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Vulnerability and adaptation to climate change in North China: the water sector in Tianjin Yuan Zhou Research unit Sustainability and Global Change, Hamburg University and Centre for Marine and Atmospheric Science, Hamburg, Germany Email: [email protected]

Abstract Climate change poses great challenge for the water sector in North China. This study takes Tianjin as a case to assess the vulnerability of the water sector to climate change and to identify the opportunities in adaptations. The socioeconomic and institutional context is firstly discussed, followed by analysis of water resources and adaptive management. A composite index consisting of sensitivity and coping capacity is developed to measure vulnerability of Tianjin. The results suggest that the vulnerability fluctuates over time and Tianjin’s water sector is becoming slightly more vulnerable. A sectoral analysis and district analysis are conducted to address the differential vulnerability across water users and across districts. In terms of future vulnerability water shortage is considered as important factor and is projected into the future. Finally the adaptation options for a short term are identified for the main stakeholders in the water sector. Keywords: water sector, climate change, vulnerability, adaptation, water shortage 1. Introduction There is broad agreement that climate change will have major impacts on water resources. Possible impacts include shifts in means of temperature, precipitation and evaporation, sea level rise and increased variability (McCarthy et al., 2001). Warmer temperatures will accelerate the hydrological cycle, altering precipitation, the magnitude and timing of runoff, and the intensity and frequency of floods and droughts (Frederick and Major, 1997). While water users and managers always have had to cope with climate, climate change presents a different kind of challenge. Most importantly, planning responses to climate change in the water sector is difficult because considerable uncertainty still exists regarding the timing and magnitude of predicted changes (Frederick and Major, 1997). North China including Beijing, Tianjin, the provinces of Hebei, Henan, Shandong, Shanxi, parts of Anhui province, and parts of Inner Mongolia are identified to be more sensitive and vulnerable to climate change (Watson et al., 1997). There are several contributing factors. Firstly these areas have water of merely 500 m3 per capita and have been experiencing water shortage since years. Consequently water is over-extracted in both rivers and from the underground. Secondly, based on the different scenarios used for GCMs (Global Circulation Models), changes in runoff could range between -16% and +7% in this area and these shifts would occur mainly during the flood season (Watson et al., 1997). Although water shortages resulting from climate change are likely to be less important than those caused by other factors, the potential water deficiency due to climate change in moderately and extremely dry years may seriously exacerbate the current water shortage and thus badly affect socio-economic development in this 1

region. In addition, Ying (2000) studies the impacts of global climate change on China’s water resources suggesting that the water resources systems in North China are more vulnerable and sensitive to climate change irrespective of drought. Based on climate scenarios, the results show that the increase in annual mean runoff will surpass 6% in northeastern area, creating flood control problems in Songhuajiang and Liaohe rivers. In other regions, runoff will decrease by 1.4-10.5%, reaching 10.5% in the Huaihe River. The decrease in runoff is estimated to be 4.4% in Beijing-Tianjin-Tangshan area, which is likely to aggravate water shortage in this area. Most of the assessments are conducted on a river basin scale with support from climate and water balance models. Human dimension of climate change and the interactions between human and environment are largely neglected in those studies. This paper, however, seeks to assess vulnerability of the water sector in the municipality of Tianjin by analysing the humanenvironment system in an integrated manner. A vulnerability approach can be used to understand how climate change will differentially affect the water sector in Tianjin. 2. Conceptual framework of vulnerability assessment Vulnerability has been defined in different ways by many researchers and related to exposure, sensitivity, adaptive capacity, robustness and resilience in many climate change studies (McCarthy et al., 2001; Kasperson et al., 2003 and Turner et al., 2003). There is no universally accepted definition of vulnerability. For example, vulnerability is defined as the degree to which a system is likely to experience harm due to exposure to a hazard, either a perturbation or stress/stressor by Turner et al. (2003). IPCC promoted an alternative definition: the degree to which a system is susceptible to or unable to cope with adverse effects of climate change, including climate variability and extremes (McCarthy et al., 2001). Although many conceptualisation of vulnerability have emerged from natural hazards and food security literature, within climate change literature it is generally considered to be a function of exposure, sensitivity and adaptive capacity (Schneider et al., 2001). Vulnerability is adapted here as the degree to which the water sector is likely to experience harm due to climate change (including climate variability and extremes), and the ability to cope or adapt to it (Kasperson et al., 2001). Vulnerability of the water sector should be examined in the context of multiple social and natural system stresses (e.g. climate change, land-use change, population change and movements, changes in institutional set-up, technology gains, and economic restructuring) in order to capture the complexities of the human-environment interface and the adaptive strategies of individuals, industries, institutions, and communities. This assessment aims not only to identify who is most vulnerable but also to understand why and to assess adaptation options. It involves both qualitative and quantitative analyses. Measuring vulnerability is important in the sense that it provides quantitative metrics to explain where vulnerable groups lie and how vulnerable they are across time or places within a system. However, such assessments are rare due to lack of robust metrics of vulnerability, complexity of the system analysed and the fact that vulnerability is not a directly observable phenomenon (Luers at el., 2003). Vulnerability is a relative concept, mainly because it is difficult to define criteria for quantifying vulnerability. For instance, every water user is vulnerable to climate change to a certain extent in that it affects the availability of water resources as well as water demand. But agriculture may suffer the most or be the most vulnerable because of its low priority in official water agenda. 2

Despite the difficulty in measuring vulnerability, several quantitive metrics have been proposed and applied (e.g. Stephen and Downing, 2001; O’Brien et al., 2003). The most common used method in global change literature is to choose a set or composite of proxy indicators (e.g. O’Brien, 2000; Downing, 2001; Adger, 2004). The indicator approach is useful for monitoring trends and exploring conceptual frameworks. The indicators are often collected for different geographical regions to analyse the relative vulnerability across areas. This study will also explore the vulnerability changing over time. Several steps are involved in assessing vulnerability of Tianjin’s water sector. Firstly the socioeconomic and institutional characteristics of the water sector, especially those in relation to climate change are studied and several hypotheses regarding vulnerability are formulated. Secondly the water resources and impacts of climate change are elaborated, along with the adaptive management. Following that, the present vulnerability is measured by constructing a composite index for the water sector in Tianjin and the differential vulnerability is analysed across major water users and across districts. Consideration is also given to future vulnerability in the near term focusing on estimation of water shortage, and adaptation options are recommended for main stakeholders. 3. The water sector in Tianjin and its socioeconomic and institutional context Among all the rivers in the north, the Haihe River is the severest in the over-exploitation of its water resources due to high population density and intensified economic activities. The overdraft has resulted in discontinuous flow, a lowered groundwater table, and degradation of water quality. Tianjin, located in the northeast of the North China Plain and the lower reaches of the Haihe River, is selected as the study area. Tianjin has water of merely 160 m3 per capita and has suffered from water shortage for a long time. As one of the municipalities under the direct administration of the Central Government of China, Tianjin is the economic centre of China's Bohai rim and is being built into a modern port city and a major economic centre in North China. Fig. 1 illustrates the geographical location of Tianjin. It has a population in excess of 10 million, of which 60% are counted as urban, with most living in the city itself (Tianjin Statistical Yearbook, 2004). The municipality consists of six urban, nine suburban districts (including three coastal) and three counties. The urban districts in and around the city lie within its outer ring and occupy 254 km2 while the municipality totals 11920 km2. The climate in Tianjin is warm, semi humid and experiences monsoon climate with typical features of the warm temperate zone. The average annual temperature in the whole year is 12.3 degrees centigrade. The normal average annual rainfall is 590 mm. Fig. 1 Tianjin’s location

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The stakeholders in Tianjin’s water sector consist of public water utilities that deliver water and relevant services, various water users, and the government and institutions that regulate, monitor and manage surface and groundwater. The pressures the water sector faces are mainly from diminishing and insecure water resources as well as growing water demand. The water sector is sensitive and vulnerable to climate variability and change in the sense that they threaten water supply especially by frequent droughts and damage croplands and human property by floods. Public water utilities are mainly state-owned; insufficiency of water resources would not directly affect their financial status. By contrast, water users (mainly referred to domestic, industry and agriculture) are the ones that are susceptible to changes in water supply and increasingly water pollution. In general, all water users are subject to welfare loss or damage to some extent when there is water shortage. Institutions and organisations involved in water management play a central role in coordination and making decisions in water allocation as well as ensuring good governance of limited water resources. Agriculture is considered as the most vulnerable due to the low priority in water allocation in the governmental plan. The hypotheses to be investigated in the framework of vulnerability of Tianjin’s water sector are: 1) The water sector is becoming more vulnerable to climate change over time. 2) Agriculture and farmers are the most vulnerable among all water users. 3) The poorest county-Jinghai County is the most vulnerable to climate change. Growing demand for water is a major concern in the water sector. Population and economic growth are two main underlying drivers of the increase in demand. The population grew at an annum of 1.2% in the past decade. In addition, every year many rural labourers from outside the city flow into Tianjin to earn some living. The mobile labour force and unregistered migrants are out of statistical counting, which implies that the actual population consuming water are more. Population growth increases the total demand for water from households and municipal uses. Moreover, urbanisation is intensified, which increases water demand for urban infrastructure and municipal uses. In general, urban households consume considerably more water than rural ones. The growing need from urban and municipal purposes poses a challenge to the ever-severe water shortage situation. At a current rate of 100 litres of water per day per head, population growth will continuously have pressure on water resources. On the other hand, its economy grows fast and steady. The industrial output of Tianjin grew at 10% per year throughout the 1990s and its gross domestic product (GDP) reached US$29.5 billion in 2003. The industry accounts for 50% of GDP in the region. As a heavy industrialised city, industries generate a huge demand for water as well and wastewater discharge from some industry contains harmful and high concentrated chemicals, which is likely to induce severe water pollution1. Furthermore, industrial expansion and population growth have concentrated serious environmental pollutants in densely populated areas. Air and water pollution, as well as and degradation become the major environmental concerns. Many of these issues end up being reflected in the health sector.

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For example, a chemical explosion contaminated the Songhua River with in northeast China left millions without water for four days in November 2005. The slick of benzene spread along the Songhua River and into the Amur River reaching Russia; an excessive discharge of cadmium from a state-owned smelting works in the Beijiang River caused the latest toxic slick, endangering a major source of drinking water for cities in the northern part of Guangdong.

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Agriculture related activities have a relatively small proportion in Tianjin’s total GDP but play an important role in securing food production. The major farm crops are grain (wheat accounting for 35%, corn for 38%), cotton, oil-bearing crops and vegetables. The grain production tends to decline while the production of other crops especially cotton and vegetables increase. China has the self-sustaining food policy, which implies that agriculture needs to be practised even in an unfavourable market. Crop production is dependent on cultivated area, water availability and fertilizer use as well as weather conditions. Rainfall is often insufficient to meet water demands for crops in the whole growing season or part of it. Irrigation is crucial for obtaining high yields. In the areas where multiple cropping is practiced, irrigation is simply essential. Given their important role, irrigated areas have been expanding continuously over the years. Nowadays, more than 75% of cropland has been brought under irrigation and most of the crop output is generated from irrigated land. The water for irrigation accounts for more than 50% of total water use of Tianjin. Agricultural production has been frequently reduced by drought incidences. Warmer temperature often happens concomitantly with droughts, which aggravates the problem. The damaged cropland and economic loss are considerable. For example, the drought has induced disaster to a cropland area of 132,000 ha in 1997, 194,000 ha in 1999 and 218,000 ha in 2000 (Water Resources Bulletin, 1997-2000). In terms of institutional arrangements, it is important to understand how water is managed on a national level before turning to Tianjin. The responsibilities of China's water resources are distributed among various national ministries, regional and local bureaus, and governments. Fig. 2 illustrates the framework of current water management in China. Ministry of Water Resources along with provincial governments take the main responsibility of water management, in cooperation with other relevant ministries and state organs. There are often conflicts between seven river basin committees and provincial government as the provinces have local power and provincial water resource planning is often designed to maximise provincial benefits. This sometimes results in sub-optimal river basin benefits.

Fig. 2 Institutional framework of water management in China (Lu et al., 2002)

Tianjin’s water resources are managed through a multitude of institutions and organisations as well (see Table 1). The Water Conservancy Bureau takes main charge of water resource 5

management along with Tianjin Municipal Environmental Protection Bureau and other institutions. The specific responsibility is detailed in Table 1. Inter-bureau cooperation among these bureaucratic institutions is sometimes poor. Table 1. Various institutions concerned with water management and their responsibilities Organization Involved 1. Planning Bureau, TMEPB 2. Water Conservancy Bureau (WCB) 3. Public Utilities Bureau (PUB) 4. Tianjin Municipal Environmental Protection Bureau (TMEPB) 5. TMEPB & WCB 6. WCB 7. TMEPB & WCB 8. TMEPB & PUB 9. Quarantine station, PUB 10. WCB, Water Saving Office 11. Office of Municipal Works (OMW) 12. TMEPB, Industry Bureau 13. TMEPB 14. Industry Bureau 15. Animal Husbandry Bureau 16. Agricultural Bureau 17. PUB

Responsibility Preparation of City Master Plan Provision of sufficient water resources Supply, treatment and distribution of water Protection of river water quality Protection of surface water quality Monitoring surface reservoir water quality Protection of surface reservoir water quality Protection of groundwater quality Monitoring groundwater quality Promotion of “water conservation measures” Promotion of water-demand management measures Water leak inspections Municipal wastewater treatment (MWWT) Monitoring discharges from MWWT Control of industrial wastewater discharges Monitoring of industrial wastewater discharges Collection of industrial effluent fees Promotion of cleaner industrial production Control pollution from animal husbandry Control use of fertilizers and pesticides Collection of water usage fees Repair and maintenance of public water infrastructure

4. Water resources, climate change and adaptive water management 4.1 Water resources in Tianjin In a normal year, the average runoff from local rainfall is 986 million m3. The average surface water totals 1055 million m3 and groundwater amounts to 832 million m3. Removing the double counting, the total amount of local water resources is about 1.82 km3 (billion cubic meters) in Tianjin, which makes water per capita merely 160 m3/capita. There are several distinct features of Tianjin’s water resources. Firstly, water is unevenly distributed temporally and spatially. The amount of surface water is affected largely by occurrence of precipitation throughout the year. About 70-80% of precipitation occurs during the flood season from June to September. At times, 40-70% of rainfall falls within three days. In addition, water is distributed skewed across the districts in Tianjin. Water resources are abundant in the north but less in the south. More than 65% of surface water in Tianjin is located in the north of the Haihe River and the exploitable groundwater is also more in the north. Secondly, the annual rainfall varies substantially among the years. For instance, in the past 45 years, there was less than normal average rainfall for 70% of the periods and more than normal for 30% of the same periods. Surface water is affected by precipitation but also its intensity as well as the change of soil moisture etc. The maximum annual runoff occurred in 1977 with an amount of 6

2.376 km3 while the minimum appeared in 1997 with merely 0.279 km3 (Liu and Liang, 1999). The former is a factor of 8.5 of the latter. Rainfall appeared typically in a cycle of several consecutive wet years followed by several consecutive dry years. Variable rainfall makes Tianjin prone to both floods and droughts. Since the 1980s, the region has faced increasingly severe drought problems. According to the hydrological data, continuous low flow years occurred for the period of 1957-1958, 1960-1963, 1965-1968, 1971-72, 1974-76, 1980-84, 1992-93, and 1997-2002 in Tianjin. The drought lasted maximum five years (Sun, 2002). Thirdly, the water inflow from upstream has a declining trend. Based on the measurement of inflow, the annual average is 8.0 km3 in 1960-69, 3.6 km3 in 1970-79, 0.7 km3 in 1980-89, and 1.9 km3 in 1990-98 (Liu and Liang, 1999). It is especially pronounced in the south, with zero inflow in a normal year. Finally, as Tianjin is close to the Bohai Sea, the intrusion of seawater is also noticeable, which causes a large amount of groundwater saline and thus make the water unusable. 4.2 Impacts of climate change on water Climate variability and change affects water resources. Two tangible variables of climate change are temperature and precipitation. Zhou and Liu (1999) analyse the climate change in Tianjin under global warming. Based on the recorded temperature from 1932 to 1990, the study shows clearly that the climate tends to become warmer, the period 1986-1990 being the warmest period since the record. The regression analysis shows that the annual average increase in temperature in Tianjin is about 0.0129 ºC. The temperature in winter has an annual rate of 0.034 ºC, obviously higher than the average while in summer it tends to be cooler with an annual rate of – 0.0093 ºC. The interannual change tends to decline, which is in accordance with the experimental results that greenhouse gases effects reduce the difference in temperature in regions in the mid latitude. In general, warm temperature affects water resources by increasing the evaporation. However, there are many other factors contributing to evaporation such as wind speed and sunshine hours. According to the statistics on temperature, wind speed, sunshine hours and evaporation, evaporation is not found to increase as expected but rather decrease over time. It is therefore concluded that temperature does not have a significant effect on evaporation in Tianjin. However, warmer temperature is often concomitant with droughts and it aggravates droughts, which increases the water uptake from crops and generates more urban needs for water. Precipitation is a more regional phenomenon than temperature. Since the 1980s the precipitation in summer tends to decline. Tianjin’s water resources consist of local water resources and the inflow water from upper reaches of the Haihe River. The local precipitation in Tianjin is a determining factor of the local surface water resources and is an important supply to groundwater as well. Since hydrological record started in 1891, the annual mean precipitation in Tianjin is 538mm. Since 1956, the average annual precipitation based on the monitoring stations in Tianjin is 590mm. Tianjin falls into a warm temperate zone with monsoon climate. Precipitation in summer (June to September) accounts for 73% of the total in a normal year. Summer precipitation has a higher correlation with the amount of surface water than annual precipitation. Fig. 3 illustrates the variations of summer precipitation and total surface water. It shows the variations are in accordance with each other for most of the years. Hence the summer precipitation determines to a large extent the amount of local surface water. Summer annual precipitation since 1950s is 421.6mm on average. It increased slightly in 1950s-1970s, reached the highest in 1970s and since 1980s it declined considerably. In 1977 the summer precipitation reached the highest 713mm and it hit record low 183mm in 1968. Regarding the summer 7

precipitation of less than 350mm as drought year and of greater than 550mm as flood year, the proportions of drought and flood years are 31% and 21% respectively.

Fig. 3 Summer precipitation (mm) and total surface water resources (107 m3) (Wang et al., 1999)

Due to the concentration of precipitation in summer and the interannual change, water resources in Tianjin entails great uncertainty. Table 2 lists the drought years and the corresponding water resources. Except for in 1958 and 1963, water resources in the drought years are less than 34% of the perennial average. In many cases, water is less than 1 km3. In particular when there exists a continuous drought e.g. in 1982 and 1983, water resources in 1983 are obviously less than the perennial average but also less than 66% of that in 1982. Although the precipitation in 1958 and 1963 are relatively small, due to the ample precipitation in upstream the total surface water resources are still abundant. On the contrary, the precipitation in Tianjin in 1961 and 1975 are greater than 500mm, but due to the low precipitation upstream the water resources in Tianjin were on a low side. Table 2 Precipitation (mm) and total water resources (million m3) in drought years (Liu and Liang, 1999) Year 1958 1963 1965 1968 1972 1980 1982 1983 1989 1992 1997 Precipitation 315 259 348 179 206 318 263 314 278 340 349 Water resources 3207 4055 809 313 911 586 1722 577 1680 1400 1500

According to statistics in 1956-1990, among the total surface water local water accounts for 41% and inflow water takes up 59%. Hence Tianjin’s surface water depends largely on inflow water from upstreams. Its water declined accordingly as the water in the Haihe River basin reduced decade by decade. In 1980s there was the lowest quantity of water and the average amount of water resources is about 38% of that in late 1950s. 4.3 Adaptive water management Under the circumstances of climate variability and water insecurity, water management plays an important role in monitoring river flows, storing summer rainfall, alleviating water shortage,

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allocating water among users and between up- and down streams. As water supply fails to meet the demand, the traditional responses are mainly supply-oriented and aim at fostering the development and exploitation of new sources and expansion of the network infrastructure to guarantee the water supply. As Tianjin’s water is largely dependent on inflow water, the extent to which new water sources can be explored in Tianjin is restricted. A large amount of water has been brought in to meet the basic need from outside. Only recently, the demand management water policies have been established, including development of water conservation programs, adoption of water saving technologies and appliances, water reuse and recycling, and economic instruments such as price. By far, these attempts have only marginal effects. Before 1970 Tianjin seemed to have benefited from abundant water as locating in the downstream of the Haihe River. However, since 1970s population growth and economic development in the river basin have abstracted an enormous amount of water from the river and thus reduced the water flowing into Tianjin. To meet urban water demand, throughout 1965 to 1980, the upstream reservoirs such as Miyun, Gangnan, Huangbizhuang, Yuecheng supplied water for Tianjin. However, in extreme shortage, water transfer from other rivers was considered as the best option. In the years 1972, 1973, 1975, 1981 and 1982, water was transferred from the Yellow River to Tianjin to eliminate water shortage. In 1981, the State Council approved the construction of water transfer project from the Luanhe River to Tianjin. Meantime, Miyun reservoir terminated delivering water to Tianjin. In 1983, the Luanhe Diversion Project was completed and became effective in supplying water to Tianjin since then. The situation of water shortage was alleviated. Nonetheless, this project is limited in its capacity and has low water supply reliability. For instance, in 1997-2000 due to the continuous drought, the water source of the Luanhe diversion, Panjiakou reservoir, has reached below its dead storage capacity. In 2000 and 2002, to mitigate water shortage, water was again diverted from the Yellow River to Tianjin. The Yellow river is also permanently short of water; at times it does not reach the sea. Water transfer was regarded as an important option in solving water shortage in the past. Fig. 4 shows the historical water transfers to cope with low water flows.

Yellow river diversion

Luan He diversion

Fig. 4 Historical coping measures to climate change (Adapted from Liu and Liang, 1999)

5. Measuring vulnerability

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5.1 Vulnerability of the water sector in Tianjin To measure vulnerability of Tianjin’s water sector to climate change, three components are considered: exposure, sensitivity, and coping and adaptive capacity. As this vulnerability should be examined in the context of multiple social and natural system stresses in order to capture the complexities of human-water interface, indicators from socioeconomic, physical and institutional aspects are taken into account. We attempt to capture the change of vulnerability over time by using time series data and to analyse the differential vulnerability across sectors and among districts. The institutional, economic and physical aspects of water resource and its management system are contributing factors to the vulnerability. To address each of them, the most relevant indicators are identified. The composite vulnerability index is considered feasible for the Tianjin case. It can be envisaged as a complex system that contains sensitivity and coping capacity as two sub-indexes. Indicators are selected based on several criteria: 1) theoretically well founded within the vulnerability framework; 2) relatively stable and independent; 3) measurable and comparable, easy to quantify; 4) based on time series data that is accessible. All those indicators that do not fulfil the criteria have to be omitted. The selected indicators are divided into the sensitivity index and the coping capacity index. The sensitivity index comprises indicators that reflect the changes associated with exposures and stresses, and those indicators directly or indirectly affect the vulnerability of the water sector. Physical variables such as runoff, groundwater availability and the amount of inflow water are included. The coping index consists of the indicators in relation to coping measures, adaptive capacity and the socioeconomic capacity of the water sector. The data are mainly derived from Tianjin Statistical Yearbook (1990-2004) and Water Resources Bulletin (1987-2003). Some data points are obtained from published or unpublished official reports from the Water Conservancy Bureau, the Haihe River Basin Commission and Tianjin Environmental Protection Bureau. A complete list of indicators chosen is shown in Table 3. The final number of indicators is considerably smaller than initially designed due to some indicators not having time series data or a high correlation with other more important indicators. For instance, precipitation is found to be highly correlated with runoff so it is left out. The proportions of agriculture and industry in GDP are not included because they are correlated to GDP per capita. Table 3 Specification of indicators in the vulnerability framework of Tianjin’s water sector Level one Sensitivity indicator proxies

Level two Water resources

Level three Annual runoff Groundwater availability

Proxy for Degree of climate variability in surface water Degree of climate variability in groundwater

Functional relationship Sen DC as Runoff IC but within a range Sen DC as groundwater IC

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Inflow of water

Coping and adaptive capacity

Water quality

Wastewater/total surface water

Water consumption

Domestic water shortage

Agriculture

Grain production/ agricultural land (Yield) Water transfer/water shortage

Physical capacity

Water availability from upstreams which is affected by climate variability and withdrawal in upstream areas Pollution charge to water environment reflecting the pressure of wastewater on freshwater Degree of people’s need for water and sanitation that is not satisfied Grain productivity subject to climate variability and change

Sen DC as Inflow IC but within a range

Contribution of water transfer to eliminate water shortage

Cop IC as it IC

Degree of access to technology and resources Social and economic resources available for adaptation after meeting other present needs

Cop IC as it IC

Sen IC as it IC

Sen IC as It IC

Sen DC as It IC

Economic capacity

Income per capita

Human resources

Dependency ratio

Renewable natural capital

Afforested area

Adaptive capacity in improving land cover and reducing the risk of flooding

Cop IC as it IC

Population density

Population pressure and stresses on ecosystem

Cop DC as it IC

Cop DC as it IC

Sen: Sensitivity; Cop: coping capacity; IC: increase; DC: decrease

It is relatively easy to produce a list of ideal indicators, but it is much more difficult to find the data. For this indicator system, data from 1987 to 2003 are used for each indicator and thus the scope for selection is limited. All the indicators are normalised before aggregating to the composite index. The normalisation is conducted based on the minimum and maximum value by rescaling the indicators up to100 with 100 as the best value representing lowest sensitivity. For instance, for the indicators whose increments result in higher sensitivity of the water sector, the minima are regarded as the best value and given 100. Conversely, if the indicators decrease sensitivity, the maxima are given a value of 100. It is similar for the coping index. The calculation is illustrated in Table 4. Since the weights for indicators are unknown and hard to justify if given different weights; for simplicity, equal weights are given to all indicators. Table 4 Normalisation of indicators Function Sensitivity Coping

100 × X i X max

100 × X min X i

Indicators ↑ Sensitivity ↓

Indicators ↑ Sensitivity ↑

Indicators ↑ Coping ↑

Indicators ↑ Coping ↓

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The sensitivity index includes indicators addressing water resources, water consumption and agricultural productivity. Water resources are sensitive to climate change in terms of their availability and quality. Runoff along with inflow of water reflects the sensitivity of surface water to climate. Drought events can be well reflected by the amount of runoff; however, flooding is not captured if there is no threshold for the inflow of water. To incorporate this factor, the inflow of water is adjusted based on the occurrence of flooding. For example, when the inflow of water is greater than 4.5 km3, there is a good chance for flooding. Groundwater reflects the sensitivity of groundwater as well as the adverse effects associated with it such as lowering groundwater table and earth subsidence, which has occurred in a large area in Tianjin. Water quality is captured in this index by the proportion of wastewater to total surface flow. Although water pollution is a serious issue in Tianjin, a better quantitative indicator is not yet available. As water supply cannot meet demand, there will be welfare loss in water users such as households, industry and agriculture. To address user’s sensitivity, we include two indicators: domestic water shortage and grain yield. Domestic water shortage is calculated based on actual consumption and desired demand. The desired demand is set to be 150 litre per capita for urban households and 80 litres for rural households according to Gleick (1999) for the year 2003. As there exists income elasticity for water, we adjusted these values according to income per capita across years based on the assumption of 100 litres per capita and 60 litres per capita for the base year 1987. This indicator reflects well the water insufficiency for households’ use. A similar attempt to find an indicator for industry however failed. That industry suffers from water shortage is a fact but no obvious indicators could capture the economic loss of industry as the industrial added value increased dramatically over time. For agriculture, the grain yield is regarded as a suitable indicator to demonstrate the effect of climate variability on crop production. The sensitivity index is illustrated in Fig. 5 along with all the contributing indicators. The thick line represents the composite index. The higher the value is, the less sensitive the water sector is in a particular year. Examining single indicators over the study period, we found that runoff fluctuates greatly and tends to decrease, so are the water inflow and water quality. Groundwater kept stable until 1993 and begun to drop dramatically since. Domestic water shortage went up and down the up again. Grain yield increased slightly till 1998 and then fell. In general, it shows that the sensitivity varies over time and has a declining trend, which implies that the water sector is becoming more and more sensitive to climate change. Take 1987 as the base year, it is observable that the sensitivity goes up and down throughout the study period. High sensitivity appears at several discontinuous years such as in 1989, 1993, 1997 and 1999. In 1989, the high sensitivity arises mainly from low runoff and inflow as well as water pollution. In 1993, aside from these factors domestic water shortage is also a contributing factor to the sensitivity. The interannual variation in sensitivity suggests that the water sector needs to be prepared for differences and the coping and adaptation options should be robust enough to address the changes.

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120

100

Index

80

60

40

20

0 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 Year Runoff

Groundw ater

Inflow

Domestic w ater shortage

Grain yield

Sensitivity index

Water quality

Fig. 5 Sensitivity index of the water sector

The coping capacity index in principle consists of all the actions and practices implemented to eliminate water shortage and the measures taken to cope or adapt to the uncertainty. Factors from socioeconomic, institutional, constitutional, behavioural and physical aspects should be captured. For example, to practise agriculture under water scarcity farmers would choose to switch from water intensive crops to less water demanding crops, or switch from grain to cash crops to guarantee more income. Farmers’ coping activities can be captured from the proportion of rice sown area of the total grain since rice consumes more water than other grain in general. There is a clear correlation between the previous year water availability and the current year planting of rice. In the meantime farmers or the local government would invest in new water infrastructure, such as expanding the use of anti-seepage channels or use more advanced irrigation technologies to improve water use efficiency. However, it is hard to obtain quantifiable indicators from such information especially in a time sequence. Due to many variables immeasurable, limited yet representative indicators are included in this analysis. Water transfer has been considered as a coping measure to alleviate water shortage in Tianjin. Water transfer relative to water shortage is regarded as an effective indicator for coping capacity. Per capita income represents the economic capacity of the region. As people become richer, it appears that they have more access to resources and technology. Dependency ratio is taken as an indicator for human resources, which reflects the social status of population and the resources available for adaptation. The last two indicators: afforestation and population density represents

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the capacity of renewable natural resources. Afforestation changes the land cover, intercepts rainfall and reduces the risk of flooding in summer. 120

100

Index

80

60

40

20

0 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 Year Water transfer

Per capita income

Dependency ratio

Population density

Afforestation

Coping capacity index

Fig. 6 Coping capacity index

Fig. 6 illustrates the coping and adaptive capacity of the water sector in Tianjin over time. The higher the value is, the greater the coping capacity. It shows that coping capacity fluctuates over the years and tends to increase. Per capita income is the main driver in improving coping capacity. On the other hand, water transfer and afforestation account for most of variations in the coping index. The small changes in dependency ratio and population density contribute to the index only marginally. In general, the curve shows a quite steady increase but it dented in the years 1995 and 2001. This may arise in part from the low transfer capacity and afforestation. Coping capacity varies over time and if it corresponds to the need to reduce sensitivity, it would work perfectly. Coupling sensitivity index and coping capacity yields the vulnerability index shown in Fig. 7 The higher the value is, the less vulnerable the water sector is. This index smoothes out the variations between sensitivity and coping capacity and falls in the range within them. The vulnerability fluctuates over time as well and generally tends to decline, which indicates the increase of vulnerability of the water sector to climate change. It is in accordance with our hypothesis. As vulnerability increases, there is an urgent need to tackle it and implement further adaptation measures.

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100 90 80

Index

70 60 50 40

Sensitivity Coping capacity Vulnerability

30 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 Year

Fig. 7 Vulnerability index

The above analysis quantifies the vulnerability of the water sector, but some institutional aspects are not fully captured. Considering the role of intuitional arrangements and change played in water management and the coping and adaptive capacity of the water sector, institutional vulnerability is preliminarily investigated. From the information gathered through governmental documents and the expert interviews conducted, the following key findings can be drawn: 1) there is a lack of cooperation and communication among institutions involved in water management. For instance, several similar projects on future water demand and supply are conducted in different bureaus and the results are different and not effectively exchanged. Basic hydrological and monitoring data exchange is also not sufficient. Some institutions compile their own dataset, which results in a lack of standard baseline dataset for common use. 2) There is often a conflict between local governments (such as Water Conservancy Bureau) and the Haihe River Basin Commission. Municipal water resource planning is often designed to maximise its local benefits, which are sometimes not compatible with river basin planning. In addition, there is very little co-ordination among the various agencies on water projects being undertaken in the Haihe river basin. 3) Due to the wide distribution of responsibilities, some functions such as protection of water qualities could not be fully realised without full participation and compromises of various institutions. 4) Adaptation for climate change including sea level rise have not yet incorporated in government plan. Although some research initiatives are under preparation in this direction. 5.2 Sectoral analysis The second hypothesis to be tested is whether agriculture/farmers is the most vulnerable among all the water users. The water users in Tianjin comprise households, the municipality, industry, agriculture, fishery and ecological uses. The major consumers are domestic (here including urban

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and rural households, and municipality use), industrial and agricultural sectors, accounting for 22%, 22% and 56% of the total use respectively in 2003. Domestic use has the highest priority in water distribution aiming at meeting basic human need. Industry has the second priority in Tianjin, with certain quota allocated to water intensive industries each year. However, agriculture, which is often considered at last, suffers most from water shortage. Groundwater is the main source for freshwater in rural areas. Due to the lack of alternatives, farmers have been forced to irrigate their crops with wastewater. Wastewater irrigation has been prevailing in rural areas of Tianjin since years. For the domestic sector, consumers are concerned about whether there is enough water to satisfy their needs and the demand for water quality. As Tianjin began to experience water shortage since 1980s due to the very limited water resources, households have been consuming water less than the counterparts in China where abundant water is available. Although household’s use is given priority, under water crisis the amount of water that could be delivered to households can be disappointing. In the above sensitivity analysis, domestic water shortage is analysed, which suggests that there exists differential water shortage over time and households have not reached a comfortable level of water consumption. This indicator only concerns about water quantity. In some cases, however, undesirable water quality is a cause of health problems and reduction of amenity. Drinking water quality is controlled and seems to match the standards. But most of the rivers running though Tianjin have water quality of worse than the national standard Class IV (Water Resources Bulletin, 1987-2003). The factors that induce the vulnerability of households to climate change are insufficient water supply and undesirable water quality. In recent years, the capricious weather and the occurrence of duststorms arising from desertification in the North China Plain aggravate the water problem. Among all the households, the urban residents are equipped with the best water facilities with relatively secured water delivery. For farmers in rural area, groundwater is the main source for household’s use and farmers are exposed to contaminated surface water. Water used to be charged at low prices. Only in recent years an increase in water prices has been seen. The price for urban households has increased from 1.4 Yuan in 1999 to 2.2 in 2001 till about 2.9 in 2003 (Xing, 2003). As an incentive to reduce water consumption, a higher price is anticipated, especially once water is transferred for use from the Yangtze River by the South North water transfer scheme. As a heavy industrialised city, Tianjin’s industry is dependent on water for production or coolinf effects in many traditional heavy industries. The risks lie in that some industries receive a small quota of water under water crisis. Under extreme scarcity, some firms do not have any water and occasionally are forced to shut down until the water crisis is over. This way, industries are hindered in their productivity especially if they have a high water demand. However, as economy develops, heavy industry is gradually replaced by high technology and knowledge based industries and services. For example, the proportion of the industry’s contribution to the GDP has dropped by 10% in last ten years. As for agriculture, irrigation plays a central role in securing crop production in the area. Groundwater is the main source for freshwater in rural area as transferred water rarely reaches to counties unless the channels pass by. The vulnerability of agriculture sector lies in that climate condition and irrigation determines crop production and farmers depend directly on crop harvest

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for their living. In this regard, farmers are the most sensitive to water availability subject to climate change. In Tianjin, the proportion of cultivated land is declining so is the productivities of the crops. The reduction of the grain yield can be attributed in part to the insecurity of water resources. Liang and Liu (1995) analyse surface water resources and grain yield in Tianjin for the period of 19631990, suggesting that due to the lowland subject to water logging, surface water that is greater than 3.5 km3 reduced grain yield, with an average of 151, 000 ton. However, when surface water was less than 3.5 km3 in current year along with a water resources greater than 2 km3 in the previous year, grain yield would generally increase, averaging 131, 000 ton. If water were less than 2 km3 in the previous year, the current yield would reduce about 62, 000 ton on average. In addition, crops that are harvested in autumn such as rice and corn suffer from marked yield reduction if flooding occurs in that year. In contrast, summer harvested wheat shows pronounced yield increment if the previous year’s water is abundant. Table 5 Regression models for grain yield

Constant Precipitation Irrigation Water resources (n-1) R square *

Coefficient -4.861 0.149 0.696* 0.431** 0.735

t statistics -2.46 1.23 3.07 3.15

Coefficient -7.09 0.31** 0.919**

Significance at 5% level; ** significance at 10% level.

t statistics -5.26 3.09 6.58

0.511

Using data from 1987-2003, a model is developed to address the relationships between the grain yield and precipitation, irrigation, and water resources. The dependent variable is the grain yield and the explanatory variables are precipitation in the current year, the amount of water irrigation and/or the water resources in the previous year. Table 5 shows the regression results for the double log function. If we include all the variables, we obtained R square of 0.735. The results show that irrigation and water resources in the previous year correlate positively with grain yield and are significant. It suggests an elasticity of 0.7 for irrigation and 0.43 for the previous water resources, which implies that for every 1% increase in irrigation water, the grain yield would increase 0.7%. If we only consider precipitation and irrigation in the current year, it is found that both of them are significant and positively correlated to yield. It suggests an elasticity of precipitation of 0.31. In sum, climate variables and water availability significantly affect the grain yield. Agriculture is sensitive to climate variability. As farmers in general are vulnerable groups who have low income and low status, the sensitivity to climate change would aggravate the farmers’ situation. In Tianjin, farmers have about half of the income as urban residents, a drought or flood occurrence would significantly reduce the income in that year. In this sense farmers’ income is not only low but also uncertain and subject to climate. 5.3 District/County analysis After analysing the vulnerability of Tianjin as a whole and of various water users, a further question of interest is how the vulnerability would differentiate inside the municipality. Tianjin has six urban districts, which are regarded as the downtown area, three coastal and six suburban districts as well as three counties. All the districts and counties share similar climate but different 17

geographical features and engaged in different activities. The urban districts have no engagement in agriculture. Since agriculture is an important activity in assessing vulnerability for other agriculture based counties, we decide to leave urban districts out and only consider the rest of districts and counties. The time series data for the counties are limited and hard to obtain. To account for the differences among districts, it is thus better to include as much information as obtainable rather than a few indicators of time series. Based on the latest data from 2003, a large number of indicators are collected for all districts or counties based on the criteria described in the previous analysis. All the indicators except precipitation is taken as relative terms either by proportion, by per capita or per area in order to enable effective comparison among districts. All the indicators are listed in Table 6. The vulnerability index for districts is reflected by several sub-indexes, including water resources, water quality, agriculture, water infrastructure and socioeconomic variables. Each indicator represents either sensitivity or coping capacity of the water sector in that district. Table 6 Vulnerability index for districts and counties Level one Level two Vulnerability index Water resources

Level three Annual precipitation Water resources/capita Water use/resources (exploitation ratio) Water quality COD (Chemical Oxygen Demand) concentration Ammonia and nitrogen concentration Agriculture Grain yield (ton/ha) Secured agricultural land (regardless of droughts or floods) Water saving land Electro-mechanical irrigated area Water infrastructure Water infrastructure investment/person Total capacity of reservoir/person Irrigation machinery/person Anti-seepage channel/person Electro-mechanical wells/person Socioeconomic GDP/capita Operating educational expense/capita Non agricultural population Population density

As there are a large number of indicators altogether, it is inevitable that that some indicators are correlated to each other. All the collected information is regarded valuable, so a reduction of indicators seems undesirable. Under this circumstance, we adopted a different method for computation of this index. The Principal Component Analysis (PCA) is used to eliminate the possibility of overlapping information in the indicators. PCA is a powerful technique in reducing the dimensionality of a data set while retaining as much information as is possible (Jolliffe, 2002). Essentially, a set of correlated variables is transformed into a set of uncorrelated variables that are ordered by reducing variability.

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The results of the PCA indicate the existence of six principal components for the 18 indicators, which explain more than 90% of the variation in the data. Although the number of components selected depends to a certain extent on the criteria used to determine the cut-off point for adding more component, both the scree-plot2 and eigenvalue greater than 1 support the choice of six components. The components and their proportion of variance (weight) are listed in Table 7. For example, Component 1 is determined predominantly by indicators belonging to agriculture such as grain yield and water saving agricultural land. Precipitation and capacity of reservoir as well as population density are also influential indicators of this component. Table 7. Principal component loading matrix

Variable Annual precipitation Water resources/capita Water use/resources COD concentration Ammonia and nitrogen concentration Grain yield (ton/ha) Secured agricultural land Water saving land Electro-mechanical irrigated area Water infrastructure investment Total capacity of reservoir Irrigation machinery Anti-seepage channel Electro-mechanical wells GDP/capita Operating educational expense Non agricultural population Population density Weight Shade: Absolute value >= 0.3

1 0.38 -0.28 0.22 -0.13 -0.03 -0.31 0.07 0.36 -0.20 0.03 0.31 0.25 0.22 0.09 -0.15 0.15 0.12 0.41 0.262

2 0.09 0.18 -0.22 0.11 0.08 0.24 -0.24 -0.16 -0.04 0.32 0.10 0.23 0.35 0.36 -0.27 -0.33 0.40 -0.06 0.232

Principal Component 3 4 -0.21 -0.13 0.08 0.04 -0.23 0.14 0.50 -0.12 0.38 -0.33 0.03 0.35 -0.01 0.46 0.13 -0.10 -0.41 -0.28 -0.07 0.29 -0.09 -0.05 0.20 0.28 0.20 -0.17 0.00 0.38 0.33 0.05 0.31 0.13 -0.04 -0.24 0.13 0.04 0.157 0.121

5 0.08 0.47 0.46 0.09 0.27 -0.04 0.33 -0.30 0.16 -0.32 0.25 0.16 -0.02 0.04 0.02 0.10 0.24 -0.06 0.070

6 -0.18 0.15 0.17 -0.02 0.40 -0.09 0.13 0.20 0.26 0.29 -0.51 -0.02 -0.04 0.04 -0.41 0.03 -0.08 0.33 0.067

Using the six principal components and the respective weights the vulnerability index is calculated for each district. The results are illustrated in Fig. 8. The higher the value is, the less vulnerable the district is. All the districts fall in the range between 35 to 80 except Han Gu, which implies that Han Gu is the least vulnerable district in Tianjin in terms of the water sector. The three coastal districts (Tang Gu, Han Gu and Da Gang), characterised by the industrial development rather than a high engagement in agricultural activity, are found to be less vulnerable compared to the rest of the districts. It implies that a diverse economy helps to reduce vulnerability, especially that induced by agriculture. It can be seen that half of the districts have about the same level of vulnerability (between 35 to 55). It suggests that Jin Nan is the most vulnerable district in Tianjin, followed by Dong Li and Bei Chen. Jin Nan’s vulnerability is 2

Scree plot is to plot the eigenvalues in decreasing order and to connect the values in the plots by straight lines. It usually has the form of an “elbow”, starting from larger eigenvalues and dropping quickly to a lower value after which the decrease is more gradual. The point where the transition occurs is chosen to determine the number of components.

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reflected by the low water resources poor water infrastructure and agricultural facilities. As an agriculture-dominant district, such insufficiency would make it more sensitive to climate change. This result rejects the hypothesis that the poorest place is the most vulnerable. 120

Han Gu

Vulnerability index

100

80 Tang Gu

Ning He County Da Gang

Jing Hai County

60 Xi Qing

Bei Chen

40

Bao Di Wu Qing

Dong Li

Ji Xian County

Jin Nan

20

0 Tang Gu Han Gu

Da Gang

Dong Li Xi Qing Jin Nan

Bei Wu Qing Bao Di Ning He Jing Hai Ji Xian Chen County County County

District/County

Fig. 8 Vulnerability of districts in Tianjin

6. Future vulnerability Based on the above analysis, it is found that the main factors influencing vulnerability are the uncertainty of water resources induced by climate change, and the growing demand generated by population and economic growth. Farmers appear more vulnerable than other water users. Districts or counties have different levels of vulnerability depending on many factors. Future vulnerability is continuing to be shaped by these underlying factors. Among all of them, water shortage seems to have the greatest effects on human welfare, economy and agricultural development. This section will concentrate on forecasting the water shortage in the future based on projections on population growth, economic development and irrigation area. Tianjin’s water supply is dependent on local water resources, inflow from upstreams and water transferred from the Luanhe River. Since the Luan He Diversion Project has been constructed and put into use in 1983, urban water supply has relied heavily on this project while rural areas and agriculture continue to use local surface water and groundwater. Tianjin has 3 large reservoirs and 11 medium reservoirs with a total storage capacity of 2.66 billion m3 and current water supply capacity of 0.86 billion m3. Together with water transferred and groundwater, there are less than 3 billion m3 per year, which could meet the basic needs of the city for socioeconomic development (see Table 8). Taking water to be transferred by the east route and the middle route of the South-North Water Transfer Scheme into account, the water supply is 20

projected to be 3.74 billion m3 in 2010 and 4.14 billion m3 in 2020 (Water Conservancy Bureau, 2003). A hydrological model to simulate runoff based on climate scenarios seems too coarse to provide sensible projections for a short term on a municipality scale. Therefore, this analysis sticks to the current water supply capacity under various reliability taking into consideration future transfer. Table 8. Water supply capacity under current situation (million m3) Water supply reliability Surface water Transfer from Luaihe Groundwater Seawater Total

50% 1031 750 734 36 2551

75% 746 750 734 36 2266

95% 236 495 734 36 1501

According to the Master Plan of Tianjin Municipality (2000), the population is projected to reach 11 million by 2010 and 12.5 million by 2020 based on assumptions on population growth and migration. The plan also projects the change in economic structure and growth taking into account the national policy, the current growth rate as well as the potential technology change. These projections serve as the basis of our water demand forecast. The details are presented in Table 9. Table 9 Basic projections used for water demand forecast 2010 2020 Population District/county (thousand) Gross output value of Population (thousand) Gross output value of industry (billion) industry (billion) urban rural total urban rural total Urban 4300 4300 140 4820 0 4820 245 Tang Gu 1030 60 1090 310 1350 60 1410 543 Han Gu 190 30 220 35 220 30 250 61 Da Gang 380 50 430 135 460 50 510 236 Dong Li 280 100 380 35 320 110 430 61 Xi Qing 280 100 380 90 330 110 440 158 Jin Nan 240 210 450 45 280 220 500 79 Bei Chen 170 230 400 50 220 240 460 88 Wu Qing 400 460 860 110 500 470 970 193 Bao Di 300 400 700 70 350 430 780 123 Ning He County 210 170 380 35 250 170 420 61 Jing Hai County 240 310 550 110 280 310 590 193 Ji Xian County 380 480 860 35 420 500 920 61 Total 8400 260 11000 1200 9800 2700 12500 2100

To estimate the domestic water demand, it is assumed that in 2010 urban and suburban households as well as urban population in counties consume 140 litres per head, and 100 litres for rural households. In 2020 the consumption will increase to 150 and 110 litres respectively.

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Based on these assumptions, the domestic water demand is estimated to be 439 million m3 in 2010 and 645 million m3 in 2020. Similarly the water demands for industry and agriculture are projected. Since 2000, industrial water use has been expanded to include municipal uses. Water demand of industry is based on the projections on industrial value added and the water use per unit of value added, which is assumed to be 5.2 m3/thousand Yuan in 2010 and 3.67 m3 in 2020. Considering the various industrial structures in each district, the water use quota is adjusted corresponding to the proportion of water intensive industries such as electric power, petroleum and chemical industries. The tertiary industry including trade and dining business as well as services is expected to grow to 55% of GDP in 2010 and 64% in 2020. For each industry, tailored water quota per value added is used to project the total water demand. As a result, the water demand for industry is projected to be 1.56 billion m3 in 2010 and 1.63 billion m3 in 2020. According to the water quota in the Agricultural Water Saving Plan of Tianjin (2000) and the projections of agricultural activities from the Agricultural Development and Planning in 2010 and 2020 (2000), the agricultural water demand is estimated. The water quota is 200 m3/mu (1 mu = 667m2) for wheat, 600m3/mu for paddy, 80 m3/mu for summer corn and 120 m3/mu for spring corn and 120 m3/mu for cotton. The current irrigation water use coefficient of 0.57 is adopted. If nothing will happen in the next 15 years, the agricultural water demand will stay the same. But the planting structure is likely to change. Taking this into account, the water demand is projected to be 3.17 billion m3 in 2010 and 3.07 billion m3 in 2020. In addition, the necessary water to sustain the environment and ecology is estimated to be 1.12 billion m3 in 2010 and 1.49 billion m3 in 2020 (Water Conservancy Bureau, 2003). On the basis of water demand and supply projected, the water shortage is derived accordingly. Table 10 shows the water shortage under current water supply as well as future supply including water transferred through the South North Transfer Water Scheme. Without any future transfer, the gap between demand and supply is considerable: over 50% of water demand will not be met in a normal year and over 70% of water is short under water reliability of 95%. If the South North Water Transfer scheme is successfully implemented and the total capacity fully realized as planned, the situation will be fairly optimistic. Water shortage would be greatly alleviated: water will be only 28% short in 2010 and 23% in 2020. However, the real situation is hard to anticipate. The water transfer scheme might not be fully implemented and possible negative effects on the whole ecosystem, as well as potential problems with water quality can undermine the expectation. Table 10 Water demand and supply projection in Tianjin (million m3) Year Water supply reliability Water demand 1 Domestic 1 Industry 1 Agriculture 2 Urban

50% 5159 439 1555 3165 1899

2010 75% 5159 439 1555 3165 1899

95% 5159 439 1555 3165 1899

50% 5347 645 1629 3073 2166

2020 75% 5347 645 1629 3073 2166

95% 5347 645 1629 3073 2166

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2

Rural Water Supply (current) Water shortage Water Supply (future) including transfer - Middle route - East route Water shortage Portion

3260 2551 2608 3724 1923 1523 400 1435 0.28

3260 2266 2893 3399 1883 1483 400 1760 0.34

3260 1501 3658 2465 1714 1314 400 2694 0.52

3181 2551 2796 4124 2323 1523 800 1223 0.23

3181 2266 3081 3799 2283 1483 800 1548 0.29

3181 1501 3846 2865 2114 1314 800 2482 0.46

According to the current water policy, domestic and urban needs are prioritised in water allocation, which implies that agriculture and rural development will be hindered the most under water stress. Agriculture will still be vulnerable, perhaps even more as increased demand from domestic and industrial needs can seize the water from agriculture. 7. Adaptation options for the near term As demonstrated there exists a differential vulnerability over time, across sectors and districts. To reduce vulnerability to climate change and variability, the adaptation options are identified for each sector. Adaptation is essentially the adjustment in response to climate stimuli. It may take the form of institutional, technological and behavioural changes. The water sector has had to cope with climate. However, as climate change presents a different challenge, water managers or users need appropriate adjustment to anticipated climate changes accordingly. A common used framework for classifying adaptation measures is based on the study of extreme events such as floods and droughts (e.g. Kates, 1985). This framework places the adjustments to extreme events in three main categories. One is accepting loss, involving bearing losses and sharing losses. Losses can be shared within a wider community or via mechanisms such as insurance and public relief. The second category is preventing effects, that is, to prevent the consequences of climate change from occurring. The last one is changing users or locations, which involves switching to a different use strategy or changing the locations, for example, of a plant. This framework is widely used in the context of adaptation to climate change. This paper adopts this framework and formulates the adaptation options under the three categories. Interviews with water managers in various levels of Tianjin’s government reveal that managers generally do not show great concern to climate change. There are also very few research projects on the impacts of climate change. They are more concerned how to deal with risks and uncertainty associated with climate variability and extreme events. They presume that they will use the same tools or measures to deal with the increased variability induced by climate change. This may arise from that water managers are not fully aware of climate change and its impacts thus the perception of the problem and the relevant adaptation options are not properly developed. It may also be attributed to a lack of standards or guidelines on how to incorporate climate change into government plan and decisions on a national and regional level. Another factor is that water is conventionally managed in a reactive rather than proactive manner. Water management evolves and adjusts from experiencing the changes. Thus far, it remains far from clear how the Tianjin’s water sector should respond to the challenge of climate change.

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Based on the ‘no regrets’ principle, a set of adaptation options is recommended for the water sector for a near term to reduce vulnerability. Table 11 presents the options and opportunities for various water sectors. In all cases diminishing water resources are assumed that are induced by climate change. For municipal and industrial uses as well as rural households, an increasing demand is also assumed. Although many options are suggested, not all of them are considered as equally appropriate. Moving from right to left of the table, the measures change from reactive to more proactive ones and from more technical options to more regulations. Stakeholders need to choose the most appropriate ones by judgements on effectiveness, reversibility, cost effectiveness and feasibility. Table 11 Selected adaptation options that can be implemented in the near term Water use sector

Municipal Households

Industries, commercial firms and institutions

Appropriate adaptation measures Accepting losses

Preventing effects

Changing uses and/or locations

Tolerate increased inconvenience

Construct cisterns and install rain barrels to story rain water

Adopt water conservation practices in the home such as water saving tap and showering appliances

Tolerate increased inconvenience and production losses

Increase on site storage capacities Recycle water used in cooling and processing Make arrangements for alternative water supplies

Increase the use of seawater and brackish water through desalination Upgrade wastewater treatment to improve effluent quality

Municipal officials (e.g. Water Resources Bureau of Tianjin)

Establish contingency plans Seek alternative water sources (e.g. Water conservation initiatives directed for water crisis water transfer from other rivers) to residential and other water users Construct additional reservoir storage

Policies to discourage water intensive industries to grow and encourage industry structural change Leak detection and system optimisation of water network Enhance the water saving in public facilities Upgrade wastewater treatment facilities to improve effluent quality

Haihe River Basin Committee

Enhance conflict resolution mechanisms in water allocation scheme

Enhance river basin management

Coordinate the different interests among upstream and downstream

Regulate the water supply and wastewater discharge within the basin

Establish legal priorities to regulate withdrawal during times of shortage Promote water conservation

National Authority

Provide subsidies to losses Subsidise long distance water due to water shortage transfer

Promote water conservation

Subsidise municipal infrastructure improvement Agriculture Farmers

Seek off-farm employment to complement income

Construct farm ponds to store water Adjust crop structure, switch to less water demanding crops Increase pumping capacity

Improve irrigation efficiency to reduce wastage by irrigation management Switch from low to high efficiency irrigation techniques

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Increase the use of brackish water Irrigation districts

Contingency planning for water shortage

Upgrade canals and storage infrastructure to increase capacity and to reduce losses in transport and storage

Promote efficiency and proper water use practices among water users Adjust operation of district water control and water distribution systems (e.g. system optimisation) Promote use of sprinkle and drip irrigation when appropriate

Tianjin municipality

Establish crop insurance, Construct additional on stream stabilisation and relief reservoirs to increase storage programmes capacity Provide farmers subsidies to Water transfer from other river losses due to water shortage basins Subsidise irrigation districts infrastructure improvement

Strengthen the maintenance of mechanical wells and anti-seepage of channels Promote efficiency and proper water use practices among water users Adjust operation of downstream Haihe river control (automation, system optimisation) Encourage and support the adjustment of crop structures and lands Promote research in good quality seeds, advanced technologies and practices.

8. Conclusions In view of the vulnerable water systems to climate change in North China, the study has emphasized on examining the vulnerability and adaptation in the case of Tianjin. Using times series data, the vulnerability of Tianjin’s water sector is measured by combining the sensitivity and coping capacity indexes. The results suggest that the sensitivity of water sector fluctuates but tends to increase over time while coping capacity is steadily increasing. The overall vulnerability fluctuates and has a slight downward trend, which implies that water sector in Tianjin is becoming more and more vulnerable but not to a great extent. The variation in vulnerability suggests that water sector needs to have robust coping and adaptation measures to deal with the interannual changes. Sectoral analysis reveals that agriculture is the most vulnerable to climate change. Various districts or counties have differential vulnerability, with Jin Nan District being the most vulnerable and Han Gu District most invulnerable. In addition, water shortage, considered as a determining factor that shapes future vulnerability, is estimated for the next fifteen years. The level of water shortage increases as population grows and industry expands. Including water supply from future transfer projects, water shortage is eliminated to a great extent. Nonetheless, given the realisation of the full capacity, water shortage still accounts for 28% of the total demand in 2010 and 23% in 2020. The gaps need to be filled by future adaptation measures. Future opportunities in adaptation for Tianjin’s water sector are presented ranging from reactive measures such as accepting and sharing losses and compensation to proactive measures such as improving water infrastructures, upgrading wastewater treatment system and promoting water conservation programs etc. Many adaptation options are identified, but the most appropriate options have to be decided by the stakeholders under certain circumstances. The challenges in the development of a composite index for vulnerability include the selection of the appropriate indicators, the decision on indicators when there exists a high correlation among

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them and the classification of indicators into sensitivity and coping categories. Lack of quantitative data and difficulty in turning information into a meaningful indicator are also often a problem. When using the Principal Component Analysis, one has to decide whether to apply it to the whole index system or to subsystems. In this study it is employed for the whole system because there are many subsystems, which sometimes contain too fewer indicators. By nature, this vulnerability index can always be improved by having access to more data or for a longer time sequence. Future work in this direction includes comparison of vulnerability of water sector across regions in North China. The results could serve as a basis for governmental planning and decisions in prioritising adaptations in relatively more vulnerable areas. Furthermore, sea level rise needs to be addressed in the framework of vulnerability in especially south coastal regions of China, where sea level rise presents a threat. Acknowledgements This research is funded by the START (System for Analysis, Research and Training) organization through a program on the Advanced Institute on Vulnerability to Global Environmental Change. References McCarthy J.J., Canziani O.F., Leary N.A., Dokken D.J. and White K.S. (eds.) (2001) Climate Change 2001: Impacts, Adaptation and Vulnerability, Contribution of Working Group II to the Third Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge Frederick K.D. and Major D.C. (1997) Climate change and water resources, Climatic Change 37, 7-23 Watson R.T., Zinyowera M.C. and Moss R.H. (eds.) (1997) IPCC Special Report on The Regional Impacts of Climate Change: An Assessment of Vulnerability, Cambridge University Press, Cambridge Ying A.W. (2000) Impact of Global Climate Change on China’s Water Resources, Environmental Monitoring and Assessment 61, 187-191 Kasperson J. X., Kasperson R. E., Turner B. L. II, Schiller,A. & Hsieh W.H. (2003) The Human Dimensions of Global Environmental Change, eds. Diekmann, A., Dietz, T., Jaeger, C. & Rosa, E. S. (MIT Press, Cambridge, MA). Kasperson, J. X., and Kasperson R.E. (2001) A Workshop Summary prepared on behalf of workshop participants, for the workshop held 17-19 May 2001, Stockholm Environment Institute (SEI), Stockholm, Sweden. SEI Risk and Vulnerability Programme Report 200101. Stockholm: Stockholm Environment Institute. )) Turner et al. (2003) A framework for vulnerability analysis in sustainability science, Proceedings of the National Academy of Sciences 100(14), 8074-8079 Schneider et al. (2001) Overview of impacts, adaptation, and vulnerability to climate change in McCarthy et al. (eds) Climate Change 2001: Impacts, Adaptation and Vulnerability, Contribution of Working Group II to the Third Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge Luers at el. (2003) A method for quantifying vulnerability, applied to the agricultural system of the Yaqui Valley, Mexico, Global Environmental Change 13, 255–267 26

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