Front. Earth Sci. China 2009, 3(4): 419–430 DOI 10.1007/s11707-009-0054-0
RESEARCH ARTICLE
Study on sustainable water use of the Haihe River Basin using ecological network analysis Yuan LI, Bin CHEN (✉) State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing 100875, China
© Higher Education Press and Springer-Verlag 2009
Abstract Sustainable water use of the Haihe River Basin is studied by using the ecological network analysis (ENA) approach. Two related aspects including socioeconomic and environmental water uses sustainability and network organization inherent in system structures are analyzed. For the study of sustainable water use from each single aspect including water use intensity, water use pressure, and environmental protection, a series of new indicators termed as total system throughput water use intensity (TSTUI), total system throughput pressure (TSTP), and environmental flow indicator (EFI) are set up by incorporating parameters of GDP, population, and environmental flow. Based on these three indices, a new integrated index, intensity-pressure-environment (IPE) is established for synthesized measure of sustainable socioeconomic and environmental water uses. The indices of ascendency and overhead are applied for analyzing and characterizing water use network organization. The four subbasins of the Haihe River during 1999–2002 and 2005– 2007 are studied. The results show that (i) the water use intensity in subbasin II is the best, while that in subbasins I and III are the worst; (ii) subbasin II and subbasins I and III suffer the highest and lowest water use pressure, respectively; (iii) the environmental flow situations in subbasins II and III are the worst and that of subbasin I is the best; (iv) as for the integrated socioeconomic and environmental water uses sustainability, subbasin III is the best, and subbasins I and IV are the worst; (v) the organization level of subbasin I is better than the others’, in which that of subbasin IV is the worst. It can be concluded that the application of ENA in sustainable water use study can provide new angles for water resources management to address the challenges of assessing and optimizing options to obtain more sustainable water use. Keywords ecological network analysis, sustainable water use, environmental flow, the Haihe River Basin Received March 1, 2009; accepted August 1, 2009 E-mail:
[email protected]
1
Introduction
The Haihe River is the largest river in North China. In recent years, water supply decreases sharply in the Haihe River Basin, due to continuous drought, uneven precipitation distribution, and degradation of the water resources. On the other hand, with rapid economic development, population growth, and living standards improvement, there has been an ever-increasing demand for water resources. Because of the decreased supply and increased demand, the problem of water shortage has greatly limited the sustainable development in this area. Concerning the rigid water use situation, the research on sustainable water use of the Haihe River Basin is essential and profound in order to use water in a sustainable way and protect the environment. The sustainable water use is an issue serving the purpose of providing water for household, industrial, commercial, and institutional uses and removing wastewater from human settlements for environmental and ecological protection. Beyond the socioeconomic character, the problem of sustainable water use is at the interface between socioeconomic factors and the environment with an ecological feature. Therefore, water use in human settlements is particularly complex, and its sustainable development is complicated in that water is withdrawn, imported, distributed, used, and discharged in different ways according to the multiple activities (Bodini and Bondavalli, 2002), and each water exchange activity directly and indirectly depends on and is affected by other activities both in behavior and function. Regarding the complexity feature of the sustainable water use problem, a systematic approach is required for studying from a systematic perspective. Ecological network analysis (ENA) is a systemsoriented modeling technique to represent material, energy, and information within an ecosystem as a network of nodes (compartments, components, storages, etc.) and connections (links, flows, etc.) (Patten et al., 1976; Ulanowicz, 1986; Wulff et al., 1989; Christensen and Pauly, 1993; Fath
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Front. Earth Sci. China 2009, 3(4): 419–430
and Patten, 1999), which places greater emphasis on the transfers between nodes rather than the characteristics of individual nodes (Ulanowicz, 1986) and identifies and quantifies the direct and indirect effects in that system (Fath and Patten, 1999; Fath and Halnes, 2007; Fath, 2007; Borrett et al., 2007). For a system that can be pictorially described as a web structure, a collection of boxes connected by arrows that describe exchanges of media that are necessary to make the system functional, ENA serves as a promising approach. As water use in human settlement can be described as a network structure, the approach of ENA has been applied as a potential technique to study the issue of sustainable water use (Bodini and Bondavalli, 2002; Li et al., 2009a, b). In a previous study (Li et al., 2009a) on sustainable water use of the Yellow River Basin, a new index termed as total system throughput intensity (TSTI) was established to measure water use sustainability in view of synthesized water use intensity covering elements of society, economy, and environment. Although TSTI can be served as a potential integrated water use sustainability measure, there are still some problems that need to be studied. According to the construction of TSTI, the water use intensity is represented by the ratio of total system throughput (TST) to gross domestic production (GDP), and the factors of water resources pressure and environment flow are defined as the coefficients to modify water use intensity. Therefore, the TSTI is a synthesized water use intensity-dominated index and is unable to examine water resources pressure and environmental flow situation, separately. One approach to overcoming this limitation is
to set up a single index for quantifying water resources pressure and environmental flow state, respectively. Therefore, in this paper, based on the index of TSTI, we propose a series of new index combining ENA index with socioeconomic and environmental flow factors to measure sustainable water use from single and integrated perspectives. In addition, the network organization analysis for sustainable water use study measured by ascendency (Ulanowicz, 1986, 1997) is included in this paper. This paper presents an extension of the application of ecological network theory in issue of sustainable water use. The objectives of this study are to analyze sustainable water use of the Haihe River Basin from the perspectives of socioeconomic and environmental water use situations with limited water resources and network organization. These objectives are approached in the following sections: Section 2 describes the study site. Section 3 emphasizes on methodology including network model and analysis indices. Section 4 illustrates the analysis results of the cases. Section 5 discusses the results. Finally, Section 6 offers the conclusions.
2
Study site
The Haihe River (Fig. 1) is the largest river in North China and formed by five large rivers including the North Canal, the Yongding, the Daqing, the Ziya, and the South Canal. It originates from the Weihe River and flows into the Bohai Gulf of the Yellow Sea, with a length of 1329 km and a drainage area of approximately 319000 km2, covering
Fig. 1 Map of the Haihe River Basin
Yuan LI et al. Sustainable water use of the Haihe River Basin using ecological network analysis
Beijing, Tianjin, the most of area of Hebei, parts of Shandong, Henan, Shanxi, and Inner Mongolia. The climate of the basin is characterized by semi-arid with cold dry winter and hot rainy summer. With an average annual precipitation of 535 mm, the Haihe River Basin is regarded as the region with the smallest rainfall in the eastern coast of China. Covering two important cities, Beijing and Tianjin, the Haihe River Basin is an important industrial and high-technology production base, playing a critical role in national, economic, and social development. In 2007, the total population of this area was 133.74 million, holding a large density of population and a high rate of urbanization. The GDP in 2007 was 3539.7 billion yuan, with an average GDP of 26467 yuan/capita, which was greatly higher than the national average GDP of 18665 yuan/capita. In this paper, the Haihe River Basin is divided into four subbasins based on the natural and hydrological conditions of river sections (CHRBWR, 1999–2002 and 2005–2007), i.e., the Luan River and Eastern Hebei coast (subbasin I), the North Region of the Haihe River (subbasin II), the South Region of the Haihe River (subbasin III), and Tuhaimajia River (subbasin IV). The primary social and economic situations covering GDP, population, and total water resources amount of the four subbasins during 1999– 2002 and 2005–2007 are listed in Table 1. Because the water use data in 2003 and 2004 are absent, these two years are not included in the studying periods.
Table 1 Socioeconomic and water resources situations of the four subbasins of the Haihe River
3.1
Methods Network model description
According to water use network model in the authors’ previous study (Li et al., 2009a), a nine-compartment steady-state network model is developed to represent water flows (see Fig. 2), with the nodes described in Table 2. As shown in Fig. 2, flows, fij, represent statistic flows (m3/a) of water from compartment i to compartment j; zk and yk are boundary input (m3/a) and output (m3/a) of the kth compartment, respectively; Compartment 1 denotes the streams of the system, z11 refers to precipitation, and z12 and y11 are surface runoff from upstream and to downstream, respectively; and y12 is the changed volume of reservoir water; y13 represents the quantities evaporation. In this study, because the data of runoff from upstream and to downstream and evaporation are absent, the flows in compartment streams are simplified using z11 to represent input (z1) from environment to streams; the output (y1) of streams is calculated through mass balance of Compartment 1. Compartment 2 receives the inflow of water from streams and then distributes them to compartments of family, agriculture, landscape, and industry. The volume of water consumed by different users, including family, agriculture, landscape, and industry are expressed
total water resources/108 m3
subbasin
year
I
1999
1777.98
1248.96
33.65
2000
1787.59
1362.28
30.30
2001
1800.56
1497.06
36.90
2002
1809.89
1632.15
24.17
2005
1888.00
2723.00
46.63
2006
1852.97
3135.57
30.93
2007
1865.12
3747.91
33.35
1999
3256.96
4278.07
46.15
2000
3383.38
4915.50
47.60
2001
3415.56
5635.30
51.60
2002
3459.23
6431.19
41.74
2005
3626.00
10433.00
56.58
2006
3674.27
12096.77
54.00
2007
3753.01
14341.92
56.98
1999
5565.34
4164.73
95.76
2000
5634.73
4626.03
149.00
2001
5675.45
5133.57
94.00
2002
5710.17
5672.01
85.17
2005
5644.00
9576.00
124.60
2006
5880.63
11036.33
111.60
2007
5936.75
13147.31
127.26
1999
1723.97
1202.92
18.21
2000
1746.22
1345.62
41.80
2001
1756.57
1480.54
17.60
2002
1766.09
1661.95
7.06
2005
2261.00
3018.00
39.66
2006
1809.40
3514.08
23.23
2007
1819.94
4160.51
30.29
II
III
IV
3
421
4
8
population/10 cap GDP/10 yuan
Note: Data source: 1) CHRBWR, 1999–2002 and 2005–2007; 2) Yearbooks published by official statistical bureaus of Hebei, Shanxi, Henan, Shandong, Inner Mongolia, and Liaoning provinces, 1999–2002 and 2005–2007; and 3) Water Resources Assessment of the Haihe River Basin
as y3, y4, y5, and y6, respectively. The volume not only includes the part that is consumed by water users but also the loss proportion. Purification system I and II receive part of the municipal and industrial wastewater and then return them into the streams. Another part of the municipal and industrial wastewater that is not purified is directly discharged into the streams. Because there are few treatments for the agricultural wastewater, the throughflows from a compartment of agriculture to purification systems are therefore not considered. Thus, the wastewater of agriculture is discharged directly into the streams. Also, since the storage volume is not involved in the ascendency analysis, the relative data are not listed in this study. In summary, the network that is explored contains a total of 21 flows, which includes one input into node 1, five
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Front. Earth Sci. China 2009, 3(4): 419–430
Fig. 2 Water use network model. (a) Flows in node streams; (b) flows of water use network Table 2 Description of compartment in water use network model compartment 1 streams 2 water distribution system (WDS) 3 family 4 agriculture 5 landscape 6 industry
Socioeconomic and environmental water uses analysis
description surface and ground water bodies located in the basin. public enterprise that distributes surface water to the entire area water used by family water uses for irrigation water uses for landscape water used by industrial activities
7 water purification system I (WPS I)
treatment facilities cleaning up domestic sewage
8 water purification system II (WPS II)
treatment facilities cleaning up industrial wastewater
9 water reused system (WRS)
3.2
ground water used by industrial activities
outputs from node streams and four nodes relating with water consumption, and 15 interflows between those nine nodes of the network, as listed in Table 3. The data of network, fij, zk, and yk are obtained from the CHRBWR (1999–2002, 2005–2007) issued by the Committee of the Haihe River and yearbooks published by the official statistical bureaus of Hebei, Shanxi, Henan, Shandong, Inner Mongolia, and Liaoning Provinces.
Regarding water use systems as integrated systems including environmental, social, and economic elements, when it comes to the intensity of water use from sustainability perspective, the compound influences of critical environmental, social, and economic factors should be covered. Total system throughput (TST), which measures the size or growth of the system in terms of the flows through all its compartments, can be considered as the carrier of socioeconomic development. Besides its characteristics related with network organization, TST is significantly influenced by the balance of water demand and supply, which is decided by the socioeconomic status and correlated with natural gift for water use systems. Therefore, to identify the size and growth of water use systems in the context of socioeconomic and natural gift conditions, we created an index in our previous study (Li et al., 2009a), which is represented as TSTI ¼ α$β$ α¼
P , Q
β¼
We , Wa
TST , GDP (1)
Yuan LI et al. Sustainable water use of the Haihe River Basin using ecological network analysis
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Table 3 Flows of water use networks of the four subbasins of the Haihe River during 1999–2002 and 2005–2007 sub-basin I
type
designation
1999
2000
2001
2002
2005
2006
2007
input
F(0, 1)
220.89
222.20
260.70
203.20
274.88
231.57
251.10
outputs
F(1, 0)
191.48
194.09
235.59
175.24
249.53
204.85
224.51
F(3, 0)
2.84
3.01
2.73
2.53
2.06
2.57
2.68
F(4, 0)
24.03
22.32
19.60
22.69
19.79
20.29
20.00
F(5, 0)
0.00
0.00
0.00
0.00
0.08
0.07
0.07
F(6, 0)
2.54
2.78
2.78
2.75
3.35
3.71
3.79
F(1, 2)
49.22
43.30
52.60
39.43
63.58
42.52
45.59
internal interactions
II
F(2, 3)
5.20
5.50
5.00
4.62
3.76
4.70
4.90
F(2, 4)
30.90
28.70
25.20
29.17
25.45
26.09
25.72
F(2, 5)
0
0
0
0
0.15
0.13
0.12
F(2, 6)
5.20
5.70
5.70
5.64
6.86
7.59
7.77
F(3, 1)
0.71
0.75
0.68
0.63
0.51
0.64
0.67
F(3, 7)
1.65
1.75
1.59
1.47
1.19
1.49
1.55
F(4, 1)
6.87
6.38
5.60
6.48
5.66
5.80
5.72
F(5, 1)
0.00
0.00
0.00
0.00
0.07
0.06
0.05
F(6, 1)
0.13
0.15
0.15
0.14
0.18
0.19
0.20
F(6, 8)
2.53
2.77
2.77
2.74
3.34
3.69
3.78
F(6, 9)
2.26
2.48
2.48
2.45
2.98
3.30
3.38
F(7, 1)
1.65
1.75
1.59
1.47
1.19
1.49
1.55
F(8, 1)
2.53
2.77
2.77
2.74
3.34
3.69
3.78
F(9, 6)
2.26
2.48
2.48
2.45
2.98
3.30
3.38
input
F(0, 1)
280.67
324.80
316.50
310.80
349.22
333.83
362.53
outputs
F(1, 0)
219.92
269.60
261.30
258.39
293.46
278.27
306.42
F(3, 0)
9.62
9.68
9.13
8.93
9.59
10.75
10.75
F(4, 0)
42.93
39.35
39.04
36.86
38.20
36.43
36.54
F(5, 0)
0.00
0.00
0.00
0.00
0.73
1.06
1.62
F(6, 0)
8.20
7.91
7.03
6.62
6.64
6.44
5.84
F(1, 2)
89.60
84.50
81.30
77.29
81.60
81.64
81.59
F(2, 3)
17.60
17.70
16.70
16.34
17.55
19.67
19.67
F(2, 4)
55.20
50.60
50.20
47.39
49.12
46.84
46.99
1.33
1.93
2.97
internal interactions
F(2, 5)
0
0
0
0
F(2, 6)
16.80
16.20
14.40
13.56
13.60
13.20
11.96
F(3, 1)
2.39
2.41
2.27
2.22
2.39
2.67
2.67
F(3, 7)
5.58
5.62
5.30
5.18
5.57
6.24
6.24
F(4, 1)
12.27
11.25
11.16
10.53
10.92
10.41
10.45
F(5, 1)
0.00
0.00
0.00
0.00
0.60
0.87
1.35
F(6, 1)
0.43
0.41
0.37
0.35
0.35
0.34
0.31
F(6, 8)
8.17
7.88
7.00
6.59
6.61
6.42
5.82
F(6, 9)
7.31
7.05
6.26
5.90
5.92
5.74
5.20
F(7, 1)
5.58
5.62
5.30
5.18
5.57
6.24
6.24
F(8, 1)
8.17
7.88
7.00
6.59
6.61
6.42
5.82
F(9, 6)
7.31
7.05
6.26
5.90
5.92
5.74
5.20
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Front. Earth Sci. China 2009, 3(4): 419–430
(Continued) sub-basin
type
designation
1999
2000
2001
2002
2005
2006
2007
III
input
F(0, 1)
588.51
833.60
616.00
661.75
738.36
684.88
758.64
outputs
F(1, 0)
434.76
691.47
471.20
513.12
601.12
547.64
621.35
F(3, 0)
13.18
13.18
13.23
13.72
12.95
13.94
13.85
F(4, 0)
122.41
112.46
115.41
115.40
107.63
108.33
108.15
internal interactions
IV
F(5, 0)
0.00
0.00
0.00
0.00
0.90
0.96
1.24
F(6, 0)
18.16
16.50
16.16
19.52
15.02
15.04
13.01
F(1, 2)
218.70
202.50
205.70
213.45
194.49
197.34
193.33
F(2, 3)
24.10
24.10
24.20
25.09
23.68
25.49
25.34
F(2, 4)
157.40
144.60
148.40
148.38
138.40
139.29
139.07
F(2, 5)
0
0
0
0
1.64
1.75
2.27
F(2, 6)
37.20
33.80
33.10
39.98
30.77
30.81
26.65
F(3, 1)
3.28
3.28
3.29
3.41
3.22
3.47
3.45
F(3, 7)
7.65
7.65
7.68
7.96
7.51
8.09
8.04
F(4, 1)
34.99
32.14
32.99
32.98
30.77
30.96
30.92
F(5, 1)
0.00
0.00
0.00
0.00
0.74
0.79
1.03
F(6, 1)
0.95
0.86
0.85
1.02
0.79
0.79
0.68
F(6, 8)
18.09
16.43
16.09
19.44
14.96
14.98
12.96
F(6, 9)
16.18
14.70
14.40
17.39
13.39
13.40
11.59
F(7, 1)
7.65
7.65
7.68
7.96
7.51
8.09
8.04
F(8, 1)
18.09
16.43
16.09
19.44
14.96
14.98
12.96
F(9, 6)
16.18
14.70
14.40
17.39
13.39
13.40
11.59
input
F(0, 1)
134.38
178.70
131.60
98.06
196.08
152.23
175.25
outputs
F(1, 0)
77.26
127.11
81.76
42.34
143.85
96.65
122.82
F(3, 0)
2.57
2.46
3.17
3.01
2.74
3.66
3.52
F(4, 0)
49.62
44.25
42.23
47.84
45.48
48.60
44.79
F(5, 0)
0.00
0.00
0.00
0.00
0.40
0.42
0.74
F(6, 0)
4.93
4.88
4.44
4.87
3.29
2.56
2.77
F(1, 2)
78.60
81.58
69.20
76.99
80.00
75.19
76.38
F(2, 3)
4.70
4.50
5.80
5.50
5.01
6.69
6.43
F(2, 4)
63.80
56.90
54.30
61.52
internal interactions
F(2, 5)
0
0
58.48
62.49
57.59
0
0
0.73
0.77
1.35
F(2, 6)
10.10
10.00
9.10
9.97
6.73
5.24
5.68
F(3, 1)
0.64
0.61
0.79
0.75
0.68
0.91
0.87
F(3, 7)
1.49
1.43
1.84
1.75
1.59
2.12
2.04
F(4, 1)
14.18
12.65
12.07
13.68
13.00
13.89
12.80
F(5, 1)
0.00
0.00
0.00
0.00
0.33
0.35
0.61
F(6, 1)
0.26
0.26
0.23
0.26
0.17
0.13
0.15
F(6, 8)
4.91
4.86
4.42
4.85
3.27
2.55
2.76
F(6, 9)
4.39
4.35
3.96
4.34
2.93
2.28
2.47
F(7, 1)
1.49
1.43
1.84
1.75
1.59
2.12
2.04
F(8, 1)
4.91
4.86
4.42
4.85
3.27
2.55
2.76
F(9, 6)
4.39
4.35
3.96
4.34
2.93
2.28
2.47
Yuan LI et al. Sustainable water use of the Haihe River Basin using ecological network analysis
where Q and P are total water resources amount (m3) and population (capita), respectively. The units of TST and TST GDP are m3 and yuan, respectively. The ratio of GDP reflects the amount of water flows per GDP value. β is defined as the environmental flow coefficient by ratio of minimum environmental flow (We) to actual environmental flow (Wa). α and β are dimensionless coefficients, indicating respectively the water resource pressure and environmental flow effects. In this paper, in order to represent water use intensity, water resources pressure, and environmental flow situation separately, three new indices are proposed referencing TSTI, represented as TSTUI ¼
TST , GDP
(2)
TSTP ¼
TST , P
(3)
We , Wa
(4)
EFI ¼
where TSTUI, TSTP, and EFI are defined as total system throughput use intensity (m3$10–4 yuan–1), total system throughput pressure (m3/cap), and environmental flow indicator, respectively, of which the index of TSTUI represents water use intensity; TSTP indicates water resources pressure; and EFI implies the environmental flow level. Furthermore, to better analyze sustainability from complicated perspectives of TSTUI, TSTP, and EFI, the results of these three values should be normalized considering their different unit. In this study, these results are normalized into interval of 0 to 1. According to their positive or negative properties, they are combined as an integrated index named as “intensity-pressureenvironment” (IPE) with the form as IPE ¼ TSTUI# þ ð1 – TSTP# Þ þ EFI# ,
(5)
where TSTUI′, TSTP′, and EFI′ are normalized values of TSTUI, TSTP, and EFI, respectively. Because the detailed information about environmental flow for the Haihe River Basin during 1999–2002 and 2005–2007 is absent, the data of minimum environmental flow are determined here referencing to the situations of exploitable water resource amount. The detailed calculation of exploitable water resources amount can be referred to the results of Ren (2007), in which it was defined as the maximum water amount that can be extracted according to socioeconomic and environmental criteria. The values of We and Wa of each subbasin during 1999–2002 and 2005– 2007 are listed in Table 4.
3.3
425
Ascendency analysis
Information indices are global attributes of the network, which embed structural characteristics, such as ascendency, development capacity, overheads, and redundancy (Ulanowicz, 1980; Ulanowicz and Norden, 1990). Rutledge et al. (1976) applied an index of information theory to ecological network in terms of average mutual information (AMI), which is the average constraint placed upon a single unit of flow in the network. Based on AMI and TST, Ulanowicz developed the ascendancy as a measure of the network’s potential for competitive advantage over other network configurations, encompassing the natural growth and development of ecological system, and asserted that it increases during the development of an ecosystem from an early undeveloped successional stage to a climax stage with the function of quantifying feedback and system size (Ulanowicz, 1989, 1997, 1998; Ulanowicz and Wolff, 1990). The upper limit to ascendency is the development capacity, and the difference between the capacity and the ascendency is termed as system overhead (Ulanowicz, 1986), which represents multiplicity of pathways. Consequently, the overhead will be high if the system is under rigorous environmental conditions (Ulanowicz and Norden, 1990). Recently, the researches on information theory in ecosystems sustainability issues have been developed. Information theory quantifying the complexity of systems embraced into ENA (Zorach and Ulanowicz, 2003) suggested that the complex structure is the secret of its sustainability (Ho and Ulanowicz, 2005), and the diversity of processes plays a crucial role in whether a system survives or disappears (Ulanowicz et al., 2009). For ecosystems, while the ability of which to persist over the long run depends largely on the layout and magnitudes of the trophic pathways by which energy, information, and materials are circulated, the network structure measured by ascendency has been argued as one of the important factors affecting system’s long-term sustainability. In this view, the ecosystem’s long term sustainability requires a particular network structure that is densely connected, efficient enough, and diverse enough, which indicates a balance between efficiency and diversity. Therefore, as water use systems are described with network structure, it seems reasonable to assume that this principle holds for water use systems as structure ecosystems. With regard to the artificial character of pathways between water suppliers, users, and treatments, the efficiency of pathway measured by ascendancy is employed here without considering the pathway diversity. In this sense, by comparisons of ascendency across different water use systems, the network organization sustainability in view of ascendency is analyzed, and the efficiencies of pathway connection, water importing and exporting, water consumption, and water transportation are described as a result.
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Table 4 We and Wa values for the Haihe River Basin during 1999–2002 and 2005–2007 sub-basin I
II
item
1999
2000
2001
2002
2005
2006
2007
surface water amounts
23.70
18.60
26.40
13.09
33.50
16.21
18.59
groundwater amounts
24.99
24.20
25.70
18.96
29.95
26.17
26.86
surface water used amounts
19.08
15.60
10.90
14.92
12.37
13.37
13.10
groundwater used amounts
21.69
23.80
24.50
24.07
23.72
25.00
25.27
Wa
7.92
3.40
16.70
27.36
4.01
We
23.54
20.68
25.2
22.42
29.93
20.34
21.84
surface water amounts
22.15
22.10
24.50
16.76
23.65
22.64
22.19
groundwater amounts
39.90
41.70
42.50
36.34
46.14
44.52
46.74
surface water used amounts
35.38
30.40
26.10
24.01
26.46
25.21
24.47
groundwater used amounts
53.98
54.20
55.20
52.35
52.33
52.25
52.17
Wa
III
IV
0
0
0
0
0
0
0
0
0
We
44.08
37.91
32.53
37.16
27.61
26.25
22.88
surface water amounts
41.77
72.10
35.00
32.39
52.30
21.53
53.01
groundwater amounts
85.88
120.80
86.10
78.07
106.00
92.97
108.37
surface water used amounts
47.37
43.09
41.85
37.20
38.84
39.16
39.95
groundwater used amounts
165.09
154.70
159.30
163.55
149.75
151.54
148.21
Wa
0
0
0
0
0
0
We
120.56
62.1
112.33
119.89
74.8
71.8
surface water amounts
21.50
12.30
3.80
0.97
12.86
5.92
8.00
groundwater amounts
7.68
35.40
20.30
12.89
33.38
25.48
29.98
surface water used amounts
5.99
7.52
5.45
5.47
9.98
10.16
8.28
groundwater used amounts
27.96
30.00
29.00
30.19
27.21
23.06
24.37
Wa We
0 19.37
10.18 14.61
0
0
16.75
24.99
9.05 13.81
0 69.78
0
5.33
9.79
9.56
Note: Data source: 1) CHRBWR, 1999–2002 and 2005–2007; and 2) Water Resources Assessment of the Haihe River Basin
4
Results
4.1
Socioeconomic and environmental water uses analysis
4.1.1
development level for subbasin II. Also, the changes of TSTUI for each subbasin during this period of years suggest that the water use intensity of the Haihe River Basin has been improved.
TSTUI 4.1.2
The calculation results of TSTUI (Fig. 3) show that water use intensity of subbasins II is the best, and subbasins I and III are the lowest ones from perspective of socioeconomic water use intensity. It indicates a better socioeconomic
TSTP
The results of TSTP (Fig. 4) suggest an opposite sustainability rank of these four subbasins from the results of TSTUI. In view of water resources pressure, subbasins I
Yuan LI et al. Sustainable water use of the Haihe River Basin using ecological network analysis
427
Fig. 3 TSTUI of the four subbasins of the Haihe River during 1999–2002 and 2005–2007
Fig. 4 TSTP of the four subbasins of the Haihe River during 1999–2002 and 2005–2007
and III are the most sustainable systems, and subbasin II becomes the most unsustainable one. The systems with higher sustainability measuring by water use intensity use water unsustainably from the point of water resources pressure. It indicates that the areas with high developed society and economy level commonly suffer water resources pressure due to their dense population.
4.1.3
EFI
As shown in Fig. 5, the environmental flow situation of subbasin I is the best one. Because of the serious water overabstraction in subbasins II and III, the environmental flows in these two areas are zero. As a result, EFI of subbasins II and III are zero.
Fig. 5 EFI of the four subbasins of the Haihe River during 1999–2002 and 2005–2007
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sewage disposal rate, e.g., flows from Compartment 3 to 7, flows from Compartment 6 to 8, and also flows from Compartment 6 to 9 to enforce water reuse.
IPE
Integrating TSTUI, TSTP, and EFI together, the results of IPE are shown in Fig. 6. The complicated results suggest that subbasin III is the most sustainable one, and subbasins I and IV are the worst ones. The reductions of IPE across these years imply an increase of water use sustainability for the Haihe River Basin. The rank of sustainability for the four subbasins in each year is shown in Table 5. 4.2
5
Discussion
The new indices proposed in this paper can be considered as a possible sustainability metric in terms of sustainable water use covering elements of environment, society, and economy. Based on the network throughputs, population and GDP are incorporated to modify the value of TST. The indices of TSTUI and TSTP represent the total throughput of the water use network per unit GDP and per unit capita, respectively, revealing the intensity of water flows associated with the economic activities of the human society and water resources pressure. The other index of EFI is employed to express the environmental flow situation from the aspect of environmental flow. The synthesized index, IPE, shapes the former single indicator into a new and tentative one representing complicated water use sustainability and connecting the ENA with other metrics associated with sustainability issues. The limitation of this study is that environmental flow as the storage volume maintained in the streams, though reflected in the constructions of EFI and IPE, has not been combined into the network model as a stream node.
Ascendency analysis
As can be seen in Fig. 7, the ascendency varies from 63% to 73% of the development capacity and the overhead pictured changes between 26% and 37%, implying a well organized artificial water use systems compared with natural system, e.g., the northern Benguela ecosystem, which is 42%, 24%, and 32% in the 1970s, 1980s, and 1990s, respectively (Heymans et al., 2004). In the four subbasins, subbasin I is better than the other three subbasins, and subbasin IV is the worst from the perspective of network structure efficiency. To further increase the organization level of water use network in the Haihe River Basin, there is a requirement to decrease water leakage during the transportation from WDS to water users for the reduction of dissipation export and promote cycling through increment in flows, which indicates a growth in
Fig. 6 IPE of the four subbasins of the Haihe River during 1999–2002 and 2005–2007 Table 5 Rank of sustainability measured by IPE for the four subbasins during 1999–2002 and 2005–2007 year
subbasin I
subbasin II
subbasin III
subbasin IV
1999
④
①
②
③
2000
③
②
①
④
2001
④
①
②
③
2002
③
②
①
④
2005
④
②
①
③
2006
④
②
①
③
2007
②
③
①
④
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Fig. 7 Information indices of the four subbasins of the Haihe River during 1999–2002 and 2005–2007. (a) Ascendency; (b) overhead
6
Conclusions
The present study intends to study the sustainable water use of the Haihe River Basin with the application of ENA. Two aspects of sustainable water use, including socioeconomic and environmental water using sustainability and network organization, are analyzed through a series of new indices and information indices. The socioeconomic and environmental water uses are quantified by TSTUI, TSTP, and EFI, and the synthesized sustainability is measured by IPE, which imposes additional constraints in a larger ecological context on the development goal of water use systems used to be considered just for human needs. Through the analyses of ascendency and overhead, the sustainability of water use from the perspective of ecosystem network organization is measured. The results of ascendency analysis show a relative higher organization level of water use systems than those of natural ecosystems, of which four systems are ranked by ascendency to represent the network organization levels. This study with ENA approach and those new indices can be served as an attempt to capture system-level characteristics of sustainable water use with the promising ENA methodology. The results can be helpful for the assessment, regulation, and management toward sustainable water use. Basing from our previous study (Li et al.,
2009a), which is the first combination of ENA index with socioeconomic and environmental factors, this study improved the sustainability index that proposed for synthesized water use intensity to allow a more detailed representation of water use sustainability from every single aspect. Since socioeconomic and environmental water use sustainability and network organization are two sides of the sustainable water use evaluation, it is necessary to study how to regulate these two items in future works. Acknowledgements This work was supported by the National Science Foundation for Distinguished Young Scholars (50625926), the National Basic Research Program of China (973) (No. 2006CB403303), and the National Natural Science Foundation of China (Grant No. 40701023).
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