鄱阳湖区洪灾风险与农户脆弱性分析_英文_

Journal of Geographical Sciences

? 2007 Science in China Press Springer-Verlag

Received : 2007-02-08 Accepted : 2007-03-16

Foundation : Key Laboratory of Poyang Lake Ecological Environment and Resource Development, No.PK2004017; Na-

tional Natural Science Foundation of China, No.40561011

Author: Ma Dingguo (1960?), Associate Professor, specialized in the fields of population geography and regional devel-

opment. E-mail: dgm600@https://www.wendangku.net/doc/0a6123193.html,

https://www.wendangku.net/doc/0a6123193.html, https://www.wendangku.net/doc/0a6123193.html,

DOI: 10.1007/s11442-007-0269-5

Farmers’ vulnerability to flood risk:

A case study in the Poyang Lake Region

MA Dingguo 1,2,3, CHEN Jie 2, ZHANG Wenjiang 1,3, ZHENG Lin 2, LIU Ying 2

1. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China;

2. College of Geography ＆ Environment, Jiangxi Normal University, Nanchang 330022, China;

3. Graduate School of the Chinese Academy of Sciences, Beijing 100039, China

Abstract: This paper quantitatively explores farmers’ vulnerability to flood in the Poyang Lake Region (PLR) with the supports of GIS spatial functions. The analysis consists of three major steps, which is based on the spatial unit of township. Firstly, the spatial extent and charac-teristics of flood risk areas were determined using a digital elevation model (DEM) derived from the 1:50,000 topographic map. Secondly, for each of the township, six indices indicating the economic activities of local farmers were calculated. These indices are: rural population proportion, cultivated land proportion, GDP per unit area, employment proportion of primary industry, net rural income per capita and agricultural income proportion. These six indices were then normalized and used for later vulnerability assessment. Thirdly, the normalized indices (as GIS data layers) were overlaid with the flood risk areas to produce the risk coeffi-cient for each township and to calculate the overall vulnerability for each township. The analysis results show that in the PLR there are high flood risk areas where the farmers’ livings are seriously influenced or threatened. About 55.56% of the total 180 townships in the flood risk areas have a high degree of flood vulnerability. The townships under flood risk are mainly distributed in the areas around the Poyang Lake and the areas along the “five rivers”. Keywords: flood risk; farmer; vulnerability; Poyang Lake Region

1 Introduction

The middle and lower reaches of most China’s major rivers are the regions with dense population and socio-economic activities. However, on the other hand these regions are also those frequently exposed to flood disasters, which often bring serious damages to local economy and society (Wang et al., 1999; Shi et al., 2000; Li et al., 2000). Since the 20th century, the frequency and strength of flood disasters have been in an increasing trend with the intensification of human activities and the growth of national economy, which imposes a serious threat to the civil safety and local properties, and restricts the local economic and

270 Journal of Geographical Sciences social development (Wang et al., 2000; Liu et al., 2005; Zhou et al., 1999). Thus, it is the target of national strategy upon flood management and is the direction for researchers and managers to address the question of natural hazards, that is, how to mitigate the flood in-duced losses and how to construct a harmonious relationship between floods and socio-economic systems.

Since the 1980s, the vulnerability of human own system, which plays an important role in the disaster process, has received wide attention from international calamity field (Martha et al., 2003; Zhu et al., 2005). By definition, vulnerability is the lack of response capability to external risk or even disaster (Chambers et al., 1989; Kelly T M et al., 2000; Bogardi J et al., 2004), indicating the sensitive degree of individuals, groups or systems to environmental changes or risks. Disaster is considered as the integrated result of interaction between natu-ral environment on the earth’s surface, harmful events and the vulnerability of socio-economic systems (Shang et al., 1998; Shi et al., 2002). On the regional scale, these three factors are the disaster environment, disaster inducing factor and disaster object, re-spectively. Under a certain disaster event, the disaster caused damage and loss would in-crease with the corresponding vulnerability (Shang et al., 1998; Shang, 2000a; Liu et al., 2002; Shi et al., 1996). At present, the natural environment and hazardous events can only be understood from the aspects of hazard causes and mechanism (although limited some-times), but it is difficult to change the disaster process and to reduce its threat. Therefore, it is of the main solution of disaster defending and mitigation to reduce the vulnerability of social-ecological systems (Liu et al., 2000; Fan et al., 2000; Su et al., 2005). International researches on vulnerability to disaster mainly focus on the establishing of vulnerability analysis frameworks (Martha G et al., 2003; Klein et al., 1999; Turner et al., 2003; Chu et al., 2005), the social vulnerability to global environmental changes (esp. climate changes) (Dow K, 1992; Wisner B, 1993; Watts M J et al., 1993; Bohe H G et al., 1994; Burton I, 1997; Handmer J W et al., 1999; Dorland et al., 1999; Adger W N et al., 1999; Adger W N, 1999), and vulnerability of daily life, civil living and food safety caused by flood disasters in developing countries (Downing T E, 1991; Yarnal B, 1994; Pelling M, 1997; Hamza et al., 1998; Mustafa D et al., 1998; Dilley M, 2000; O’Brien K et al., 2004). These efforts par-ticularly emphasize the formulations of vulnerability to disaster exposures. In China, vul-nerability related studies began in the 1990s. Through discussing definitions, factors, and explanations of vulnerability，this study aims to construct vulnerability assessment indicators for flood object and the quantitative analysis model within a region (or a system) (Zhu et al., 2005; Shang et al., 1998; Liu et al., 2002; Fan et al., 2000; Su et al., 2005; Wei et al., 2004; Shang et al., 2000b; Wang et al., 2003; Fan et al., 2003; Shi et al., 2004; Liu et al., 2005; Cui et al., 2005), and the vulnerability of agriculture and water resource systems to global climate changes at the national scale as well (Cai et al., 1996; Wang J et al., 2005; Wang G et al., 2005).

It is of great importance to expound the vulnerability of different regions to assist the government to make efficient and practical policies for allocating relief funds and to help the regions to improve their capabilities against disasters. In general, farmers are the major vul-nerable group in flood risk. Taking the Poyang Lake Region (PLR) as a case, this paper aims to discuss the impacts of flood disasters on farmers, to explore the rural farmers’ vulnerabil-ity to flood, and to analyze the spatial pattern characteristics by mapping different vulner-abilities among various PLR townships.

MA Dingguo et al.:Farmers’ vulnerability to flood risk: A case study in the Poyang Lake Region 271 2 Materials and methodology

2.1 The study area

The Poyang Lake lies in the northern Jiangxi province, to the south of the middle and lower

reaches of the Changjiang (Yangtze) River. It is the largest fresh water lake in China and

also the biggest Asian wetland. More importantly, the Poyang Lake palys an important regulation function in the watershed discharge system of the Changjiang River (Duan et al.,

2001; Zhu et al., 2002). As a seasonal lake, the Poyang Lake recharges water from the five

rivers (Ganjiang River, Fuhe River, Xinjiang River, Raohe River and Xiuhe River) and dis-

charges to the Changjiang River at Hukou when the lake level rises in an annual period. So,

the Poyang Lake is doubly regulated by the five rivers and the Changjiang River, which re-

sults in its annual fluctuation of water level between the wet summer season and the rela-

tively dry fall and winter. When the lake reached the highest water level of 22.59 m on July

31, 1998 (at Hukou Hydrological Station of Wusong Base Level), its area and volume are

4,070 km2 and 320×108 m3, respectively. And the lowest water level is 5.9 m observed on February 6, 1963, and the corresponding area and volume are 146 km2 and 4.5×108 m3. The

sharp variation of water body brings up a unique landscape for the lake which looks like

narrow river channels at low water while a vast expanse of water at flood (Figure 1).

a) Map of the high water level (Aug. 25, 1998) b) Map of the low water level (Dec. 10, 1999)

Figure 1 Comparison of different water levels in the Poyang Lake

The Poyang Lake Region (PLR) in this study refers to the areas around the lake which

ranges from 28°11'N to 29°51'N and from 115°31'E to 117°06'E, including two cities (Nanchang and Jiujiang) and ten counties (Nanchang, Xinjian, Jinxian, Hukou, Xingzi, Duchang, De’an, Yongxiu, Yugan and Poyang). The total area is 20,970 km2, accounting for

12.56% of the province’s total of Jiangxi. The warm, humid subtropical monsoon brings

℃

favorable climate resources, with a mean annual temperature of 16.5–17.8 and plentiful

rainfall of 1570 mm, 50% of which concentrates in the period of April–June (PL Study Committee, 1998). The PLR is one of China’s major production bases of grain, cotton, edi-

ble oil and fish in Jiangxi province. However, except Nanchang and Jiujiang cities, region is

also one of the serious poverty-stricken areas in Jiangxi province. In 2004, the population of

these 10 counties accounted for 15.08% of the total of Jiangxi, but the GDP and local financial

272 Journal of Geographical Sciences revenues only account for 8.76% and 6.86% of the province’s total, respectively.

2.2 Data collection

In this paper, the data and information used include: (1) digital elevation model (DEM) of 1:50,000 and the townships administrative map in the PLR; (2) information of the cultivated land area derived from Landsat TM images; (3) water level observation at major local hy-drological stations from 1949–2001 provided by the Key Laboratory of Poyang Lake Eco-logical Environment and Resource Development; and (4) main socio-economic indicators of townships in 2004 derived from “The Basic Information of Administrative Townships”, “The Rural Cooperative Economic Table Statistics”, and other statistical data.

2.3 Methodology

The basic idea of this paper is to explore the flood risk and vulnerability of rural farmers using the PLR spatial attribute data with the support of ESRI ArcGIS analysis functions. The methodological scheme is shown in Figure 2.

Figure 2 Conceptual framework of the study

(1) Constructing database for spatial analysis: To take townships as the basic spatial sta-tistical unit and establish the townships' spatial database which is the basis for the later vul-nerability analysis for various indicators.

(2) Selecting Indicator: To select rural population proportion and cultivated land propor-tion as exposure indices, and select GDP per unit area, rural employment proportion of the primary industry, net income per farmer and agricultural income proportion to express farmer’s response capability to flood. To define the flood intensity, four critical water levels at Hukou Hydrological Station are selected, which are 18.50 m, 20.50 m, 21.68 m and 22.59 m. The national project of “restoring lakes from fields” advises that the 18.50 m in the PLR should be the flood water level to completely retreat for flood regulation. In addition, 20.50 m and 21.68 m are defined as the lowest flood water levels for the partially retreated levee with the protected area less or more than 667 hm2 respectively, while the level of 22.59 m is the highest water level record (in 1998).

(3) Normalizing spatial data: To facilitate the overall vulnerability calculation for each

MA Dingguo et al .: Farmers’ vulnerability to flood risk: A case study in the Poyang Lake Region 273

township, normalize the above six indices with the following methods.

For positive correlation indices with vulnerability, then

m x

x y m i ij

ij

ij ×=∑=1, (1≤i ≤m , 1≤j ≤n ) (1) For negative correlation indices, then

ij ij m

i ij m i ij x x x x ?+=′≤≤≤≤11min max ， m x x y m i ij

ij

ij ×′

′=∑=1, (1≤i ≤m , 1≤j ≤n ) (2) where y ij is the normalized index, x ij is the initial index value, m is the township number un-der investigation, and n is the indices number.

(4) Calculating vulnerability: The township units below different critical water levels are extracted in GIS and the flooded units are assigned as the influence coefficient of 1.00, 0.95, 0.90 or 0.85 depending on the corresponding critical water level (from low to high). For townships above 22.59 m, the coefficient is 0. Then the farmers’ vulnerability for each township unit is defined as a weighted sum:

∑==6

12j ij i y k V (3)

where V i is the farmers’ vulnerability value of the i th unit, and greater value means more vulnerable, y ij is the normalized value of the j th index in the i th unit, and k means the influ-ence coefficient of flood water level, which is determined according to the elevation of the study area.

3 Analysis of flood disaster risk in the PLR

3.1 The floods in the PLR

The PLR is among the regions with flood occurring most frequently in China. During the 12th –19th century, the region experienced an increasing flood trend (Figure 3). Since the 20th century, flood event has happened once every 2.7 years, with both flood water level and flood frequency increase obviously. In the recent serious flood periods of 1954, 1983, 1995 and 1998, the highest water levels observed at Hukou were 21.68 m, 21.71 m, 21.80 m and 22.59 m, respectively (Shu et al., 2001). During the latter half of the century, the Hukou level higher than 21 m only occurred twice in the first 40 years. However, the water level of 21 m was observed four times in the last 10 years of the latter half of the century. Undoubt-edly and obviously, flood risk there is getting more serious with time.

3.2 Flood causes

3.2.1 The meteorological and hydrological characteristics consist of the prerequisite for the frequent occurrence of the floods

Generally, the flood season of the Poyang water system is the period of April –July when the five rivers simultaneously discharge water into the Poyang Lake so as to lead the lake water

274 Journal of Geographical Sciences

Figure 3 Flood trends in the PLR since the 12th century

Table 1Monthly variation and frequencies of annual maximum water levels recorded in the PLR (1949–2001)

Frequency of the highest water level Station Statistic years Occurrence time

Occurrence times (Num.)Probability (%)

(Month)

Hukou 1949–2001 7–9 43 81.13 Xinzi 1951–2001 7–9 38 74.51 Duchang 1952–2001 7–9 38 76.00 Wucheng 1953–2001 7–9 37 75.51 Ziyin 1962–2001 7–9 33 82.50 Source: The Key Lab of Poyang Lake Ecological Environment and Resource Development

level higher. The largest annual lake inflow from the five rivers would occur during April–June with a probability of 84.2% (Zhu et al., 2002). The main flood season of the Changjiang River is from July to September, which would restrain the lake discharge so that the lake level rises quickly and keeps high for quite a long period (Table 1). Both the situa-tions of the simultaneous inflow from the five rivers and the block of the lake discharge jointly lead to the annual highest level and even floods, which can explain the obvious sea-sonal fluctuation of the lake level.

3.2.2 Human activities further boost flood intensity

The PLR is the earliest economically developed region in Jiangxi and is densely populated. The population density there is 433 people/km2 (in 2004), which is higher than the province average level by 176. The average cultivated land per capita is only 0.06 hm2 (excluding the urban area of Nanchang and Jiujiang). The problem of more population and less land be-comes even more serious.

(1) The lake has shrunk both in water area and volume because of land reclamation activi-ties by building dams around the lake, which caused the degeneration of flood regulation, the rise of flood level and the deterioration of flood disasters. The reclamation of the lake and marshland to meet the need of land resource for development reached the climax during the 1950s–1960s. Large-scale reclamation was not restricted until the 1980s when the total reclamation area reached 1466.9 km2 (Table 2). The shrinkage of the lake volume has ex-ceeded 80×108 m3, which is about 53% of the lake volume in the late 1990s.

Table 2Inning situation in the PLR during the 20th century (km2)

Time 1950s 1960s 1970s 1980s Area of reclamation 394.9 793.4 211.7 66.9 Cumulative area of reclamation394.9 1188.3 1400.0 1466.9 Source: After Shu et al.,2001.

MA Dingguo et al.:Farmers’ vulnerability to flood risk: A case study in the Poyang Lake Region 275 (2) In the last decades, there were serious deforestation activities in the Poyang lake basin

which led to aggravation of soil erosion, lake bed elevation and decrease of flood regulation capability. During the period of the 1950–1980s, the area of barren hills increased from

81.89×104 hm2 to 285.40×104 hm2. Firstly, this destruction of vegetation cover would accel-

erate the runoff process on land surface, which then induces both sudden rises of flood peak

and of volume from upper reaches to the lake. Secondly, the degradation of natural land

cover has made the lake basin become one of the regions confronting most serious soil ero-

sion in southern China. In this region, the soil eroded area has reached 336.12×104 hm2 (Shu

et al., 2001).

3.3 Spatial distribution characteristics of flood risk

Only inundating a region where people lives and bringing damages to civil properties or to

economic activities, that can the flood be called a disaster to human being. The area affected

by flood then is the risk area. Different critical water levels at Hukou are used to indicate

flood intensity. With the supports of ArcGIS operation and DEM information, the spatial distributions of flood risk at different critical levels are produced through interpolative simulation with level observations of 12 hydrological stations around the lake (Figure 4).

This simulation shows that the flood risk is mainly distributed in the areas around the lake

and the five rivers’ banks. It should be noted that Figure 4 shows flood situation without any

levee protection, so flood risk areas are by no means actual disaster areas. In fact, it is be-

cause of the levee protection that Nanchang, Jiujiang and other densely populated cities can

escape from flood damage.

Figure 4The spatial distribution of flood risk in the PLR

Table 3The proportion of flood risk areas at different water levels in the PLR

amount

Elevation(Wusong datum, m)< 18.50 18.50–20.5020.50–21.6821.68–22.59 Total

7110 Area (km2) 5270

370

510

960

Proportion (%) 74.12 13.50 7.18 5.20 100

Involved townships (number) 200 7 7 9 223

276 Journal of Geographical Sciences With the raster calculation function of ArcGIS, the flood risk areas at different water lev-els are estimated and shown in Table 3. About 74.12% of the total risk area would be flooded when the water level reaches 18.5 m while the proportion would increase to 87.62% when the water level rises to 20.5 m. The structure of flooded area indicates the high sensitivity and vulnerability of the study area to flood (Table 3). In fact, the five rivers seldom experi-ence floods simultaneously, and the actual flood affected area is dependent upon the location of precipitation centers and extent.

4 Farmers’ vulnerability analysis

The farmers’ vulnerability to flood in Poyang risk area can be reflected both in the flood damages to their properties and livelihood and in their recover capability after flood disaster. The latter is more important to the regional development, and to the maintenance and im-provement of farmer’s living standards. The farmers’ vulnerability of flood area actually is the socio-economic vulnerability. Due to statistical reasons, only 185 townships in 10 coun-ties and 1 district (Lushan of Jiujiang city) are chosen to explore the farmers’ vulnerability. In addition, related data are absent for 5 of the 185 town units, so totally 180 townships are fallen in actual coverage of further analysis.

4.1 Diagnosis of individual vulnerability indicator

Individual indicators can express farmers’ vulnerability to flood from different aspects. The selected townships are compared with different statistical elements of 2004 and the land use data of 2005, in order to delineate the spatial distribution of vulnerable farmer groups in the lake. In ArcGIS, the spatial attribute tables and the flood vulnerability maps of individual variables are produced for the risk areas (Figure 5).

4.1.1 Risk exposure analysis

In the PLR, the main subject exposed to flood risk is the affected residents and their proper-ties. The higher the exposure degree to flood risk is, the more vulnerable the main risk sub-ject is. The proportions of rural population and cultivated area in townships are the impor-tant factors indicating the exposure degree to flood risk.

Rural population proportion: Rural population is the main group affected by floods. So, the higher the rural population proportion is, the stronger the vulnerability of a certain township to flood is. The rural population proportion in the PLR is as high as 78.28% aver-agely (Table 4). Among these 180 townships, 138 have a rural population accounting for over 80% of the total. The rural population of the 138 townships is about 376×104, which is Table 4Grade distribution of exposure variables of the study units in the PLR (2004)

Proportion of rural population Proportion of cultivated land area

% Rural population

(104 persons)

Townships

(num.)

%

Cultivated land

(104 hm2)

Townships

(num.)

>90 256 93 >60 12 25 80–90 120 45 50–60 14 35 70–80 50 18 40–50 15 39 50–70 28 13 30–40 15 43 <50 18 11 <30 9 38

MA Dingguo et al.:Farmers’ vulnerability to flood risk: A case study in the Poyang Lake Region 277

Figure 5 Spatial distribution of major variables in the PLR

278 Journal of Geographical Sciences 79.66% of the total rural population of the study area. In addition, there are 93 townships with a rural population of over 90% of the total each or a rural population adding up to about 256×104, accounting for 54.24% of the total rural population of the study area. With respect to the spatial distribution (Figure 5a), the rural population proportion in the eastern PLR is higher than in the western while the proportion in the northern is higher than in the southern. Such a high proportion of population confined to rural area of the PLR leads to the high vulnerability to flood, which seriously influences and threats the farmers and their liveli-hood.

Proportion of the cultivated land: The low-lying land of the PLR make it liable to be inundated by flood, which often brings serious damage to local agriculture. Generally speaking, the higher the cultivated land proportion is, the stronger the vulnerability of a cer-tain region to flood risk is. The total cultivated land proportion is approximately 37.45% in the PLR, where there are 99 townships with this proportion exceeding 40% (Table 4). The cultivated land of the 99 townships reaches 41×104 hm2, 63.08% of the total cultivated land in the PLR. It indicates these townships with compact agricultural land are seriously vul-nerable to flood. In addition, there are 25 townships with cultivated land proportion exceed-ing 60%, which are distributed in the southern and western PLR.

4.1.2Response capability to flood

The farmers’ response capability to flood is important to determine the flood vulnerability degree, and it is also directly related with the potential and level of socio-economic devel-opment. Therefore, the comparison of socio-economic factors of townships is helpful to de-termine the response capability of farmers to flood, the flood vulnerability of townships and the spatial pattern of risk vulnerability.

GDP per unit area: Under certain flood, an economically relatively developed region with high GDP can more effectively make use of local plentiful of resources to avoid or combat flood. Although the absolute economic losses in the developed region may be higher than those of the developing one, the loss rate of the former is lower. So, the developed re-gion has the stronger capacity to cope with flood, and the disaster damage can be recovered more easily and quickly. However, the developing region with poor response capability can be heavily struck in the civil living and agricultural production by a flood of the same inten-sity. It would take quite a long time to restore slowly from the flood damages. So, it can be summarized that the higher the GDP per unit area is, the lower the rural farmers’ vulnerabil-ity is. For the 180 townships in the PLR, the average GDP per unit area is approximately only 171×104 yuan/km2. Only 47 townships’ GDP per unit area is more than 200×104 yuan/km2 (Table 5), which accounts for 65.92% of the total GDP of the 180 townships. The average unit GDP of other 133 townships is less than 75×104 yuan/km2. This fact indicates the low flood response capability of most townships. Figure 5c shows the relatively higher GDP in the southern and western PLR.

The net income of per rural capita: This indicator reflects the development level of the overall rural economy in the study area, and also reflects the overall living conditions of the local farmers. In the area with high per capita net income, the rural economy is strong and local farmers are well-off. Then the corresponding vulnerability is relatively weak. Other-wise, if the rural per capita net income is low and the farmers are poor, then the region is more vulnerable to flood risk. The rural per capita net income is only 2330 yuan in the study area. Among the 180 townships, 105 have a rural income per capita less than 2330 yuan. The

MA Dingguo et al.:Farmers’ vulnerability to flood risk: A case study in the Poyang Lake Region 279 average per capita net income of these 105 townships is less than 1800 yuan. The overall

low rural income is an important characteristic related with flood disaster vulnerability. The townships with relatively high net income are mainly distributed in the southern and western

PLR. And rural income near the urban area is relatively high as shown in Figure 5d.

Employment proportion of primary industry: The primary industry in the PLR is ag-riculture. The higher the employment proportion of primary industry is, the more the labors concentrate in the agricultural field, the more the local economy depends on agriculture, and

therefore the stronger the vulnerability to flood is. On the contrary, if the employment pro-

portion of primary industry of a certain region is low and more labors are taken in by the secondary and tertiary industries, then this region would be more capable to defend flood

risk and the corresponding vulnerability is lower. For the 180 townships (Table 5), the aver-

age employment proportion of primary industry is 54.94%, and there are 135 towns with this proportion exceeding 50%, accounting for 80.20% of the primary industrial employment of

the whole study area. And the primary industry employment proportion of 86 townships is

more than 60%, accounting for 56.73% of the primary industrial employment of the whole

study area. The heavily agriculture-oriented employment is a prime reason for the high vul-

nerability of most of the PLR townships. For the urban townships contributing to local main secondary and tertiary industries and some suburban townships with relatively favorable economical and transportation conditions, the labor proportion of primary industry is gener-

ally low (Figure 5e).

Agricultural income proportion in the entire rural revenue: In the revenue structure of

rural economy, the greater share of agricultural income is, the simpler the rural economic

structure is, the weaker the economic diversity is, the poorer the marketing and diversified economy develops, the more local farmers depend on agriculture, and the more intensive the

flood vulnerability is. Conversely, the stronger the marketing and diversified economy de-

velops and the more sources the farmers can make income from, then the less constraint the

natural environment imposes on local economy and the more weakly the floods influence

local rural economy and society. For the study area, the average proportion of agricultural

income accounts for 43.43% of the total rural revenue, which indicates that the agriculture is

the main source of rural income. There are 138 townships with this proportion exceeding

40% (Table 5), which accounts for 76.67% of the total number of the townships and 82.70%

of the total agricultural income. And for the townships with this proportion exceeding 50%,

the corresponding numbers are 98, 54.44% and 57.99%, respectively. Spatially, with respect

to the agricultural income proportion, the eastern and northern PLR are higher than the

western and southern, and the urban area is lower than the rural (Figure 5f).

Table 5Grade distribution of major socio-economic variables of the study units in the PLR (2004)

GDP per unit area

Per capita net

income of farmers

Proportion of primary

industry employee

Proportion of

agricultural income

104 yuan/km2Townships

(num.)

yuan

Townships

(num.)

%

Townships

(num.)

%

Townships

(num.)

<100 73 <1500 20 <30 10 <20 14 100–200 60 1500–2000 48 30–50 35 20–40 28 200–500 30 2000–2500 42 50–60 49 40–50 40 500–1000 10 2500–3500 54 60–70 42 50–70 59 >1000 7 >3500 16 >70 44 >70 39

280 Journal of Geographical Sciences 4.2 Spatial integration of farmers’ flood vulnerability

The individual indices for vulnerability analysis are normalized with equations (1) and (2). The above discussed individual indices of township flood risk are integrated to produce new comprehensive vulnerability factor with equation (3), which results in new attribute table for further analysis. Then, the spatial distribution of farmers’ vulnerability to flood is mapped for the whole PLR (Figure 6).

Figure 6 Spatial distribution of farmers’ vulnerability to flooding in the PLR

In general, the flood vulnerability of the eastern PLR is higher than that of the western. The spatial disparity in flood sensitivity and response capability lies in their differences in the land use and socio-economic development. In the PLR, the areas of high flood risk are mainly the middle and lower reaches of the five rivers, where the terrain goes smoothly along the rivers. The fact of frequent floods and undeveloped local economy is both the cause and effect of the high vulnerability to flood. The areas with the lowest flood vulner-ability are the urban and suburban townships. This benefits from their relative favorable economical environment, the developed economy and the strong flood-defending infra-structure. The comparison of Figures 5 and 6 can show that the generally high proportions of rural population, cultivated land, primary industrial employment and agricultural income cause high farmers’ vulnerability to flood, while high proportions of GDP per unit area and net rural income per capita would decrease this vulnerability.

Among the 180 townships of the PLR, the highest vulnerability index is 3.12 while the lowest is 1.01, and the average is 2.50. There are 100 townships whose vulnerability index is above the average level (Table 6). So, the farmers of most townships in the region are ex-posed to high flood vulnerability. Floods pose a serious obstacle to the development of rural economy and the improvement of rural income.

According to the spatial distribution of farmers’ flood vulnerability, the average vulner-ability is chosen as the critical value in the study. If the vulnerability index of one township is above this critical level, the township is thought to be high vulnerable. Otherwise, one township isn’t vulnerable to flood. Based on this critical value, the spatial distribution of flood vulnerability is further classified with histogram (Table 6) in order to compare the

MA Dingguo et al.:Farmers’ vulnerability to flood risk: A case study in the Poyang Lake Region 281 Table 6 Comparison of factors between areas with different values of vulnerability to flooding in the PLR

(2004)

Vulner- ability Town-

ship

Rural

population

proportion

(%)

Cultivated

land pro-

portion (%)

GDP per unit

area (104

yuan/km2)

Net rural

income

per capita

(yuan)

Employment

proportion of

primary in-

dustry (%)

Rural

income

proportion

(%)

<2.0 12 25.50 31.54 655.21 3665 24.57 9.20 2.0–2.3 26 62.71 28.40 225.69 2689 43.53 37.56 2.3–2.5 42 85.25 33.11 139.73 2334 53.94 41.34 2.5–2.8 64 87.42 40.08 164.05 2339 61.83 48.73

>2.8 36 93.01 48.09 84.32 1907 68.92 70.21

characteristics of individual attribute elements under different vulnerability degrees, which would reflect the relative regional difference of flood vulnerability in the study area and would provide scientific information for decision-makers to flood disaster mitigation.

Table 6 is derived by summarizing the vulnerability indicators of different regions, which provides a clear relationship between the flood vulnerability of farmers and different socio-economic variables. In general, the vulnerability level of the PLR townships shows positive relationships with the proportions of rural population, cultivated land area, the pri-mary industrial employment, and negative relationships with the GDP per unit area and net rural income per capita. The influences of the above different socio-economic variables are integrated to express the vulnerability index, which expresses farmers’ general response ca-pability to flood. However, under different flood risks, there may be few situations when one individual indicator does not agree with the general index (i.e., vulnerability class) for some townships. So in the decision-making for defending and mitigating flood disaster, the farmer group with weak response capability to flood should be taken into account, which needs to be provided to assist decreasing the flood risk. At the same time, local specific situations of different townships also should be carefully accounted so as to determine the vulnerable farmer group defined by individual flood risk indicators. This will help to make cus-tomizedly appropriate measures for flood rescues, which practically help farmers to get rid

of poverty and to increase their flood response capability.

5 Conclusions and discussion

Some conclusions are drawn as follows:

(1) The PLR is one of the regions that suffer from the most serious flood disasters in China. The flood affected area is extensive and intensive. Flood disaster results not only from its inherent natural mechanism, but also from the socio-economic activities in the re-gion. The degree of farmers’ vulnerability reflects the severe impact of flood disaster.

(2) The flood vulnerability of farmers is related to several rural socio-economic factors. From the aspect of risk response capability to floods, this paper selected six rural socio-economic factors to define flood risks. At the same time, the influences of different flood levels on local rural economy are also taken into account to quantitatively explore the flood vulnerability degrees for different townships in the PLR. The results show that more

282 Journal of Geographical Sciences than half of the townships have high vulnerability to flood, this reminds us that in the PLR the flood risk is very serious and the task to defend flood disaster is tough but necessary. It should be noted definitely, the degree of local flood vulnerability is by no means the degree of actual flood damage. Undoubtedly, the economically important areas with dense popula-tion call for building strong flood defending infrastructures, in order to improve flood de-fending capability to mitigate flood disaster induced damages. However, for the areas with high vulnerability, flood disasters would bring serious threats or even fatal damages to the local rural production and living, which often results in the uneasily recoverable deteriora-tion of local rural economy. So, this study dedicated to expound how to improve the flood response capability of local farmers so as to reduce the flood vulnerability.

(3) In this paper, the results of quantitative calculation reflect the spatial difference of farmer’s flood vulnerability within these townships, so as to define the vulnerable group to flood and to provide scientific information for decision-making of flood defending and mitigating. For a certain region, as the flood vulnerability will be different using different socio-economic variables and different calculation methods, what is essential is the defini-tion of vulnerability group. The difference in the understanding of the flood vulnerability will lead to the disparity in the final calculation of flood risk. So, it is important to choose and use vulnerability indices both carefully and appropriately for defining the affected groups in flood disaster defending and mitigating.

(4) In the study, the spatial analysis of flood risk is based on the administrative unit of township, which avoids neglecting the interior spatial difference resulting from larger spatial unit and can indicate the spatial characteristics of the farmer’s flood vulnerability in the PLR more accurately.

Acknowledgement: The helps from Mr. Xiong Dakan of Jiangxi Institute of Planning and Design of Wa-ter Resources are appreciated.

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网络安全及网络安全评估的脆弱性分析

网络安全及网络安全评估的脆弱性分析 [摘要]随着计算机网络技术的迅速发展，在共享网络信息的同时，不可避免存在着安全风险，网络安全问题已成为当前网络技术研究的重点。网络安全风险评估技术能够检测网络系统潜在的安全漏洞和脆弱性，评估网络系统的安全状况，是实现网络安全的重要技术之一。 [关键词]计算机网络安全评估脆弱性 中图分类号：TP3 文献标识码：A文章编号：1671－7597 (2008) 0110018－01 随着计算机网络技术的快速发展，全球信息化已成为世界发展的大趋势。在当今的信息社会中，计算机网络在政治、经济、军事、日常生活中发挥着日益重要的作用，从而使人们对计算机网络的依赖性大大加强。现有的计算机网络在建立之初大都忽视安全问题，而且多数都采用TCP/IP协议，TCP/IP协议在设计上具有缺陷，因为TCP/IP协议在设计上力求运行效率，其本身就是造成网络不安全的主要因素。由于计算机网络具有连接形式多样性、开放性、互联性等特点，使网络很容易受到各种各样的攻击，所以当人们充分享受网络所带来的方便和快捷的同时，也应该充分认识到网络安全所面临的严峻考验。 一、网络安全 （一）网络安全的定义 网络安全是指计算机网络系统中的硬件、数据、程序等不会因为无意或恶意的原因而遭到破坏、篡改、泄露，防止非授权的使用或访问，系统能够保持服务的连续性，以及能够可靠的运行。网络安全的具体概念会随着感兴趣角度的不同而不同。从用户的角度来说，他们希望自己的一些绝密信息在网络上传输时能够得到有效的保护，防止一些非法个人通过窃听、篡改、冒充等手段对用户的绝密信息进行破坏。 从网络安全管理员来说，他们希望本地网络信息的访问、读写等操作能够得到有效的保护和控制，避免出现拒绝服务、资源非法占用、非法控制等威胁，能够有效地防御黑客的攻击。对于国家的一些机密部门，他们希望能够过滤一些非法、有害的信息，同时防止机密信息外泄，从而尽可能地避免或减少对社会和国家的危害。网络安全既涉及技术，又涉及管理方面。技术方面主要针对外部非法入侵者的攻击，而管理方面主要针对内部人员的管理，这两方面相互补充、缺一不可。 （二）网络安全的基本要求 1.机密性（Confidentiality）它是指网络中的数据、程序等信息不会泄露给非授权的用户或实体。即信息只能够被授权的用户所使用，它是保护网络系统安全的重要手段。完整性（Integrity）它是指网络中的数据、程序等信息未经授权保持不变的特性。即当网络中的数据、程序等信息在传输过程不会被篡改、删除、伪造、重放等破坏。可用性（Availability）它是指当网络中的信息可以被授权用户或实体访问，并且可以根据需要使用的特性。即网络信息服务在需要时，准许授权用户或实体使用，或者当网络部分受到破坏需要降级使用时，仍可以为授权用户或实体提供有效的服务。可靠性（Reliablity）它是指网络系统能够在特定的时间和特定的条件下完成特定功能的特性。可靠性是网络系统安全最基本的要求。可控性（Controllablity）它是指对网络信息的传播和内容具有控制能力的特性。它可以保证对网络信息进行安全监控。 6.不可抵赖性（Non-Repudiation）它是指在网络系统的信息交互过程中，确认参与者身份的真实性。它可以保证发送方无法对他发送的信息进行否认，并且可以通过数字取证、证据保全，使公证方可以方便地介入，通过法律来管理网络。 二、网络安全评估中的脆弱性研究

新版名校专递：高考地理特色专题讲练(11)河流洪涝灾害的成因分析(含答案)

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常用分析化学专业英语词汇

常用分析化学专业英语词汇

常用分析化学专业英语词汇absorbance 吸光度absorbent 吸附剂absorption curve 吸收曲线absorption peak 吸收峰absorptivity 吸收系数accident error 偶然误差accuracy 准确度 acid-base titration 酸碱滴定acidic effective coefficient 酸效应系数 acidic effective curve 酸效应曲线 acidity constant 酸度常数activity 活度 activity coefficient 活度系数adsorption 吸附 adsorption indicator 吸附指示剂 affinity 亲和力 aging 陈化 amorphous precipitate 无定形沉淀 amphiprotic solvent 两性溶剂amphoteric substance 两性物质 amplification reaction 放大反应 analytical balance 分析天平analytical chemistry 分析化学 analytical concentration 分析浓度 analytical reagent (AR) 分析试剂 apparent formation constant 表观形成常数 aqueous phase 水相argentimetry 银量法 ashing 灰化 atomic spectrum 原子光谱autoprotolysis constant 质子自递常数 auxochrome group 助色团

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