Mitig Adapt Strateg Glob Change DOI 10.1007/s11027-015-9689-1 O R I G I N A L A RT I C L E
Impacts of climate variability and changes on domestic water use in the Yellow River Basin of China Xiao-jun Wang 1,2,3 & Jian-yun Zhang 1,2 & Shahid Shamsuddin 4 & Ru-lin Oyang 5,6 & Tie-sheng Guan 1,2 & Jian-guo Xue 7 & Xu Zhang 1,2
Received: 21 September 2014 / Accepted: 8 October 2015 # Springer Science+Business Media Dordrecht 2015
Abstract We present a methodology for using a domestic water use time series that were obtained from Yellow River Conservancy Commission, together with the climatic records from the National Climate Center of China to evaluate the effects of climate variability on water use in the Yellow River Basin. A suit of seven Global Circulation Models (GCMs) were adopted to anticipate future climate patterns in the Yellow River. The historical records showed evidences of rises in temperature and subsequent rises in domestic water demand in the basin. For Upstream of Longyangxia region, the impact was the least, with only 0.0021 × 108 m3 for a temperature increase of 1 °C; while for Longyangxia-Lanzhou region, domestic water use was found to increase to 0.18 × 108 m3 when temperature increases 1 °C. Downstream of Huayuankou was the region with the most changes in temperature that gave the highest increase of 1.95 × 108 m3 in domestic water demand for 1 °C of change of temperature. Downstream of Huayuankou was
* Xiao-jun Wang
[email protected] 1
State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing 210029, China
2
Research Center for Climate Change, Ministry of Water Resources, Nanjing 210029, China
3
State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing 100875, China
4
Faculty of Civil Engineering, Universiti Teknologi Malaysia (UTM), 81310 Johor Bahru, Malaysia
5
Bureau of Comprehensive Development, Ministry of Water Resources, Beijing 100053, China
6
Key Laboratory of Water Cycle & Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
7
Yellow River Conservancy Commission, Zhengzhou 450004, China
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identified as the most vulnerable area, where domestic water demand increases nearly by 42.2 % with 1 °C increase of temperature. Judging from the trends of temperature range, we concluded that future temperature in Yellow River Basin has an increasing tendency. This could worsen the existing issues of domestic water demand and even more to trigger high competition among different water-using sectors. Keywords Climate change . Domestic water demand . Water resources management . Yellow River Basin . Regression analysis
1 Introduction Water demand includes water needs for industrial, agricultural, domestic, and ecological purposes (Zhang 2005; Wang et al. 2014c). Changes in water demand depend on many factors including population growth, economic development, climate change, lifestyle change, water-saving practices, technological advances, water price, etc. (Sebri 2014; Wang et al. 2015). It is acknowledged that the pressing problem in securing continuous water demand has to be tackled from an integrated perspective taking into account the environmental, human, and technological factors, and in particular, their interdependence (Sophocleous 2004; Butler and Memon 2006; Pahl-Wostl 2007; Magini et al. 2008; Henderson et al. 2013; Babel et al. 2007; Wang et al. 2014a). The present study concentrated on one of the critical factors, namely the influence of climate change on water demand. It is widely accepted that water demand will increase due to the rise of temperatures and changes in precipitation patterns. In this regard, a number of studies have been carried out to estimate the influence of climatic variables on water demand (Maidment et al. 1985; Herrington 1996; Bougadis et al. 2005; Ghiassi et al. 2008; Zhou et al. 2000; Khatri and Vairavomoorty 2009; Caiado 2010; Chaturvedi et al. 2013; Wang et al. 2014a; Yuan et al. 2014). Various methods have been proposed which include linear regression, Box and Jenkins model (Maidment et al. 1985), artificial neural network (Bougadis et al. 2005; Ghiassi et al. 2008; Khatri and Vairavomoorty 2009), system dynamics (Wang et al. 2013, 2014d), and other methods (Alvisi et al. 2003). Those studies intended to understand the impacts of climatic variables such as rainfall, air temperature, sunshine duration, relative humidity, wind speed, etc. on daily, weekly, monthly, seasonal, and annual water demands. Results revealed that climatic conditions and water use are significantly correlated. Yet, generalization of these results by various authors to cater for domestic water demand remains a difficult task. The present world’s population is around 7.2 billion, which is projected to grow continuously until 2070 (UN-Habitat 2013). The residential water demand at global scale will also grow with the increase of global population. Increasing water demand will put tremendous pressure on water supply system, particularly in urban areas. The urban population in 2014 accounted for 54 % of the total global population, and it is expected to grow at a rate of 1.84 % per year (WHO 2014). As more people move to urban areas, cities around the world are experiencing increased water stress (McDonald et al. 2014). The large cities occupy only 1 % of global land surface but draw water from almost half of the Earth’s surface. This huge demand of water in small areas has caused water scarcity in one out of four large cities of the world, which containing almost 400 million people and accounting for $4.8 trillion of economic activity or more than 5 % of global gross domestic product (GDP) (McDonald
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et al. 2014). It has been projected that the world’s population will reach to 9.6 billion, and about 66 % of the world’s population will be urban in 2050 (United Nations 2014). Even in less developed countries, a majority of people will be living in urban areas by 2017 (World Health Organization 2014). As the world continues to urbanize, urban water managers will face a growing challenge to maintain safe and adequate water supplies. The challenges will increase further as the water supplies will be affected by climate change driven alterations in water demand (Loftus 2011). It has been anticipated that a large population will be under water stress due to increased water demand, if proper adaptation measures are not taken (Wang et al. 2014c, 2015). A clear understanding of water demand changes in response to the variations in the climate can help in the planning and management of water resources, in order to be able to adapt to the changing environment (Elmahdi et al. 2009; Belton and Miller 2014; IPCC 2014; Wang et al. 2014b, 2015). Domestic water demand covers water needs for residential purposes such as in-house water use for drinking, food preparation, bathing, washing, toilet flushing, etc. as well as outdoor water needs for gardening, lawn watering, etc. (Alvisi et al. 2003; Alaa and Nisai 2004; Garcia et al. 2004; Blokker et al. 2010; Wang and Fu 2011). Studies related to climate change impacts on domestic water demand in different regions of the world are discussed below. A number of studies revealed that the increase of domestic water demand is due to the increase of evapotranspiration caused by higher temperature. For instance, Frederick (1997) studied urban water use in four mountainous counties of Utah, United States (US) and suggested that 1 % rise in temperature would cause an increase in residential water demand between 0.02 and 3.8 %, while 1 % decrease in precipitation would cause an increase in residential water demand between 0.02 and 0.31 %. A statistical analysis of water use in New York City, New York, US showed that for the temperature exceeding 25 °C, daily per capita water use increases by 11 L/ 1 °C (Protopapas et al. 2000). Neale et al. (2007) studied the climate change impacts on residential water demand in Okanagan Basin of British Columbia, Canada and reported that residential water demand would increase by 0.0031 to 0.0111 ML for every 1 °C increase in monthly mean daily maximum temperature. The following are reported from semi-arid zone. Guitzler and Nims (2005) studied the effects of inter-annual climate variability on water demand in Albuquerque, New Mexico, US and reported that over 60 % of the variance of year-to-year changes in summer residential demand was accounted for inter-annual temperature and precipitation changes. Zachariadis (2010) studied the residential water scarcity in Cyprus due to climate change and projected that climate change would aggravate the already existing water scarcity in Cyprus by 2030. Price et al. (2014) examined the influence of climatic variables on annual residential water use in the city of Phoenix, Arizona, US and reported that temperature, precipitation, and/or drought conditions have significant impacts on residential water use. On the other hand, arid zone gives a different scenario. Karamouz et al. (2011) projected no appreciable change in average domestic water demand in Tehran, Iran due to climate change. Changes in domestic water demand may not be significant in some regions due to the increase of precipitation. Overall changes in domestic water demand depend on how much the increased rainfall could balance the increased evapotranspiration loss due to the temperature rise. Downing et al. (2003) also projected that the increase in household water demand due to climate change is likely to be rather small, e.g., less than 5 % by 2050s. Jampanil et al. (2012) estimated indirect increase in domestic water demand only by 0.03 % in Rayong Province of Thailand due to climate change. Increase of water demand due to temperature rise was also reported by Kenney et al. (2008), Harlana et al. (2009), Polebitski et al. (2011) and Lott et al. (2013).
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The Yellow River is the second largest river in China, which plays a key role not only in the country’s economic development but also in the historic and cultural identity of the Chinese people. From the upper to the lower reaches, we usually divide the basin into eight regions namely, Upstream of Longyangxia (UL), Longyangxia–Lanzhou (LL), Lanzhou–Hekouzhen (LH), Hekouzhen–Longmen (HL), Longmen–Sanmenxia (LS), Sanmenxia–Huayuankou (SH), Downstream of Huayuankou (DH), and the Interior Area (IA), respectively, as in Fig. 1. The Yellow River Basin, designated as the cradle of Chinese civilization, accommodates approximately 9 % of China’s population and 17 % of its agricultural area (Liu and Zhang 2002; Xu et al. 2002; Giordano et al. 2004; Wang et al. 2012a). Population in the basin has grown rapidly in recent years, and this significant increase has widen the gap between water supply and demand (Giordano et al. 2004; Barnett et al. 2006; Goncalves et al. 2007; Wang et al. 2012a). In 2012, the total water demand in the Yellow River Basin was 523.6 × 108 m3, which includes 392.97 × 108 m3 of surface water, and 130.63 × 108 m3 of groundwater. River water source is heavily abstracted for domestic use along the river, and groundwater is heavily relied on by the Northern cities. This has caused water resources to deplete to alarming level, and the shortage of water has triggered conflicts among the dwellers in the Yellow River Basin (Wang et al. 2012a). Besides, climate change is also one of the factors that influence the domestic water demand, and therefore, it is necessary to analyze the effects of climate change on domestic water use in the river basin. The objective of this study was to assess the impacts of climate change on domestic water demand in different sub-basins of the Yellow River Basin. In this paper, we presented a methodology of using time series of domestic water use that were obtained from the Yellow River Conservancy Commission, together with the climatic records from the National Climate Center of China to evaluate the effects of climate variability on domestic water use in the basin
Fig. 1 The Yellow River Basin and its sub-basins
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in the past years. We also referred to long-term projection suggested by the Representative Concentration Pathway (RCP) scenarios adopted by the IPCC Fifth Assessment Report (AR5). Out of the four possible projected climate futures, we chose the scenario RCP 4.5 to represent the onset of global warming in the Yellow River Basin by 2100. A suit of seven Global Circulation Models (GCMs) were adopted to anticipate future climate patterns in the Yellow River Basin. It is expected that the study would help development and planning authorities as well as policymakers to improve their understanding of climate change impacts on water resources and would also assist them in adopting policy responses. The methods presented in this study assess the impacts of climate variability on residential water demand and can be replicated in other regions to understand the changes in residential and water demand in order to develop adaptation responses and measures.
2 Materials and methods 2.1 Data and sources Population growth, domestic water use, and economic development data from 2000 to 2012 were extracted from the published China Water Resources Bulletins (MWR 2000–2012). Recent data of domestic water use for the regions of UL, LL, LH, HL, LS, SH, DH, and IA in the Yellow River Basin were collected from the Yellow River Water Resources Bulletins (YRCC 2000–2012).
2.2 Linear regression analysis Statistical methods are generally used to assess the sensitivity of water use to climate variability (Guitzler and Nims 2005; Balling and Gober 2007; Sarker et al. 2013; Chang et al. 2014). Statistical forecasting methods rely on historic data to define relationships between independent (water use) and dependent (climate) variables (Butler and Memon 2006). These relationships are then used to predict future domestic water demand. Linear regression method has been used in most of the studies to understand the impacts of rainfall and temperature on residential water use (Balling and Gober 2007; Sarker et al. 2013; Chang et al. 2014) as it is computationally easier but reliable. In statistics, linear regression is an approach for modeling the relationship between a scalar dependent variable, y, and one or more explanatory variables denoted, x. In case of more than one explanatory variable, the process is called multiple-linear regression (Wang and Fu 2011; Sajil Kumar et al. 2013). In this study, linear regression model was used, in which data were modeled using linear predictor functions, and unknown model parameters were estimated from the collected data. We used domestic water use data as y and temperature change as x. As such, the linear regression model in which the conditional mean of domestic water use y given the value of temperature change x is an affine function of temperature change, as in Eq. 1: y ¼ β0 þ β 1 x þ ε
ð1Þ
Linear regression models are often fitted using the least squares approach, but they may also be fitted in other ways, such as by minimizing the lack of fit in some other norms, or by
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minimizing a penalized version of the least squares loss function as in ridge regression (Wang and Fu 2011; Sajil Kumar et al. 2013), ^1 ¼ ^0; β min Qðβ 0 β 1 Þ ð2Þ Q β −∞< β 0 β1 < ∞
Then, it can be represented as: 8 ^ 0 ¼ y−β 1 x > β > > > n > X > > < xi −x yi −y ^ 1 ¼ i¼1 > >β n > 2 X > > > > x −x i :
ð3Þ
i¼1
Where n
x¼
n
1X 1X xi ; y ¼ y n i¼1 n i¼1 i
Then ^0 þ β ^1x ¼ ^y ¼ β
^ 1 x−x ¼yþβ
ð4Þ
Where β1 is the slope, meaning the changes in domestic water use when temperature increases by 1 °C; thus, we define this as climatic elasticity of water use. The linear regression method assumes that the data are normally distributed. Therefore, test of normality of data was carried out using the Kolmogorov–Smirnov one sample test. The Kolmogorov–Smirnov statistic for a given cumulative distribution function F(x) is Dn ¼ supx j F n ðxÞ−F ðxÞj
ð5Þ
Where F(x) is the empirical distribution function based on the random sample X1, X2,…, Xn; F(x) is the hypothesized distribution function, which is considered as normal distribution in the present study; and supx is the greatest vertical distance between Fn(x) and F(x). If the sample comes from distribution F(x), then Dn converges to 0. In the present study, a significance level of 0.05 was considered to test the normality of water use and temperature data.
3 Effects of climate change 3.1 Changes of domestic water use in the Yellow River Basin The basin area of Yellow river has to provide adequate water supply for nine provinces including Qinghai, Sichuan, Gansu, Ningxia, Inner Mongolia, Shanxi, Shaanxi, Henan, and Shandong. Moreover, Qinghai, Gansu, Ningxia, and Inner Mongolia are in the arid region, while Sichuan and Henan are the most densely populated areas of China. Notwithstanding the rising demand, recurrence of water shortage has been frequent in recent years. Especially in 1997, there were 226 days of Bno flow^ when no river waters reach the Bohai Sea. This caused serious water stress in the basin’s development (Liu and Zhang 2002; Liu and Xia 2004;
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Wang and Fu 2011). With the population growth in recent years, domestic water demand has gained sharp increase. Water uses by different sectors in the Yellow River Basin are illustrated in Fig. 2. Domestic water consumption is one of the main users of the surface and groundwater sources. The problem of water shortage progressively become more acute than ever before because of the population growth in the middle reaches of the Yellow river. Some of the main tributaries in the middle reaches, such as Kuye River, Tuwei River, etc., showed a decreasing trend of runoff. Some of the tributaries even dried up in recent years causing serious impacts to the natural ecological environment, and on the whole, interrupting the continuance of water cycle in the sub-basins (Liu and Zhang 2002; Wang and Fu 2011).
3.2 Regression analysis of domestic water uses over surface temperature With the changing climate and domestic water use during past years, analysis was carried out to evaluate the effects of climate change on domestic water use. It is obvious that population is the single largest factor that determines long-term trend in residential water demand. The upward trend in water demand in the Yellow River Basin over the time period also follows the increase of population closely. Therefore, it was required to isolate the component of water use that is related to climate variability. A simple linear regression with water use as the dependent variable and population as independent variable was used to remove the influence of population on water use. Similarly, the effect of GDP on water demand was removed as some studies argued that GDP has significant impact on residential water use. The rectified water use and temperature time series’ were then tested for normality using the Kolmogorov–Smirnov test. The result revealed that the temperature and water demand data were normally distributed at a significance level of 0.05. Therefore, regression analysis of historical temperature and domestic water use was conducted to estimate the effects of climate variability on domestic water use. The relations between domestic water use and temperature in different sub-basins of the Yellow River Basin are shown in Fig. 3. The results showed that changes in temperature have direct effects on domestic water demand in the Yellow River Basin. As for different regions, the changing climate has different effects. The effect seems the least for UL, only 0.0021 × 108 m3 when temperature increases 1 °C. For LL region, domestic water increases 0.18 × 108 m3. DH suffers direct hit for having the highest increase of 1.95 × 108 m3 for 1 °C of temperature hike. But, as distribution of population differs from region to region, we resorted to use an index of climatic elasticity to reflect the severity of climatic change over a region. Table 1 shows the said index according to regions in the river basin.
(a) Surface Water
(b) Groundwater
Fig. 2 Water usage by different sectors in the Yellow River Basin. a Surface water, b groundwater
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(a) Upstream of Longyangxia (UL)
(b) Longyangxia-Lanzhou (LL)
(c) Lanzhou-Hekouzhen (LH)
(d) Hekouzhen-Longmen (HL)
(e) Longmen-Sanmenxia (LS)
(f) Sanmenxia-Huayuankou (SH)
(g) Downstream of Huayuankou (DH)
(h) Interior Area (IA)
Fig. 3 Relationships of temperature and domestic water use in Yellow River Basin. a Upstream of Longyangxia (UL), b Longyangxia–Lanzhou (LL), c Lanzhou–Hekouzhen (LH), d Hekouzhen–Longmen (HL), e Longmen– Sanmenxia (LS), f Sanmenxia–Huayuankou (SH), g Downstream of Huayuankou (DH), h Interior area (IA)
From the table, it can be remarked that Downstream of Huayuankou (DH) is the most vulnerable region, where the domestic water demand increases nearly by 42.2 % for every rise of temperature by 1 °C.
3.3 Impacts climate change of domestic water use As for the future, we used RCP 4.5 to represent global warming projections in the Yellow River Basin (Arnell and Lloyd-Hughes 2013). RCP 4.5 is an intermediate pathway which assumes that varying degrees of mitigation will lead to an end of century temperature anomaly
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of 2.4 °C. The RCP 4.5 is a scenario assumes stabilization of radiative forcing at 4.5 W/m2 in the year 2100 without ever exceeding that value. While there are many alternative pathways to achieve a radiative forcing level of 4.5 W/m2, the application of the RCP4.5 provides a common platform for climate models to explore the climate system response to stabilizing the anthropogenic components of radiative forcing (Thomson et al. 2011). The latest policy of the Chinese government is environmental sustainability and lower green house gas emission. Recently, China has committed to reduce greenhouse gas emission by expanding the share of zero-emission sources in primary energy to 20 % by 2030. Comparison of land use and land cover (LUCC) changes in China under the RCP 4.5 scenario and real land use structures revealed that the simulated LUCC is more close to the real conditions (Deng et al. 2014). Therefore, RCP 4.5 scenario was chosen in the present study. Downscaling of seven GCMs were analyzed to identify the future changing climate in the Yellow River Basin as in Fig. 4. The GCMs were Beijing Climate Center Climate System Model version 1 (BCC-CSM1-1), Beijing Normal University Earth System Model (BNU-ESM), Centre National de Recherches Météorologiques Climate Model version 5 (CNRM-CM5), Goddard Institute for Space Studies Model E version 2 with Russell ocean model (GISS-E2-R), Model for Interdisciplinary Research on Climate- Earth System (MIROC-ESM), Max-Planck Institute Earth System Model-Low Resolution (MPI-ESM-LR), and Meteorological Research Institute Coupled General Circulation Model version 3 (MRI-CGCM3). The downscaled temperatures were used to understand the future changes in temperature in the Yellow River Basin. The climate variability temperature data suggests that future ambient temperature in the Yellow River Basin will increase. This could worsen the existing issues with domestic water demand, and even more to trigger high competition among different water-using sectors, and may demand more attentions for management.
Table 1 Climatic elasticity of domestic water demand in the Yellow River Basin Average domestic water use (108m3)
Climatic elasticity (108m3/°C)
13.1
0.088
0.002
2.3
9.1
1.481
0.182
12.3
Lanzhou–Hekouzhen (LH)
16.3
2.375
0.379
15.9
Hekouzhen–Longmen (HL)
11.2
0.892
0.317
35.5
Longmen–Sanmenxia (LS)
19.2
6.86
1.186
17.3
Region
Area (104km2)
Upstream of Longyangxia (UL) Longyangxia–Lanzhou (LL)
Increase of domestic water use (%) due to climate change
Sanmenxia–Huayuankou (SH)
4.2
2.207
0.346
15.7
Downstream of Huayuankou (DH)
2.2
4.629
1.953
42.2
Interior area (IA)
4.2
0.053
0.010
18.9
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Fig. 4 Future climate changes scenario in the Yellow River Basin
4 Discussion and conclusions In this paper, time series of domestic water use from the Yellow River Conservancy Commission and the climatic records from the National Climate Center of China were collected to evaluate the effects of climate variability on domestic water use in the Yellow River Basin. The historic records showed evidences of rises in temperature and subsequent rises in domestic water demand in the basin. For Upstream of Longyangxia (UL), the impact was the least, with only 0.0021 × 108 m3 for a temperature increase of 1 °C; while for Longyangxia–Lanzhou (LL) region, domestic water use was found to increase to 0.18 × 108 m3 when temperature increases by 1 °C. Downstream of Huayuankou (DH) was the region with the most changes in temperature that gave the highest increase of 1.95 × 108 m3 in domestic water demand for 1 °C increase of temperature. Downstream of Huayuankou (DH) was identified as the most vulnerable area, where nearly 42.2 % in domestic water demand increases with 1 °C increase of temperature. In order to ensure reliability of domestic water supply, forecast of future climatic trends would be necessary. Many climate models are available, and climatic parameters are shared by reputed institutions. With these, it is increasingly at ease to provide forth coming insights to any threats of water supply. RCP scenario is demonstrated here to cultivate an awareness of global warming and its implications to Yellow River Basin. Increasing temperature is demonstrated here to have made water supply critical in parts of the Yellow River Basin like many other parts of the world. Increased domestic water demand will certainly create a major challenge for water supply management. The world’s population is expected to reach more than 9.6 billion by 2050, and 66 % of the world’s population will be urban (United Nations 2013). This will certainly increase domestic water demand, particularly in urban regions. This means that increased residential water demand due to climate change will aggravate the water crisis in many regions of the world that are already facing water shortages due to growth in the economy and in population. Increased water demand might increase conflicts between different water users, including in-stream needs for retaining ecosystem sustainability. Therefore, the sustainable management of water resources in the context of a changing environment is a growing concern among policymakers.
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Usually, water supply management strategies are adopted to meet the growing water demand. However, it is always not be possible to meet the increasing demand using supply augmentations, as potential sources of water are limited (Wang et al. 2012b). Furthermore, financial resources in many developing countries are insufficient for making the water system investments that are required for supply augmentation (Wang et al. 2014c). Water demand management (WDM) refers to any technical, economic, administrative, financial, or social approaches to reducing the quantity or quality of water required to accomplish a specific task (Butler and Memon 2006; Wang et al. 2011, 2014c; Global Water Partner 2012). Wang et al. (2014c) set up a framework for implementation of WDM in the middle reaches of Yellow River. Through the analysis of water use data, they showed that WDM can reduce water demand to a certain extent, without hampering economic development and without putting significant constraints on society (Wang et al. 2014c). Therefore, in consideration of the fact that sources of water are limited, it is recommended that more emphasis be given to water demand management. It is expected that adaptation through water demand management practices, it is possible to mitigate the negative impacts of climate change and improve the livelihood of mass population. It is hoped that the method presented in this paper to analyze the effects of climate change could assist water managers to have a better understanding on rapid assessment of water demand in the context of changing scenarios. Some important questions to be pondered with: (1) what kind of procedures is best to assess the climate change impacts? In this paper, we presented linear regression analysis to estimate the effects of climate change on domestic water use. Bear in mind that changes in domestic water demand are influenced by many factors. Having only the linear relationship between domestic water demand and temperature change is simple to use and easy to understand. Future research should consider other factors; (2) Forecast of long-term impacts on domestic water demand should be taken seriously. There are uncertainties pertaining to the future climate models. Reasonable forecast would guarantee wise planning of water resources. Climate variability records and simulation modeling results suggest ambient temperature change will affect domestic water supply in the Yellow River Basin. Therefore, it seems reasonable to speculate that global scale climate variability may influence water supply in other river basins around the world. River basin managers seek tools to address climate variability. It is hoped that the methods presented in this paper can assist resource managers and decision makers to rapidly assess water demand trends and develop appropriate response measures for application at local, national, and global scales. Acknowledgments We are grateful to the National Natural Science Foundation of China (No. 51309155, 41101030, 41330854), National Basic Research Program of China (No. 2010CB951104 and 2010CB951103), China Postdoctoral Science Foundation funded project (No. 2013M530027), Central Public-interest Scientific Institution Basal Research Fund (No. Y513004), China water resource fee funded project (No. 1261530210034), Special Fund of State Key Laboratory of China (No. Y514010, Y515023), Open Research Fund of State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin (China Institute of Water Resources and Hydropower Research Project No. IWHR-SKL-201515) and the Asia-Pacific Network for Global Change (Grant No. ARCP2013-25NSY-Shahid) for providing financial support for this research. We are also thankful to anonymous reviewers and editors for their helpful comments and suggestions.
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