来表征五华河流域下垫面的基本情况(图2)。从图2看出,五华河流域下垫面条件较为复杂。从地形上看(图2a),五华河流域坡度大部分在0°~15°之间,少量的较陡坡(15°~25°)、陡坡(25°~35°)主要分布在流域西南侧的登云、通衢、紫市、潭下及长布一带的山地,极少量大于35°的急陡坡分布于长布镇西南地区,流域地形整体呈现东北平缓、西南陡峭的趋势。从土地利用类型上看(图2b),五华河流域土地利用类型以林地为主,林地面积共占流域面积的77%,其中有林地占流域面积的62%,灌木林地、疏林地和其他林地分别占流域面积的1%、6%和8%。耕地面积次之,约占流域面积的19%,其中水田、旱地分别占流域面积的15%、4%,耕地主要沿河道分布。草地、水域和建设用地面积较小,分别仅占流域面积的2%、1%和1%,空间分布上草地和水域在流域内呈零星状分布,建设用地主要分布在流域的南部并靠近河流。从土壤类型上看(图2c),流域的土壤类型主要以赤红壤为主,占流域面积的71%,其中赤红壤、片赤红壤、页赤红壤、麻赤红壤和侵蚀赤红壤面积分别占流域面积的5%、3%、22%、18%和23%,在通衢、潭下及其以东的地区广泛分布。水稻土、红壤面积次之,分别占流域面积的16%和9%,空间分布上水稻土靠近河流分布,红壤主要分布于登云、黄布、紫市、长布一带。页黄壤和酸性紫色土数量较少,面积仅占流域面积的3%和1%,页黄壤主要分布于黄布与鹤市边界、紫市与长布边界地区,酸性紫色土主要分布于潭下、转水、华城三镇交界处。

图2 五华河流域下垫面条件 Fig. 2 The conditions of underlying surface in Wuhua River Basin

下垫面对地表产流的影响用TWI和CN值来评价。从图3a可见,五华河流域TWI主要集中在5.09~8.05的范围内,TWI在-23.67~5.09范围内的地区较少且分布分散,TWI在8.05~44.87范围内的空间分布与河道的空间位置基本一致,呈“人”形贯穿整个流域。由图3b可见,五华河流域80%以上的区域CN值在70~77之间。CN值为55的低值区域主要分布于登云、岐岭、黄布、紫市、长布等地,为有林地、中等渗透率土壤覆盖区域。CN值大于80的区域在流域内呈小范围零散状分布,为建设用地和水域、较低渗透率土壤覆盖区域。由图3c可见,五华河流域在下垫面(地形、土地利用类型、土壤类型)的影响下产流差异较大,低产流区主要分布在流域西南部的登云、岐岭、黄布、紫市、长布一带,与CN值低的区域基本吻合,表现出林地和透水性较好的土壤类型对地表产流的强烈抑制作用,地形因素的抑制作用不明显。高产流区与地形湿度指数在8.05~44.87范围内的区域基本一致,呈“人”形分布,表现出地形因素对地表产流的强烈促进作用,土地利用、土壤因素的促进作用不明显。

图3 五华河流域下垫面性质影响下地表产流空间分布 Fig. 3 Spatial distribution of runoff yield affected by underlying surface in Wuhua River Basin

4.2 非点源污染风险区识别

按照3.2小节的方法,利用自然断点法将流域非点源污染风险区分为5级(图4)。高风险区(阻力距离为0~3195)分布于迥龙、田心、龙母、铁场、登云、通衢、鹤市、紫市、岐岭、华城、转水、潭下、水寨等地的河道两岸,区内大面积的耕地、建设用地形成极高的污染负荷,加上水文现象活跃,大量的污染物质经快速迁移后进入河流,对河流水质形成决定性的影响。次级风险区(阻力距离为3195~7918)邻近高风险区分布,这部分区域与大面积的耕地和建设用地有一定的距离,污染负荷产出程度有所降低,但下垫面产流丰富,污染物迁移活跃,少量污染物质会进入河流对水体造成污染。过渡区(阻力距离为7918~13613)距高污染负荷地区较远,水文现象相对不那么活跃,为污染风险区和生态安全区的过渡地带。弱生态安全区(阻力距离为13613~20699)和生态安全区(阻力距离> 20699)主要分布于远离高污染负荷区或河流的长布、潭下北部、横陂西部及铁场与叶塘、登云交界等部分地区,这些区域产生的污染负荷量极小,且水文现象不活跃,地表产流能力低,污染物迁移受到抑制,或污染物迁移至河流的距离较远,污染物质能够到达河流并对河流水体造成污染的可能性极小。

图4 五华河流域非点源污染风险区 Fig. 4 Non-point source pollution risk area in Wuhua River Basin

4.3 非点源污染风险路径识别

由于耕地污染以化肥、杀虫剂和除草剂污染为主,建设用地污染以工业、生活垃圾污染为主,不同源地造成的污染差异较大,在进行实际治理时,需要针对不同的污染情况采取不同的方案,为此须将不同源地类型的非点源污染风险路径分开,逐一进行研究分析。

运用最小累积阻力模型分别获得两种不同类型源地到河道的最小累积阻力路径,即非点源污染风险路径,计算每一条路径的通过阻力,将通过阻力按从小到大分为1~5级,断点依次为0、500、5000、10000,计算每一级风险路径的数量和数量百分比(表4)。由表4可知,以耕地为“源”的风险路径有3080条,以建设用地为“源”的风险路径有2855条,可见耕地非点源污染风险路径的数量要大于建设用地非点源污染风险路径的数量。耕地非点源污染风险路径通过阻力最大值为38632.7,建设用地非点源污染风险路径通过阻力最大值为65109.8,可见在路径通过阻力最大值上,耕地非点源污染风险路径要小于建设用地非点源污染风险路径。由此表明,以耕地为源的非点源污染风险路径不仅数量多,而且入河阻力小,耕地形成的非点源污染更容易对水体造成污染。以建设用地为源的非点源污染风险路径数量相对较少,且入河阻力相对较大,建设用地形成的非点源污染对水质影响相对较小。

表4 最小累积阻力值 Tab. 4 Minimum cumulative resistance value

从各等级风险路径数量及百分比分布情况来看,以耕地为“源”的非点源污染风险路径中,1级路径数量最多,有1516条,占耕地总路径数量的49.22%。2级路径、3级路径、4级路径数量依次为427条、385条、342条,各占耕地总路径数量的13.86%、12.50%、11.11%。1~4级的耕地风险路径数量共占了总数的86.69%,5级路径的数量为410条,比例仅占13.31%。可见近半的耕地非点源污染风险路径通过阻力为零,化肥、农药等农业污染物质基本不受下垫面作用直接进入水体对水体造成严重污染。绝大多数的耕地非点源污染风险路径通过阻力较小,仅少数路径受到阻力较大,表明耕地形成的非点源污染极易迁移进入水体对水体造成显著影响。与耕地非点源污染风险路径相反,以建设用地为“源”的非点源污染风险路径中数量比例最大的为5级路径,共1598条,其比例达55.97%。2级路径、3级路径、4级路径数量分别为68条、628条、527条,占建设用地非点源污染风险路径总数的2.38%、22.00%、18.46%。1级路径数量最少,只有34条,比例仅占1.19%。表明建设用地形成的大部分非点源污染在迁移过程中受下垫面阻碍作用强烈,随着路径阻力等级的降低,相应的路径数量也不断减少,而完全不受下垫面阻力的建设用地非点源污染风险路径数量极少,建设用地形成的非点源污染对水体的影响相对较小。

图5a可见,大面积的耕地呈长条发散状邻近河道分布,耕地越邻近河流产生的1级路径越多,其通过阻力值为0,这部分河流极易直接遭受周边农业污水的侵入,且因耕地沿河分布,使污水的可能入河地点沿河流紧密分布,治理此类污染不应采取对关键源区进行面状整治的措施,而应在邻近耕地的河道两岸建立一定宽度的植被缓冲区,防止农业污水直接进入河流。从图5b可看出,在以建设用地为“源”的非点源污染的影响下,以登云、岐岭、华城为界,南部产生的建设用地非点源污染风险路径主要为2~3级,而北部产生的非点源污染风险路径主要为4~5级,表明由于流域南部大规模城镇的聚集,南部更易形成进入河流的低阻力风险路径,南部河流比北部更易受到建设用地污染物质的影响。总体来看,建设用地形成的1级非点源污染风险路径极少,绝大部分污染物质经受下垫面作用之后才会进入河流,对此类非点源污染可以就关键源区展开污染物拦截、转化等措施,从而达到遏制非点源污染的效果。

图5 五华河流域非点源污染风险路径 Fig. 5 Non-point source pollution risk path in Wuhua River Basin

值得注意的是,在紫市、长布、岐岭分布有长距离的1级耕地风险路径,在华城、叶塘两处存在长距离的1级建设用地风险路径,这些区域的污染物极有可能会跨过距离较长的下垫面后进入河流,所产生的污染物质不仅会影响附近河道,还会随地表径流到达其他支流河道,造成对其他河道水质和迁移路径下垫面环境的污染。尤其是位于长布镇的长距离污染物质迁移路径,因其处于生态安全区而极易遭到忽略,导致长布镇很大程度上面临一系列生态水体安全问题。而建设用地易发生重金属污染,在建设用地风险路径上不宜种植食用作物,防止作物对重金属物质的吸收进而危害人体,可以在该路径上种植利于污染物降解转化的植被,从而阻止污染物迁移。

4.4 结果验证

为验证本研究方法的合理性,根据4.3小节的结果,在耕地污染物入河阻力小的河子口和建设用地污染物入河阻力小的五华河口分别取水样进行水质检测(图6),检测内容包括电导率、悬浮物、硫酸盐、溶解氧、六价铬和总磷。使用雷磁JPB-607A便携式溶解氧测定仪现场进行溶解氧的检测,使用XZ-0142多参数水质分析仪当天在实验室进行悬浮物、硫酸盐、六价铬和总磷的检测。检测结果如表5所示。

图6 五华河流域水样采集点 Fig. 6 The distribution of sampling points

表5 水样检测结果(mg/L) Tab. 5 The test results of water samples (mg/L)

表5分析可得,河子口的总磷含量是0.06 mg/L,五华河口的总磷是0.02 mg/L,河子口的总磷含量比五华河口的总磷含量高。而河子口的溶解氧、悬浮物、硫酸盐和六价铬的含量分别是5.5 mg/L、16.06 mg/L、22.5 mg/L、0.09 mg/L,五华河口的溶解氧、悬浮物、硫酸盐和六价铬的含量分别是5.4 mg/L、27.71 mg/L、87.58 mg/L、0.18 mg/L,除溶解氧外,河子口的悬浮物、硫酸盐和六价铬的含量均比五华河口的悬浮物、硫酸盐和六价铬的含量低。耕地因为化肥的施用,所造成的非点源污染主要以总磷含量过高为主,而建设用地因为工厂及生活垃圾的影响,所造成的非点源污染则以悬浮物、含硫盐类和重金属为主。可见,在河子口处水质主要受到了耕地的影响,而五华河口处水质则主要是受到了建设用地的影响。这一结果与模拟的结果吻合。

5 结论与讨论

现有的研究对于非点源污染风险区识别的研究较多[12, 20],而对于非点源污染风险路径的探究较少,为此,本文运用了最小累积阻力模型,以高污染负荷的地区为“源”,下垫面产流性质为阻力面,对五华河流域非点源污染风险区和风险路径进行识别研究,得出以下结论:

(1)用最小累积阻力模型识别非点源污染风险区和风险路径,数据量要求低,操作简单,能够有效的对流域内的风险区和风险路径进行提取和分析。

(2)五华河流域下垫面条件差异较大造成其产流性质差异较大,受林地和透水性较好的土壤类型对地表产流的强烈抑制作用,低产流区主要分布在流域西南部的登云、岐岭、黄布、紫市、长布一带;在地形因素对地表产流的强烈促进作用下,高产流区贯穿流域呈人字形长条状分布。

(3)五华河流域非点源污染高风险区分布于迥龙、田心、龙母、铁场、登云、通衢、鹤市、紫市、岐岭、华城、转水、潭下、水寨等地的河道两岸,在地形、土地利用类型和土壤类型的共同作用下,随着与耕地、建设用地及河道距离的增加,高风险区逐渐过渡为生态安全区。

(4)五华河流域范围内耕地中的富营养物质和沉积物更容易随地表径流进入受纳水体,耕地对五华河水质的影响要大于建设用地。

(5)以耕地为“源”的风险路径和以建设用地为“源”的风险路径在阻力、长度、形态和污染物质方面都存在较大差异,对不同源地的非点源污染治理应采取不同的措施。治理以耕地为“源”的非点源污染宜在紧挨耕地的河流两岸建立一定宽度的植被缓冲区,治理以建设用地为“源”的非点源污染则宜围绕关键源区开展治理。

本研究对五华河流域进行风险区和风险路径识别,有利于今后对五华河流域非点源污染进行针对性治理,为五华河流域进一步进行景观格局优化提供理论依据,并为流域非点源污染风险区和风险路径的识别提供一种新的思路。但是本研究对这一方法的应用上还存在一些不足。首先是对于“源”的选取,本研究是基于流域的实际情况和已有的相关研究设定阈值,选取大于阈值的水田、旱地和建设用地作为“源”,在阈值的设定上具有一定的主观性。另外,CN值是基于美国的实际情况提出来的,在中国范围内直接运用会不可避免的造成误差。在资金和实验室设备的限制下,只采集了两个典型地区的水样进行了主要的水质检测,实地验证不够全面,有待于以后进行进一步的实证研究。

The authors have declared that no competing interests exist.

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<p>近年来非点源污染已经成为水污染的主要来源,对非点源污染发生机理和控制方法的研究有着重要的科学和现实意义.为了研究不同土地利用方式对非点源污染的影响,本文基于土地利用变化模型CLUES模拟了城市规划、历史趋势和生态保护3个预案下浑河太子河流域土地利用未来变化.应用SWAT模型对非点源污染进行了模拟研究,并结合实测数据对模拟结果进行了评价.结合两个模型研究了3个土地利用预案下非点源污染对土地利用和景观格局变化的响应.结果表明: SWAT模型在浑河太子河流域模拟精度较高,该模型在研究区具有适用性.城市规划和历史趋势预案下非点源污染负荷不断增加,城市规划方案下最高,生态保护预案下非点源污染负荷呈不断下降趋势.不同土地利用和景观格局对非点源污染有一定的影响,科学合理的生态建设能够有效减少非点源污染负荷.研究结果可以为流域的非点源污染研究提供案例,为非点源污染防治和最佳管理措施的制定提供科学依据,为相关政策制定提供参考.</p>
[6] White M J, Storm D E, Busteed P R, et al.Evaluating nonpoint source critical source area contributions at the watershed scale. Journal of Environmental Quality, 2009, 38(4): 1654-1663.
Areas with disproportionately high pollutant losses (i.e., critical source areas [CSAs]) have been widely recognized as priority areas for the control of nonpoint-source pollution. The identification and evaluation of CSAs at the watershed scale allows state and federal programs to implement soil and water conservation measures where they are needed most. Despite many potential advantages, many state and federal conservation programs do not actively target CSAs. There is a lack of research identifying the total CSA pollutant contribution at the watershed scale, and there is no quantitative assessment of program effectiveness if CSAs are actively targeted. The purpose of this research was to identify and quantify sediment and total phosphorus loads originating from CSAs at the watershed scale using the Soil and Water Assessment Tool. This research is a synthesis of CSA targeting studies performed in six Oklahoma priority watersheds from 2001 to 2007 to aid the Oklahoma Conservation Commission in the prioritized placement of subsidized conservation measures. Within these six watersheds, 5% of the land area yielded 50% of sediment and 34% of the phosphorus load. In watersheds dominated by agriculture, the worst 5% of agricultural land contributed, on average, 22% of the total agricultural pollutant load. Pollutant loads from these agricultural CSAs were more than four times greater than the average load from agricultural areas within the watershed. Conservation practices implemented in these areas can be more effective because they have the opportunity to treat more pollutant. The evaluation of CSAs and prioritized implementation of conservation measures at the watershed scale has the potential to significantly improve the effectiveness of state and federally sponsored water quality programs.
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[周慧平, 高超, 朱晓东. 关键源区识别: 农业非点源污染控制方法. 生态学报, 2005, 25(12): 3368-3374.]
非点源污染的控制难度大、成本高,必须首先识别流域内的关键源区。对国内外应用的关键源区识别方法从指标筛选、指标体系建立到关键源识别等关键技术环节进行了系统的评述,以促进该方法在我国农业非点源污染控制中的应用。
[9] Sivertun Å, Prange L.Non-point source critical area analysis in the Gisselö Watershed using GIS. Environmental Modelling & Software, 2003, 18(10): 887-898.
In the southeast in Norrk02ping, Sweden, is a small fjord-like bay called Sl01tbaken. The water quality in Sl01tbaken—with its narrow outlet to the Baltic Sea—depends highly on the water quality of the streams that flow in it. While point pollution sources can be identified easily in general, the non-point sources are harder to find. The most important sources for non-point pollution are agricultural areas, and the pollutants are mostly nutrients like phosphorus, which come from the fertilisation of the fields. One important catchment area for Sl01tbaken is the 57.7 km Gissel02 river basin (part of the topographic map 8GNO), which contains large agricultural areas.The transport of water pollutants is based on the same hydrological processes as erosion and sediment transport. The implementation of such a model in a GIS allows the analysis of a large area with a high resolution. When choosing the model, special attention was paid to the possibility of using a verified model that is easy to implement in a commercial GIS without the need of too much expert knowledge. This may allow its widespread use in many administrative applications that need non-point source information. A feasibility test for an enhanced GIS USLE model was done in the Gissel02 drainage basin before it was implemented for all river basins in the whole administrative area of Norrk02pings kommun.It is possible to use the suggested simplified USLE model to estimate the load of both pollutants and sediments, and it is able to show the areas that are critical for the water quality at the outlet of the water basin. The model has been evaluated in three studies [Int. J. Geogr. Inf. Syst. 2 (4) (1988) 365; A GIS to target critical areas for non point source management, in: Proceedings of the International Non Point Source Management Symposium, Austin, TX, November 7, 1989; Vatten 48 (1992) 117]. Then, implemented in a very simple GIS that allowed only rough estimates of terrain models and distances, the model was able to estimate the total suspended solids (TSS) and total phosphorus (TP) loads in the Svart02 river basin of 1539 km in the same region as Gissel02 and the Bornsj02 river basin outside Stockholm. Besides an estimated of 0.91–0.98 (verified by a more than one year measurement from manual and automated sampling stations in the whole river basin), the benefit with the GIS implemented USLE was the possibility to identify the risk areas with high spatial accuracy. During the last decade, both available databases and software have changed the possibilities to identify areas at risk of nutrient leakage. Schein [GIS Methods for Monitoring Sustainable Development by Analysis of Land-use and Land Cover Changes in the Surroundings of Link02ping (Sweden), Institut für Photogrammetrie und Fernerkundung, Technische Universit01t Dresden, Germany] and Schein and Sivertun [Method and models for sustainable development monitoring and analyses in GIS, in: Proceedings of the International Workshop on Geo-Spatial Knowledge Processing for Natural Resource Management, University of Insubria, Varese, Italy, June 28–29, 2001] show that the enhanced land use data available through the European Union agricultural support program can be used together with remote sensing data to fine tune the modified GIS USLE model. The problems with the new 50×50 m digital elevation data now available are also pointed out here. Obvious errors in the data and possibilities to enhance the model by introducing a better terrain model were two important suggestions in these works. In this article, two modifications to the original model are suggested. One is enhancement of the digital terrain model by using height curves from the digital 1:50?000 scale topographic map, and the other is a smooth distance function that better reflects the impact of nutrients on water bodies.Because of its easy implementation on standard low cost systems, the GIS USLE model is suitable for analysing huge areas for critical places. The results can lead to more detailed studies in the risk areas thus identified or to investigations on the effect of land use changes, or can be used simply for taking care in the use of fertilisers and other chemicals in the critical agricultural areas.
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ABSTRACT: The Export Coefficient model (ECM) is capable of generating reasonable estimates of annual phosphorous loading simply from a watershed's land cover data and export coefficient values (ECVs). In its current form, the ECM assumes that ECVs are homogeneous within each land cover type, yet basic nutrient runoff and hydrological theory suggests that runoff rates have spatial patterns controlled by loading and filtering along the flow paths from the upslope contributing area and downslope dispersal area. Using a geographic information system (GIS) raster, or pixel, modeling format, these contributing area and dispersal area (CADA) controls were derived from the perspective of each individual watershed pixel to weight the otherwise homogeneous ECVs for phosphorous. Although the CADA-ECM predicts export coefficient spatial variation for a single land use type, the lumped basin load is unaffected by weighting. After CADA weighting, a map of the new ECVs addressed the three fundamental criteria for targeting critical pollutant loading areas: (1) the presence of the pollutant, (2) the likelihood for runoff to carry the pollutant offsite, and (3) the likelihood that buffers will trap nutrients prior to their runoff into the receiving water body. These spatially distributed maps of the most important pollutant management areas were used within New York's West Branch Delaware River watershed to demonstrate how the CADA-ECM could be applied in targeting phosphorous critical loading areas.
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Identification of critical source areas (CSAs) (areas contributing most of the pollutants in a watershed) is important for cost-effective implementation of best management practices. Identification of such areas is often done through watershed modeling. Various watershed models are available for this purpose, however it is not clear if the choice (and complexity) of a model would lead to differences in locations of CSAs. The objective of this study was to use two models of different complexity for identifying CSAs. The relatively complex Soil and Water Assessment Tool (SWAT) and the simpler Generalized Watershed Loading Function (GWLF) were used to identify CSAs of sediment and nutrients in the Saugahatchee Creek watershed in east central Alabama. Models were calibrated and validated for streamflow, sediment, total nitrogen (TN) and total phosphorus (TP) at a monthly time scale. While both models performed well for streamflow, SWAT performed slightly better than GWLF for sediment, TN and TP. Sub-watersheds dominated by urban land use were among those producing the highest amount of sediment, TN and TP loads, and thus identified as CSAs. Sub-watersheds with some amount of agricultural crops were also identified as CSAs of TP and TN. A few hay/pasture dominated sub-watersheds were identified as CSAs of TN. The identified land use source areas were also supported by field collected water quality data. A combined index was used to identify the sub-watersheds (CSAs) that need to be targeted for overall reduction of sediment, TN and TP. While many CSAs identified by SWAT and GWLF were the same, some CSAs were different. Therefore, this study concludes that model choice will affect the location of some CSAs.
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景观格局优化是景观生态学中的难点和热点问题。选择滇池流域为研究区域,在RS和GIS的支持下对2008年Landsat TM影像进行解译判读,获得景观类型图,运用最小耗费距离模型对区域景观格局进行优化。结合景观各组分生态系统服务功能价值和空间作用,构建了生态源地、生态廊道和生态节点等景观组分,以加强生态网络的空间连通性,提高景观格局稳定性,完善生态功能。源地具有较高的生态系统服务功能,需要维持和增大源斑块面积。所构建城市区域廊道、森林生态廊道、农业生产廊道应采取保持廊道规模,建立缓冲区,加强植被绿化,减少污染物的排放等措施以提高整个廊道的连通性。节点位于景观生态流和连通的重要位置上,需要加强控制。该研究对流域生态规划和土地利用优化布局有一定的参考价值。
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生态安全格局构建是保障城市生态安全,实现城市可持续性发展的重要途径,也是景观生态学研究的热点领域之一。生态源地识别与空间阻力面构建一直是生态安全格局构建中的技术难点。以广东省云浮市为例,从生态系统服务重要性、生态敏感性与景观连通性三个方面识别生态源地,利用DMSP/OLS夜间灯光数据修正基本生态阻力面,并运用最小累积阻力模型判定生态廊道,从而综合构建云浮市生态安全格局。研究表明:云浮市生态源地占全市总面积的36.47%,主要分布在西部与南部的山林地。云浮市生态廊道总长度为508.87 km,其中景观廊道总长度为315.58 km,组团廊道总长度为193.29 km,呈环状辐射分布于植被覆盖相对较好的山区地带。全市16个自然保护区基本都位于生态源地范围内。构建的"重要性—敏感性—连通性"框架可以为区域生态安全格局构建提供新思路,从而有效指引相关空间规划。
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Habitat patches situated amidst an otherwise inhospitable landscape are often considered as islands in the sense of the equilibrium theory of insular zoogeography. Their species richness can be affected by isolation from other areas of suitable habitat. However, the isolation of habitat islands is not only dependent on the distance from the source area, as with oceanic islands, but also on the characteristics of the interjacent landscape. To account for the latter, the use of a measure of isolation termed ‘minimal cumulative resistance’ (MCR) is proposed. A simple model is described for calculating MCR from a grid-based map on which estimated dispersal resistances are assigned to landscape types. Application of the model is illustrated with a specific case: the allocation of new forests in the western part of the Netherlands. Although its application is bound by a number of restrictions, it is concluded that the model can be a useful aid in physical planning and nature conservation.
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耕地所引起的农业面源污染是三峡库区主要生态环境问题之一。该文设置距离长江干流0~20、20~40、40~60和60~80 km的缓冲区,对库区耕地源景观划分4个等级,依据耕地面源污染过程,在获取地形、地貌、气象、水文、土壤和植被等方面的主要自然影响因子的基础上,构建影响耕地面源污染的阻力基面,借助最小累计阻力模型测算不同等级源景观阻力面,并通过自然断点法对阻力面进行5个等级的源-汇风险分级(极低风险区、低风险区、中风险区、高风险区和极高风险区),以此识别影响库区耕地面源污染的源-汇风险格局,结果表明:①库区一级源耕地占总耕地面积的50%以上,越向外围延伸耕地分布空间越小,且重庆库区的分布多于湖北库区,旱地的分布多于水田;②在耕地源景观所处的缓冲区范围内,阻力面偏小,并围绕源景观向外呈现不断增大的趋势,且水田源景观阻力面大于旱地源景观;③受空间距离的影响,阻力面的空间特征表现为高值区空间范围明显小于低值区;④库区耕地面源污染源-汇风险格局特征表现为高风险趋势,极高风险区(21 706.13 km2)>中风险区(16 257.75 km2)>极低风险区(10 311.6 km2)>高风险区(7 464.65 km2)>低风险区(2 221.61 km2);⑤高风险区主要集中于库区平行岭谷区,而低风险区主要分散在距离长江干流偏远的秦巴山区和武陵山区;⑥研究结果有助于从影响面源污染的阻力面角度评价由耕地所产生面源污染的风险程度及等级,为科学防范和治理农业面源污染提供决策依据。
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[郭宏斌, 黄义雄, 叶功富, . 厦门城市生态功能网络评价及其优化研究. 自然资源学报, 2010, 25(1): 71-79.]
在城市系统中改善与提高生态功能网络的连接对提升城市生态系统的功能及价值、限制城市格局蔓延具有重要意义。针对厦门市的城市化问题,运用熵值法综合评价不同景观类型的结构和功能对于城市生态功能流的影响,在GIS软件辅助下,利用最小耗费距离模型提取生态功能网络中廊道的最佳路径,最终通过网络结构分析确定生态功能网络的最优框架。研究结果表明,厦门市城市绿地空间分布不均,建成区内生态斑块质量不高,破碎程度较大,生态功能流无法形成良好的衔接,在今后的城市规划中,急需对城市生态功能网络的结构和功能进行优化。本研究的方法和结果可为城市生态功能网络研究的定量分析及网络的建设提供科学依据和参考。
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本文将城市扩展视为城市用地克服生态阻力向外扩散的过程,基于最小累积阻力模型(MCR)进行方法创新,在模型中引入不同等级源的相对阻力因子,并考虑生态障碍对于城市扩展的刚性约束,构建适合于城市扩展模拟的生态阻力面模型(UEER)。在此基础上,利用广州市土地利用第二次调查数据、遥感影像数据、DEM数据以及其他生态要素相关数据,通过源的确定与分级、基面阻力综合评价、UEER模型运算等步骤,构建了广州市城市扩展的生态阻力面,并用于模拟城市用地扩展至不同规模情景时的空间分布及边界。结果表明:①通过UEER模型生成的生态阻力面能够综合反映城市扩展水平过程所需克服的生态阻力,因此能够反映生态约束下城市扩展的空间运动趋势,可以用于城市扩展模拟。②与基于MCR模型的模拟结果相比,基于UEER模型的模拟结果更加符合实际并体现生态保护的要求。从城市扩展的规模与强度控制看,模拟结果更加符合实际需求,并体现政策调控方向。从城市形态以及与生态要素的关系看,一些重要的生态要素在快速城市化进程中能够得以保留,同时生态障碍作为生态隔离,能够有效地防止城市的蔓延式扩展,从而使城市扩展表现出明显的组团式特征。
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以生态脆弱区典型县域磴口县为研究区,基于2015年遥感影像解译数据,构建MCR—P模型提取县域尺度生态源地、廊道和节点,将Voronoi图模型引入到生态节点布局研究中,构建泰森盲区形心优化模型(BCBS模型),进行生态节点布局优化。结果表明,磴口县共能提取出391块生态源地节点和667个潜在生态节点,其中需优化节点182个。优化后生态节点的覆盖率达到87.79%,较现状生态节点覆盖率提升了22.56%。节点分布均匀性降低至0.3978,节点空间分布更加均匀。优化后节点构成的泰森多边形盲区的面积减小了48446hm^2,且节点覆盖圆空间结构连片化,优化后生态网络结构更为稳定。
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