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Bottled Water: United States Consumers and Their Perceptions of Water Quality

Lois wright morton, robert l mahler.

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Author to whom correspondence should be addressed; E-Mail: [email protected] ; Tel.: +1-515-520-0727; Fax: +1-515-294-2303.

Received 2011 Jan 1; Revised 2011 Jan 27; Accepted 2011 Feb 15; Issue date 2011 Feb.

This article is an open-access article distributed under the terms and conditions of the Creative Commons Attribution license ( http://creativecommons.org/licenses/by/3.0/ ).

Consumption of bottled water is increasing worldwide. Prior research shows many consumers believe bottled water is convenient and has better taste than tap water, despite reports of a number of water quality incidents with bottled water. The authors explore the demographic and social factors associated with bottled water users in the U.S. and the relationship between bottled water use and perceptions of the quality of local water supply. They find that U.S. consumers are more likely to report bottled water as their primary drinking water source when they perceive that drinking water is not safe. Furthermore, those who give lower ratings to the quality of their ground water are more likely to regularly purchase bottle water for drinking and use bottle water as their primary drinking water source.

Keywords: bottled water, water quality perceptions, ground water quality

1. Introduction

Consumption of bottled water is increasing by ten percent every year worldwide, with the fastest growth seen in the developing countries of Asia and South America [ 1 ]. The United States (U.S.) is the largest consumer market for bottled water in the world. The U.S. consumption of bottled water in 2008 was estimated to be 8.6 billion gallons, or 27.6 gallons per person [ 2 ]. Despite the common belief that bottled water is safer to drink and has better taste than tap water, scientific studies have shown that the belief is not necessarily true [ 3 , 4 ]. Research also shows that the sales and consumption of bottled water can have environmental and social impacts whose consequences are yet to be fully understood [ 5 – 7 ]. After years of substantial growth in sales, the U.S. bottled water market is recently slowing down. The current economic downturn may have played a part in the drop; however, environmental concern is also an important factor. Some research has found that environmental awareness campaigns may have curbed consumer demand [ 8 – 10 ].

Previous studies about bottled water have focused on its production, regulation, sales and consumption, and criticism and concerns. However, few researchers have examined the relationship between consumer use of bottled water and perceptions of drinking water quality. In this article, the authors explore the demographic and social factors associated with bottled water users in the U.S. and the relationship between bottled water use and perceptions of the quality of local water supply. A brief discussion of bottled water and tap water and bottled water consumers is used to develop several hypotheses. These hypotheses are tested using a national dataset representing twenty-one U.S. states. Results and discussion are followed by implications directed toward educators and public policy makers as they fund and develop programs that promote knowledge about health and local drinking water.

1.1. Bottled Water vs. Tap Water

Bottled water has been used in place of tap water for its convenience, better taste, and perceived purity [ 1 , 3 , 11 ]. Perceptions of bottled water being of higher quality, however, are challenged by the increasing number of water quality incidents with bottled water [ 12 ]. A study showed that only five percent of the bottled water purchased in Cleveland, Ohio had the required fluoride recommended by the state, whereas the sampled tap water 100% met this requirement [ 3 ]. The same experiment also conducted bacteria count on both bottled water and tap water samples. The result showed that all of the tap water samples had a bacterial content under 3 CFUs/mL (colony-forming unit, a measure of viable bacterial or fungal numbers) and the bottled water samples' bacterial content ranged from 0.01–4,900 CFUs/mL. Although most of the water bottle samples were under 1 CFU/mL, there were 15 water bottle samples containing 6–4,900 CFUs/mL [ 3 ]. Another study focusing on the temperature and duration of storage for bottle water found that the bacterial growth in bottled water was markedly higher than that in tap water, especially at higher temperatures [ 4 ].

Many scientific reports on bottled water urge increased public awareness and development of guidelines/regulations on the industry of bottled water [ 1 ]. Incidents with bottled water quality are largely reported as associated with lenient regulations on bottled water. Bottled water plants are subject to the U.S. Food and Drug Administration (FDA) monitoring and inspection. Despite specific inspection requirements, bottled water plants are given low priority for safety inspection compared with other food plants because of FDA’s staffing and financial constraints [ 13 ]. The “Nutrition Facts” label on bottled water usually shows only limited information about the water [ 1 ].

Despite the popularity of bottled water in the U.S., there are a number of environmental and social concerns. Plastic bottles are a waste problem adding to landfill overload when not recycled. Water bottling plants have impacts on local groundwater aquifers and streams [ 5 ]. Taking too much water can reduce or deplete groundwater reserves and reduce the flow of streams and lakes, causing stress on ecosystems. Although 75% of the world bottled water is produced and distributed on a regional scale, trading and transporting the other 25% bottled water also raises the concern for pollution and carbon dioxide emission [ 6 ]. The price of bottled water is on average 500 to 1,000 times higher than that of tap water [ 6 ], contributing to concern for affordable access to drinking water. Limited resource populations that use bottled water for drinking are least able to afford the high cost associated with bottled water [ 1 ]. Another issue associated with increased consumption of bottled water is that it can erode public tap water revenues and the capacity of governments to provide necessary improvements in basic water infrastructure [ 7 ].

1.2. Consumers of Bottled Water

Eighty-five million bottles of water are consumed in the United States every day and more than thirty billion bottles a year [ 14 ]. The adoption of a health preventive action like drinking bottled water is suggested to be influenced by perception of risk associated with drinking water [ 15 ]. The perception of risk is also thought to be closely related to the subjective assessment of drinking water quality [ 11 ]. This suggests that perceptions of drinking water safety and beliefs about the ground and surface water quality in a local area might be explanatory factors for a decision to select bottled water over tap water.

Another safety factor influencing consumer decision to select bottled water over tap water is the type of water supply system where the consumer lives. Small water systems (small town, tribal system, rural water district) [ 16 ] in the U.S. were found to have problems complying with federal/state quality standards. According to one study, due to inadequate funding and facilities, small water systems reportedly violated federal drinking water regulations more frequently than larger ones [ 11 ]. Although the number of public water consumers whose water does not meet current standards has decreased significantly over years, the task of water regulation is still challenging given both the financial limitations and increasing public concern about their drinking water [ 11 ].

Socio-economic status is also a factor affecting consumer decisions, particularly given the high cost associated with bottled water. Gender and education differences have been found to affect preference of bottled water over tap water because of their noted differences in perception of environmental risk [ 11 , 17 ].

Risk perception and preventive behaviors are the result of complicated social, cultural, and psychological factors as well as objective information [ 18 ]. This suggests that because of the differences in economic, social, and environmental contexts, residents of different regions might have different attitudes towards bottled water. In an earlier study, the findings showed that people in the Pacific region had more per capita consumption of bottled water than in other places of the U.S. [ 11 ]. In this article, the regional factor is examined and the popularity of bottled water is mapped across geographic regions.

2. Experimental Section

2.1. hypotheses.

Prior studies of bottled water consumption have identified a variety of explanatory factors for consumption behavior. However, these factors have not been considered together in one single model. For example, the regional differences found between the Pacific and the rest parts of the U.S. might be due to confounding factors such as differences in community size, local water quality problems, or water supply systems. Therefore, we propose to test these variables of interest simultaneously using a logistic regression. Hypotheses regarding use of bottled water are as follows:

H1: Perceptions of poorer groundwater and surface water quality represent higher risk in drinking water and therefore are hypothesized to be associated with higher likelihood of purchasing bottle water as a primary drinking source compared to those reporting perceptions of higher water quality. Related, perceptions that drinking water is not safe are associated with higher likelihood of purchasing bottled water for drinking as a primary water source.

H2: Based on the observations about small water supply systems, we hypothesize that small water supply (community well and rural district) users are more likely to use bottled water for drinking compared to public municipal water supply users. Community size is used as a control variable.

H3: Because of the environmental impact associated with bottled water, we test the association between environmental attitudes and bottled water use. The association between the two is hypothesized to be that the more pro-environmental views a person holds, the less likely the person frequently uses bottled water for drinking.

H4: We hypothesize a regional effect on the use of bottled water, although the specific pattern about such regional differences is not clear at this stage.

Other variables tested in the logistic model include age, education, and gender.

2.2. Methodology

Data used for this study were collected from a national stratified random sample mail survey about water issues conducted by Dr. Robert Mahler of University of Idaho. Our analysis used data from twenty-one states, which partially cover five out of the ten U.S. EPA water regions [ 19 ]. Data were collected 2004 through 2009 (region 8 and 9, 2004; region 7, 2006; region 6, 2008; and region 4, 2009). Sample sizes for each state were calculated based on the state population and targeted sampling error of four to six percent, with anticipation that the return rate would exceed fifty percent [ 20 ]. In each individual state, samples were either randomly selected from phone books or obtained from a professional social sciences survey company (Survey Sampling International, Norwich, Connecticut). The questionnaires were pilot tested, revised, and then mailed to sampled names and addresses. The final sample size was 5,823. Standard mail survey methods [ 21 ] were followed in all the regions and institutional review board (IRB) approval was obtained from University of Idaho Office of Research Assurance prior to the survey process. Response rates of each state ranged from 37% to 70%, with median return rates reaching the targeted 50%. The questionnaires, generally about 50 questions, varied in their content and wording due to the regions’ differing priorities. However, there were a number of core questions that all states asked. It is these questions in common that make up our data set. These core survey items asked about respondents’ perceptions of water quality, use of bottled water, water supply type, general environmental attitudes, and demographic information.

Two sources of drinking water questions were of interest in this study. The first one was “where do you primarily get your drinking water.” Possible responses to this question included: private supply (private well, river, pond, lake, etc. ), public municipal supply, small water supply systems (including rural water district and community well), and purchase bottled water. If respondents chose “purchase bottled water” for this question, they were identified as primary users of bottled water.

The second question asked if the respondent “often use bottled water for drinking purposes.” If respondents answered “yes” to this question, they were labeled as regular users of bottled water. The above two questions were not mutually exclusive, which means that a primary bottled water user may be a regular bottled water user.

First, we tested hypotheses one, three and four on the primary bottled water users using a logistic regression model. The independent variables used in this logistic regression were as follows:

Surface and ground water quality perceptions. Respondents were asked to rate the surface and ground water quality in their area. Responses were coded 1 = poor, 2 = fair, 3 = very good/excellent.

Drinking water safety. The original question asked if the respondents felt their home drinking water is safe to drink. Response options were 0 = no, and 1 = yes.

Environmental attitudes . Respondents were asked to indicate where they stand on environmental issues by placing a mark on a line with numbers 1 to 10, where 1 represented preference for total natural resource use and 10 represented preference for total environmental protection.

Community size. Community size was measured by asking respondents to choose from the options which best described their community size, although no strict definition was given to the term “community”. Community sizes were measured with five categories. 1 was “less than 3,500 people”; 2 = “3,500 to 7,000”; 3 = “7,000 to 25,000”; 4 = “25,000 to 100,000”, and 5 was “more than 100,000.”

Age and gender . Age was a continuous variable measuring the ages of respondents, and gender was recorded as 0 = female and 1 = male.

Education . Five categories of formal education levels were provided to choose from, ranging from “less than high school” to “advanced degree.”

Residence region . The two bottled water questions of interest were asked in the following regions and states, which include several states of the southeast region (Region 4: Alabama, Florida, Mississippi, Tennessee); the southern region (Region 6: Arkansas, Louisiana, Oklahoma, Texas); the Midwest Heartland region (Region 7: Iowa, Kansas, Missouri, Nebraska); the mountain region (Region 8: Colorado, Montana, North Dakota, South Dakota, Utah, Wyoming); and the southern Pacific region (Region 9: Arizona, California, Nevada [ 22 ]). Figure 1 gives a visualization of the above states and regions.

Map of the Sampled Regions and States.

Secondly, we applied a logistic regression on the regular bottled water users. With this part of analysis, we focused on the respondents who used sources other than bottled water for primary drinking purposes but reportedly often used bottled water for drinking. The hypothesis to be tested with this model is the second one, and the independent variable of primary interest is water supply type, which has three categories: 1 = private supply (private well, river, pond, lake, etc. ), 2 = public municipal supply, and 3 = small water supply systems (including rural water district and community well). All the other independent variables used in the previous model were also included in this logistic regression model.

3. Results and Discussion

3.1. descriptive summary of the sample.

The demographic distribution of survey respondents was similar to that reported for the general adult population based on the 2000 US census data for the demographic factors of community size, age (adult population), and formal education level. The only factor not in line with 2,000 census data was gender. Here, male respondents were much more heavily represented compared to the general population as a whole (about two thirds of the respondents were male, see Table 1 ). Even though 50% of the mailed surveys were addressed to females, it was apparent that the male adult in the surveyed household was more likely to respond to the survey [ 20 ]. The summary of sample statistics is shown in Table 1 below.

Summary Statistics.

Over 13% of all respondents reported that they used bottled water as the primary source for drinking water, while 45.4% of all respondents said they often used bottled water for drinking. The mean for surface water quality perception was 1.99 (fair), and the mean for ground water quality perception was 2.22 (slightly above fair), a little higher than that of surface water. About fifteen percent respondents said they felt their home drinking water was not safe to drink. This percentage corresponded well to the percentage of respondents that used bottled water as their primary drinking source. On a scale of 1 to 10, average environmental attitude score was 5.76, and responses tended to cluster in the middle of the 1 to 10 scale. Thirty-five percent respondents marked their environmental view as 5, midway between totally eco-centric and totally anthropocentric. Other responses with higher percentage are 4 (9%), 6 (15%), and 7 (16%). About 12% respondents responded with higher scores (8–10), and the lower extreme scores (1–3) are only 6% of the total responses. This represents a balanced, somewhat more pro-environmental view towards the relationship between protection of nature and human use of natural resources. Mean age of the survey respondents was 56.8, while average formal educational achievement was between “some college” and “college degree.” About two thirds of the respondents were male.

3.2. Logistic Regression Model 1: Primary Bottled Water Users

Our first model used a logistic regression model to examine the relationship between primary bottled water users and water quality perceptions ( Table 2 ).

Logistic Regression for primary bottled water users (N = 3,232).

P < 0.10;

P< 0.05;

< 0.001.

We found that groundwater quality perception was a significant predictor. As the ground water quality perception increased by one ascending-ordered category, the odds of a person using bottled water as primary source of drinking water was reduced by 33%. Compared with a person who feels their home water is safe to drink, a person who does not trust their home drinking water safety was more than 4.8 times more likely to use bottled water as their primary source of drinking water. However, there was no significant difference in bottled water use among respondents with different surface water quality perceptions. Environmental attitudes were not a significant predictor for primary bottled water use.

Age and gender were also found to be significant predictors for bottled water use. When all other conditions were exactly equal, a respondent who was one year older in age was about 2% less likely to use bottled water as the primary source of drinking water. From a gender standpoint, the odds that a female uses bottled water for primary drinking source are 1.32 times as much as the odds for a male, with all other conditions being equal. Education level was not a significant predictor for bottled water use.

Place of residence was found to have important effect on the use of bottled water. For example, community size had a positive relationship with being a primary bottled water user. As the community size increased by one ascending category, the odds of the resident of larger community using bottled water for primary drinking purposes were increased by 0.116 times. The use of bottled water as primary source of drinking water was also closely related to where the respondents lived in the U.S. For example, a respondent in the Midwest (region 7), when compared with a respondent living in the southern Pacific region (region 9), was over 80% less likely to be a primary user of bottled water. Similarly, for a respondent in the mountain region (region 8), the odds of the person using bottled water as primary drinking water source were reduced by 53% compared with a resident in the southern Pacific region (region 9). Similar to the southern Pacific region (region 9), the southern region (region 6) and the southeast region (region 4) also have more residents primarily depending on bottled water for drinking (see appendix for detailed regional bottled water use comparison).

With logistic regression models, there is no equivalent r-squared statistics to show the explained variability in the dependent variable. However, the pseudo R 2 shows that the explanatory variables have moderate strength of associations with consumption of bottled water. The model non-significant chi-square test and likelihood ratio test statistics (1.0), which suggests good model fit [ 23 ].

Overall, this model shows that U.S. consumer perceptions about groundwater quality have strong associations on the purchase of bottled water for drinking. This suggests that bottled water use may be considered a substitute for other water sources when groundwater quality is perceived to be poor.

3.3. Logistic Regression Model 2: Regular Bottled Water Users

A second logistic regression model was used to predict regular users of bottled water ( Table 3 ).

Logistic Regression for regular bottled water users (N = 2,850).

P < 0.05;

P < 0.001.

These results show similar patterns as with primary bottled water users found in Table 2 . Groundwater quality perception, safe drinking water perception, age, gender, and region of residence were found to be significant predictors. Community size, however, unlike in the first regression model, was not significant. The likelihood of private water supply users being regular bottled water users was about 25% less than that of small water supply system users. There were no significant differences in bottled water use between municipal water supply users and small water supply system users.

The pseudo r-squared statistics are relatively small compared with our first model, which suggests that the same independent variables do not have particularly strong correlations with or explaining power for regular bottled water usage, although the chi-square test statistic is still non-significant.

3.4. Discussion

With findings of both logistic models, we confirmed the hypothesized negative association between perception of ground water quality and bottled water use. Given that an estimate of 49% of the U.S. population depends on groundwater for its drinking water supply from either a public source or private well [ 24 ], the groundwater quality perception seems to explain the consumers’ behavior regarding bottled water. Perception of drinking water safety is found to be highly associated with bottled water use. The findings about water quality perceptions generally confirmed that when public doubts about the safety of their tap water, they look for alternatives like bottled water [ 6 , 14 ]. No significant relationship, however, was found between surface water quality perception and bottled water use.

Our data do not include actual water quality or safety conditions so it is not known whether consumer’s perceptions of the condition of their local drinking water are accurate reflections of the real water quality or not. If perceptions are accurate, then community leadership along with regulatory agencies needs to act to correct the problems for public health to be maintained. However, one might ask why consumers have turned to bottled water purchases rather than voice their concern and pressure public water departments and elected officials for solutions. This is particularly relevant since it is public municipal and rural water system supply users rather than private water supply users that are likely to purchase bottled water. Public water systems are tax supported, regulated and maintained under much more rigorous monitoring and testing conditions than bottled water manufacturers. This suggests that if a large number of consumers purchase drinking water as a substitute for public tap water, they can undermine the water infrastructure investments needed to assure safe public water supplies. This has implications for community capacities to provide low cost, accessible, and safe drinking water for their entire population. Without safe public water supplies, limited income households’ health and well-being are at risk.

Our findings show that although municipal water supply users and small water supply users were equally likely to be regular bottled water users when every other condition is held the same, private water supply users (private well or surface water sources) were less likely to use bottled water than small water supply users. Consumers on private wells are often targets of public health campaigns reminding them to have their water tested regularly. To the extent this happens, private water supply users may believe they have more knowledge of and control over the quality of their water supply and thus trust it. Also, media coverage and increased headlines concerning problems with public water systems around the world can lead to high distrust (appropriately) of local water supplies [ 14 ]. The poor water conditions also increase the cost of treating water in public systems so that it is safe for consumption. This can lead to changes in water taste despite being safe to drink after treatments. While substituting bottled water for public tap water under these circumstances may be a short term “fix”, it does not address long term problems of water quality or the effect it has on escalating the cost of public water as increased treatments become necessary.

Residents of larger communities were found to be more likely to be primary bottled water users, which means that a higher proportion of population in larger communities tend to depend on bottled water rather than their tap water for drinking purpose. Note that this association is established when other conditions are controlled for. That is, for two persons in the same region, with the same perceptions towards their drinking water, surface and ground water quality, and having exactly the same demographic characteristics (age, gender, education), the person from larger community is more likely to depend on bottled water for drinking purpose. As some researchers have suggested, factors like media hype about water supply problems, commercial campaigns on bottled water, or even peer pressure for more fashionable ways of drinking all contribute to bottled water consumption [ 6 , 14 ]. And considering that these factors are usually stronger in larger cities, it is likely that people in larger cities have more negative feelings about their water supply systems and turn to bottled water for solution. However, if respondents were already using some sort of water supply for drinking purpose, then there is no significant association found between their community size and whether or not they regularly consume bottled water. With limited information in our data we were not able to fully explain the associations found between community size and bottled water consumption, and we suggest future research look at community level variables for possible answers.

Our data also show that younger people and females are more likely to purchase bottled water. Young people are generally believed to be more susceptible to marketing and advertising, which are essential keys held by the bottled water companies [ 6 , 14 ]. And the higher likelihood of female drinking bottled water is consistent with previous literature on gender differences in risk, especially health and food related risk perceptions [ 25 , 26 ]. The findings about more consumption in these two groups of people suggests a need to target these audiences with messages about the importance of learning about their local water quality as well as the costs and quality differences between bottled water and public drinking water supplies.

Our hypothesis about environmental attitudes was not supported by the data. The relationship between environmental attitudes and bottled water use was not significant. Consumers with stronger overall concern about the environment do not seem to transfer this concern to pollution and waste problems associated with purchasing bottled drinking water. But again, because of the relatively longer cycle of research using multistate data (data collection in some states were done back in 2004), our data might not be able to reflect the newest trend of national environmental concern on bottled water.

Finally, the hypothesized regional effect regarding bottled water use was confirmed by the data. Residents of the Midwest and west mountain regions were far less likely to use bottled water for either primary drinking purpose or other occasions of regular uses, while residents of the southern pacific, the south, and the southeast were all equally likely to be bottled water users. This suggests that other variables such as culture, actual water quality conditions, media coverage of water issues and other place specific factors may be influencing the decision to use bottled water versus tap water from a private or public system. Water resource quantity and income might also be driving forces for the differences. Further research is needed to better explain regional variations.

4. Conclusions

Water is essential to human health and life. Access to safe water supplies and affordability are central concerns of public health and individual consumers. In this study we find that perceptions of ground water quality and local water supply safety are associated with decisions to purchase bottled water versus use public water systems for drinking water. When local water is not considered safe or of high quality U.S. consumers are more likely to use bottled water as a primary water source. Furthermore, negative perceptions of safety increase the likelihood of a consumer frequently purchasing bottled water regardless of whether their primary source of drinking water is a small water system or large municipal water supply system.

Two key implications of our findings are that (1) public health officials and community leaders need to work to assure that public municipal drinking water supplies are safe; in addition, they should find effective ways to communicate to local residents the safety of their water supply; and (2) environmental leaders and activists need to campaign about the long lasting impacts of plastic water bottles. Further the public must be engaged in understanding the relationship of water quality to the capacity of local water systems to maintain safety and good taste standards. Consumer distrust of their groundwater quality should be leveraged to create community action to address legitimate concerns.

Acknowledgments

This research was partially funded by the National Institute of Food and Agriculture (NIFA), U.S. Department of Agriculture (USDA) under agreement 2008-51130-19526 also known as the Heartland Regional Water Coordination Initiative, the Iowa Agriculture and Home Economics Experiment Station, and USDA agreement 2008-51130-0474, also known as the Pacific Northwest Regional Water Resources Coordination Project.

A separate analysis, a one-way ANOVA (analysis of variance) was done to compare regional differences in bottled water use for primary drinking purposes. Table 1 shows bottled water use in each region and differences with statistical significance. The variable (primarily purchase bottled water for drinking) is a dichotomous variable with two possible responses 0 (not purchase) and 1 (purchase bottled water for drinking). Therefore the following means reflect proportion of respondents responding with 1 in each region. Post-hoc Bonferroni pair tests were conducted on the means and the last column of the following table shows regions with significant differences (at 0.05 level). For example, the first row shows that region 4 has mean which is significantly different from that of region 6, 7, 8, and 9, respectively.

Bottled water use by region.

Region 9 and region 6 have significantly higher percent of primary bottled water users, followed by region 4. Region 7 and region 8 have the least primary bottled water users.

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• Published: 03 August 2021

Future global urban water scarcity and potential solutions

• Chunyang He   ORCID: orcid.org/0000-0002-8440-5536 1 , 2 ,
• Zhifeng Liu   ORCID: orcid.org/0000-0002-4087-0743 1 , 2 ,
• Jianguo Wu   ORCID: orcid.org/0000-0002-1182-3024 1 , 2 , 3 ,
• Xinhao Pan 1 , 2 ,
• Zihang Fang 1 , 2 ,
• Jingwei Li 4 &
• Brett A. Bryan   ORCID: orcid.org/0000-0003-4834-5641 5  

Nature Communications volume  12 , Article number:  4667 ( 2021 ) Cite this article

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• Environmental sciences
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Urbanization and climate change are together exacerbating water scarcity—where water demand exceeds availability—for the world’s cities. We quantify global urban water scarcity in 2016 and 2050 under four socioeconomic and climate change scenarios, and explored potential solutions. Here we show the global urban population facing water scarcity is projected to increase from 933 million (one third of global urban population) in 2016 to 1.693–2.373 billion people (one third to nearly half of global urban population) in 2050, with India projected to be most severely affected in terms of growth in water-scarce urban population (increase of 153–422 million people). The number of large cities exposed to water scarcity is projected to increase from 193 to 193–284, including 10–20 megacities. More than two thirds of water-scarce cities can relieve water scarcity by infrastructure investment, but the potentially significant environmental trade-offs associated with large-scale water scarcity solutions must be guarded against.

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Introduction.

The world is rapidly urbanizing. From 1950 to 2020, the global population living in cities increased from 0.8 billion (29.6%) to 4.4 billion (56.2%) and is projected to reach 6.7 billion (68.4%) by 2050 1 . Water scarcity—where demand exceeds availability—is a key determinant of water security and directly affects the health and wellbeing of urban residents, urban environmental quality, and socioeconomic development 2 , 3 , 4 , 5 , 6 . At present, many of the world’s urban populations face water scarcity 3 . Population growth, urbanization, and socioeconomic development are expected to increase urban industrial and domestic water demand by 50–80% over the next three decades 4 , 7 . In parallel, climate change will affect the spatial distribution and timing of water availability 8 , 9 . As a result, urban water scarcity is likely to become much more serious in the future 10 , 11 , 12 , potentially compromising the achievement of the United Nations Sustainable Development Goals (SDGs) especially SDG11 Sustainable Cities and Communities and SDG6 Clean Water and Sanitation 13 , 14 .

Urban water scarcity has typically been addressed via engineering and infrastructure. Reservoirs are commonly used to store water during periods of excess availability and continuously supply water to cities to avoid water shortages during dry periods 15 . Desalination plants are increasingly used to solve water deficit problems for coastal cities 16 . For cities where local water resources cannot meet demand, inter-basin water transfer can also be an effective solution 17 (Supplementary Table  8 ). However, investment in water infrastructure is costly; requires substantial human, energy, and material resources; is limited by natural conditions such as geographic location and topography; and may have very significant environmental impacts 2 , 3 , 18 . Hence, a comprehensive understanding of water scarcity and the potential solutions for the world’s cities is urgently required to promote more sustainable and livable urban futures 7 , 18 , 19 .

Previous studies have evaluated urban water scarcity 2 , 3 , 7 , 19 (Supplementary Table  3 ). However, these studies have been limited in a number of ways including: assessing only a subset of the urban population (e.g., large cities only or regional in focus); considering only part of the water scarcity problem (i.e., availability but not withdrawal); or lacking a future perspective. For example, in assessing global urban water scarcity, Flörke et al. 7 considered 482 cities (accounting for just 26% of the global urban population) under a business-as-usual scenario, and while McDonald et al. 2 assessed a larger range of cities and scenarios, they considered water availability only, not withdrawals. As a result, significant uncertainty in estimates of current and future extent of urban water-scarcity remain, varying from 0.2 to 1 billion people affected in 2000 and from 0.5 to 4 billion in 2050 (Supplementary Table  4 ). A comprehensive assessment of global urban water scarcity is needed to identify cities at risk and provide better estimates of the number of people affected.

In addition, although many studies have discussed potential solutions to urban water scarcity, few have investigated the feasibility of these solutions for water-scarce cities at the global scale. Proposed solutions include groundwater exploitation, seawater desalination, increased water storage in reservoirs, inter-basin water transfer, improved water-use efficiency, and urban landscape management 2 , 3 , 14 , 19 . However, the potential effectiveness of these solutions for the world’s water-scarce cities depends on many factors including the severity of water scarcity, urban and regional geography and hydrogeology, socio-economic characteristics, and environmental carrying capacity 7 , 20 . Pairing the identification of water scarce cities with an evaluation of potential solutions is essential for guiding investment in future urban water security.

In this study, we comprehensively assessed global urban water scarcity in 2016 and 2050 and the feasibility of potential solutions for water-scarce cities. We first quantified the spatial patterns of the global urban population for 2016 at a grid-cell resolution of 1 km 2 by integrating spatial urban land-use and population data. We then identified water-scarce areas at the catchment scale by combining global water resource availability and demand data, and calculated the global urban population in water-scarce areas in 2016. We also quantified the global urban population in water-scarce areas for 2050 under four socioeconomic and climate change scenarios by combining modeled projections of global urban area, population, and water availability and demand. Finally, we evaluated the feasibility of seven major solutions for easing water scarcity for each affected city. We discuss the implications of the results for mitigating global urban water scarcity and improving the sustainability and livability of the world’s cities.

Current urban water scarcity

Globally, 933 million (32.5%) urban residents lived in water-scarce regions in 2016 (Table  1 , Fig.  1b ) with 359 million (12.5%) and 573 million (20.0%) experiencing perennial and seasonal water scarcity, respectively. India (222 million) and China (159 million) had the highest urban populations facing water scarcity (Table  1 , Fig.  1c ).

a spatial patterns of large cities in water-scarce areas (cities with population above 10 million in 2016 were labeled). b Water-scarce urban population at the global scale. c Water-scarce urban population at the national scale (10 countries with the largest values were listed). Please refer to Supplementary Data for urban water scarcity in each catchment.

Of the world’s 526 large cities (i.e., population >1 million), 193 (36.7%) were located in water-scarce regions (96 perennial, 97 seasonal) (Fig.  1a ). Of the 30 megacities (i.e., population >10 million), 9 (30.0%) were located in water-scarce regions (Table  2 ). Six of these, including Los Angeles, Moscow, Lahore, Delhi, Bangalore, and Beijing, were located in regions with perennial water scarcity and three (Mexico City, Istanbul, and Karachi) were seasonally water-scarce (Fig.  1a ).

Urban water scarcity in 2050

At the global scale, the urban population facing water scarcity was projected to increase rapidly, reaching 2.065 (1.693–2.373) billion people by 2050, a 121.3% (81.5–154.4%) increase from 2016 (Table  1 , Fig.  2a ). 840 (476–905) million people were projected to face perennial water scarcity and 1.225 (0.902–1.647) billion were projected to face seasonal water scarcity (Table  1 ). India’s urban population growth in water-scarce regions was projected to be much higher than other countries (Fig.  2b ), increasing from 222 million people to 550 (376–644) million people in 2050 and accounting for 26.7% (19.2%–31.2%) of the world’s urban population facing water scarcity (Table  1 ).

a Changes in water-scarce urban population at the global scale. Bars present the simulated results using the ensemble mean of runoff from GCMs, the total values (i.e., perennial and seasonal), and percentages are labeled. Crosses (gray/black) present the simulated results (total/perennial) using runoff from each GCM. b Changes in water-scarce urban population at the national scale (10 countries with the largest values were listed). Bars present the total values simulated using the ensemble mean of runoff from GCMs. Crosses present the total values simulated using runoff from each GCM. Please refer to Supplementary Data for urban water scarcity in each catchment.

Nearly half of the world’s large cities were projected to be located in water-scarce regions by 2050 (Fig.  3 , Supplementary Fig.  3 ). The number of large cities facing water scarcity under at least one scenario was projected to increase to 292 (55.5%) by 2050. The number of megacities facing water scarcity under at least one scenario was projected to increase to 19 (63.3%) including 10 new megacities (i.e., Cairo, Dhaka, Jakarta, Lima, Manila, Mumbai, New York, Sao Paulo, Shanghai, and Tianjin) (Table  2 ).

Only the water-scarce cities are listed. Cities with a population >10 million in 2016 are labeled.

Factors influencing urban water scarcity

Growth in urban population and water demand will be the main factor contributing to the increase in urban water scarcity (Fig.  4 ). From 2016 to 2050, population growth, urbanization, and socioeconomic development were projected to increase water demand and contribute to an additional 0.990 (0.829–1.135) billion people facing urban water scarcity, accounting for 87.5% (80.4–91.4%) of the total increase. Climate change was projected to alter water availability and increase the urban population subject to water scarcity by 52 (−72–229) million, accounting for 4.6% (−9.0–18.4%) of the total increase.

Bars present the simulated results using the ensemble mean of runoff from GCMs, crosses present the simulated results using runoff from each GCM.

Potential solutions to urban water scarcity

Water scarcity could be relieved for 276 (94.5%) large cities, including 17 (89.5%) megacities, via the measures assessed (Table  3 , Supplementary Table  5 ). Among these, 260 (89.0%) cities have the option of implementing two or more measures. For example, Los Angeles can adopt desalination, groundwater exploitation, inter-basin water transfer, and/or virtual water trade (Table  3 ). However, 16 large cities, including two megacities (i.e., Delhi and Lahore) in India and Pakistan, are restricted by geography and economic development levels, making it difficult to adopt any of the potential water scarcity solutions (Table  3 ).

Domestic virtual water trade was the most effective solution, which could alleviate water scarcity for 208 (71.2%) large cities (including 14 (73.7%) megacities). Inter-basin water transfer could be effective for 200 (68.5%) large cities (including 14 (73.7%) megacities). Groundwater exploitation could be effective for 192 (65.8%) large cities (including 11 (57.9%) megacities). International water transfer and virtual water trade showed potential for 190 (65.1%) large cities (including 10 (52.6%) megacities). Reservoir construction could relieve water scarcity for 151 (51.7%) large cities (including 10 (52.6%) megacities). Seawater desalination has the potential to relieve water scarcity for 146 (50.0%) large cities (including 12 (63.2%) megacities). In addition, water scarcity for 68 (23.3%) large cities, including five megacities (i.e., New York, Sao Paulo, Mumbai, Dhaka, and Jakarta), could be solved via the water-use efficiency improvements, slowed population growth rate, and climate change mitigation measures considered under SSP1&RCP2.6.

We have provided a comprehensive evaluation of current and future global urban water scarcity and the feasibility of potential solutions for water-scarce cities. We found that the global urban population facing water scarcity was projected to double from 933 million (33%) in 2016 to 1.693–2.373 billion (35–51%) in 2050, and the number of large cities facing water scarcity under at least one scenario was projected to increase from 193 (37%) to 292 (56%). Among these cities, 276 large cities (95%) can address water scarcity through improving water-use efficiency, limiting population growth, and mitigating climate change under SSP1&RCP2.6; or via seawater desalination, groundwater exploitation, reservoir construction, interbasin water transfer, or virtual water trade. However, no solutions were available to relieve water scarcity for 16 large cities (5%), including two megacities (i.e., Delhi and Lahore) in India and Pakistan.

Previous studies have estimated the global urban population facing water scarcity to be between 150 and 810 million people in 2000, between 320 and 650 million people in 2010, and increasing to 0.479–1.445 billion people by 2050 (Supplementary Table  4 ). Our estimates of 933 million people in 2016 facing urban water scarcity, increasing to 1.693–2.373 billion people by 2050, are substantially higher than previously reported (Supplementary Fig.  5a ). This difference is attributed to the fact that we evaluated the exposure of all urban dwellers rather than just those living in large cities (Supplementary Table  3 ). According to United Nations census data, 42% of the world’s urban population lives in small cities with a total population of <300,000 (Supplementary Fig.  4 ). Therefore, it is difficult to fully understand the global urban water scarcity only by evaluating the exposure of large cities. This study makes up for this deficiency and provides a comprehensive assessment of global urban water scarcity.

In addition, we used spatially corrected urban population data, newly released water demand/availability data, simulated runoff from GCMs in the most recent CMIP6 database, catchment-based estimation approach covering the upstream impacts on downstream water availability, and the new scenario framework combining socioeconomic development and climate change. Such data and methods can reduce the uncertainty in the spatial distribution of urban population and water demand/availability in the future, providing a more reliable assessment of global urban water scarcity.

Our projections suggest that global urban water scarcity will continue to intensify from 2016 to 2050 under all scenarios. By 2050, near half of the global urban population was projected to live in water-scarce regions (Figs.  2 ,  3 ). This will directly threaten the realization of SDG11 Sustainable Cities and Communities and SDG6 Clean Water and Sanitation . Although 95% of water-scarce cities can address the water crisis via improvement of water-use efficiency, seawater desalination, groundwater exploitation, reservoir construction, interbasin water transfer, or virtual water trade (Supplementary Table  5 ), these measures will not only have transformative impacts on society and the economy, but will also profoundly affect the natural environment. For example, the construction of reservoirs and inter-basin water transfer may cause irreversible damage to river ecosystems and hydrogeology and change the regional climate 4 , 15 , 17 , 21 , 22 . Desalination can have serious impacts on coastal zones and marine ecosystems 16 , 23 . Virtual water trade will affect regional economies, increase transport sector greenhouse gas emissions, and may exacerbate social inequality and affect the local environments where goods are produced 19 , 24 .

Water scarcity solutions may not be available to all cities. The improvement of water-use efficiency as well as other measures require the large-scale construction of water infrastructure, rapid development of new technologies, and large economic investment, which are difficult to achieve in low- and middle-income countries by 2050 14 . In addition, there will be 16 large cities, such as Delhi and Lahore, that cannot effectively solve the water scarcity problem via these measures (Supplementary Table  5 ). These cities also face several socioeconomic and environmental issues such as poverty, rapid population growth, and overextraction and pollution of groundwater 25 , 26 , which will further affect the achievement of SDG1 No Poverty , SDG3 Good Health and Well-being , SDG10 Reduced Inequalities , SDG14 Life below Water and SDG15 Life on Land .

To address global urban water scarcity and realize the SDGs, four directions are suggested. We need to:

Promote water conservation and reduce water demand. Our assessment provides evidence that the proposed water conservation efforts under SSP1&RCP2.6 are effective, which results in the least water-scarce urban population (34–241 million fewer compared to other SSPs&RCPs) at the global scale and can mitigate water scarcity for 68 (23.3%) large cities. The application of emerging water-saving technologies and the construction of sponge cities, smart cities, low-carbon cities, and resilient cities as well as the development of new theories and methods such as landscape sustainability science, watershed science, and geodesign will also play an important role for the further water demand reduction 5 , 6 , 27 , 28 , 29 . To implement these measures, the cooperation and efforts of scientists, policy makers and the public, as well as sufficient financial and material support are required. In addition, international cooperation must be strengthened in order to promote the development and dissemination of new technologies, assist in the construction of water infrastructure, and raise public awareness of water-savings, particularly in the Global South 30 .

Control population growth and urbanization in water-scarce regions by implementing relevant policies and regional planning. Urban population growth increases both water stress and the exposure of people, making it a key driver exacerbating global urban water scarcity 2 . Hence, the limitation of urban population growth in water-scarce areas can help to address this issue. According to our estimation, the control of urbanization under SSP3&RCP7.0, which has the lowest urbanization rate among four scenarios, can reduce the urban population subject to water scarcity by 93–207 million people compared with the business-as-usual scenario (SSP2&RCP4.5) and the rapid urbanization scenario (SSP5&RCP8.5), including 80–178 million people in India alone by 2050 (Fig.  2 ). To realize this pathway, policies that encourage family planning as well as tax incentives and regional planning for promoting population migration from water-scarce areas to other areas are needed 18 . In particular, for cities such as Delhi and Lahore that are both restricted by geography and socioeconomic disadvantage and have few options for dealing with water scarcity, there is an urgent need to control urban population growth and urbanization rates.

Mitigate climate change through energy efficiency and emissions abatement measures to avoid water resource impacts caused by the change in precipitation and the increase in evapotranspiration due to increased temperature. Our contribution analysis shows that the impacts of climate change on urban water scarcity is quite uncertain (ranging from a reduction of 72 million water-scarce urban people to an increase of 229 million) under different scenarios and GCMs (Fig.  4 ). On average, climate change under the business-as-usual scenario (SSP2&RCP4.5) will increase the global water-scarce urban population by 31 million in 2050. If the emissions reduction measures under SSP1&RCP2.6 are adopted, the increase in global water-scarce urban population due to climate change will be cut by half (16 million) in 2050. Thus, mitigating climate change is also important to reducing urban water scarcity. Considering that climate change in water-scarce areas would be affected by both internal and external impacts, mitigating climate change requires a global effort 31 .

Undertake integrated local sustainability assessment of water scarcity solutions. Our assessment reveals that 208 (71.2%) large cities may address water scarcity through seawater desalination, groundwater exploitation, reservoir construction, interbasin water transfer, and/or virtual water trade (Supplementary Table  5 ). While our results provide a guide at the global scale, city-level decisions about which measures to adopt to alleviate water scarcity involve very significant investments and should be supported by detailed local assessments of their relative effectiveness weighed against the potentially significant financial, environmental, and socio-economic costs. Integrated analyses are needed to quantify the effects of potential solutions on reducing water scarcity, their financial and resource requirements, and their potential impacts on socio-economic development for water-scarce cities and the sustainability of regional environments. To guard against the potential negative impacts of these measures, comprehensive impact assessments are required before implementing them, stringent regulatory oversight and continuous environmental monitoring are needed during and after their implementation, and policies and regulations should be established to achieve the sustainable supply and equitable distribution of water resources 24 , 32 .

Uncertainty is prevalent in our results due to limitations in the methodology and data used. First, constrained by data availability, in the evaluation of urban water scarcity in 2016 we used water demand/availability data for 2014 derived from the simulation results of the PCRGLOBWB 2 model, and only considered the inter-basin water transfers listed in City Water Map and the renewable groundwater simulated from the PCRGLOBWB 2 model instead of all available groundwater 3 , 33 . In the assessment of urban water scarcity and feasibility of potential solutions in 2050, we used water demand data derived from Hanasaki et al. 34 , in which irrigated area expansion, crop intensity change, and improvement in irrigation water efficiency were considered, but the change in irrigation to adapt to climate change as well as the impacts of energy systems (e.g., bio-energy production, mining, and fossil fuel extraction) on water demand were not fully considered 35 . Second, in order to maintain consistency and comparability of the water stress index (WSI) with the PCRGLOBWB 2 outputs 33 , environmental flow requirements were not considered. Following Mekonnen and Hoekstra 36 and Veldkamp et al. 37 (2017), we used an extreme threshold for WSI of 1.0 (where the entire water available is withdrawn for human use). If a more conservative threshold (e.g., WSI = 0.4 which is the threshold defining high water stress) was used, estimated global water scarcity and the urban population exposed to water stress would be much higher 7 .

In summary, global urban water scarcity is projected to intensify greatly from 2016 to 2050. By 2050, nearly half of the global urban population (1.693–2.373 billion) were projected to live in water-scarce regions, with about one quarter concentrated in India, and 19 (63%) global megacities are expected to face water scarcity. Increases in urban population and water demand drove this increase, while changes in water availability due to climate change compounded the problem. About 95% of all water-scarce cities could find at least one potential solution, but substantial investment is needed and solutions may have significant environmental and socioeconomic consequences. The aggravation of global urban water scarcity and the consequences of potential solutions will challenge the achievement of several SDGs. Therefore, there is an urgent need to further improve water-use efficiency, control urbanization in water-scarce areas, mitigate water availability decline due to climate change, and undertake integrated sustainability analyses of potential solutions to address urban water scarcity and promote sustainable development.

Description of scenarios used in this study

To assess future urban water scarcity, we used the scenario framework from the Scenario Model Intercomparison Project (ScenarioMIP), part of the International Coupled Model Intercomparison Project Phase 6 (CMIP6) 38 . The scenarios have been developed to better link the Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs) to support comprehensive research in different fields to better understand global climatic and socioeconomic interactions 38 , 39 . We selected the four ScenarioMIP Tier 1 scenarios (i.e., SSP1&RCP2.6, SSP2&RCP4.5, SSP3&RCP7.0, and SSP5&RCP8.5) to evaluate future urban water scarcity. SSP1&RCP2.6 represents the sustainable development pathway of low radiative forcing level, low climate change mitigation challenges, and low social vulnerability. SSP2&RCP4.5 represents the business-as-usual pathway of moderate radiative forcing and social vulnerability. SSP3&RCP7.0 represents a higher level of radiative forcing and high social vulnerability. SSP5&RCP8.5 represents a rapid development pathway and very high radiative forcing 38 .

Estimation of urban water scarcity

To estimate urban water scarcity, we quantified the total urban population living in water-scarce areas 2 , 3 , 7 , 19 . Specifically, we first corrected the spatial distribution of the global urban population, then identified water-scarce areas around the world, and finally quantified the urban population in water-scarce areas at different scales (Supplementary Fig.  1 ).

Correcting the spatial distribution of global urban population

The existing global urban population data from the History Database of the Global Environment (HYDE) provided consistent information on historical and future population, but it has a coarse spatial resolution of 10 km (Supplementary Table  1 ) 40 , 41 . In addition, it was estimated using total population, urbanization levels, and urban population density, and does not align well with the actual distribution of urban land 42 . Hence, we allocated the HYDE global urban population data to high-resolution urban land data. We first obtained global urban land in 2016 from He et al. 42 . Since the scenarios used in existing urban land forecasts are now dated 43 , 44 , we simulated the spatial distribution of global urban land in 2050 under each SSP at a grid-cell resolution of 1km 2 using the zoned Land Use Scenario Dynamics-urban (LUSD-urban) model 45 , 46 , 47 (Supplementary Methods 1). The simulated urban expansion area in this study was significantly correlated with that in existing datasets (Supplementary Table  6 ). We then converted the global urban land raster layers for 2016 and 2050 into vector format to characterize the spatial extent of each city. The total population within each city was then summed and the remaining HYDE urban population cells located outside urban areas were allocated to the nearest city. Assuming that the population density within an urban area was homogeneous, we calculated the total population per square kilometer for all urban areas and converted this back to raster format at a spatial resolution of 1 km 2 . The new urban population data had much lower error than the original HYDE data (Supplementary Table  7 ).

Identification of global water-scarce areas

Annual and monthly WSI values were calculated at the catchment level in 2014 and 2050 as the ratio of water withdrawals (TWW) to availability (AWR) 33 . Due to limited data availability, we combined water-scarce areas in 2014 and the urban population in 2016 to estimate current urban water scarcity. WSI for catchment i for time t as:

For each catchment defined by Masutomi et al. 48 , the total water withdrawal (TWW t,i ) equalled the sum of water withdrawals (WW t , n , i ) for each sector n (irrigation, livestock, industrial, or domestic), while the water availability equalled the sum of available water resources for catchment i ( R t , i ), inflows/outflows of water resources due to interbasin water transfer ( \(\varDelta {{{{\mathrm{W{R}}}}}}_{t,i}\) ), and water resources from each upstream catchment j (WR t , i , j ):

The changes of water resources due to interbasin water transfer were calculated based on City Water Map produced by McDonald et al. 3 . The number of water resources from upstream catchment j was calculated based on its water availability (AWR t , i , j ) and water consumption for each sector n (WC t , n , i,j ) 49 :

For areas without upstream catchments, the number of available water resources was equal to the runoff. Following Mekonnen and Hoekstra 36 , and Hofste et al. 33 , we did not consider environmental flow requirements in calculating water availability.

Annual and monthly WSI for 2014 were calculated directly based on water withdrawal, water consumption, and runoff data from AQUEDUCT3.0 (Supplementary Table  1 ). The data from AQUEDUCT3.0 were selected because they are publicly available and the PCRaster Global Water Balance (PCRGLOBWB 2) model used in the AQUADUCT 3.0 can better represent groundwater flow and available water resources in comparison with other global hydrologic models (e.g., the Water Global Assessment and Prognosis (WaterGAP) model) 33 . The annual and monthly WSI for 2050 were calculated by combining the global water withdrawal data from 2000 to 2050 provided by the National Institute of Environmental Research of Japan (NIER) 34 and global runoff data from 2005 to 2050 from CMIP6 (Supplementary Table  1 ). Water withdrawal \({{{{{\mathrm{W{W}}}}}}}_{s,m,n,i}^{2050}\) in 2050 for each sector n (irrigation, industrial, or domestic), catchment i , and month m under scenario s was calculated based on water withdrawal in 2014 ( \({{{{{\mathrm{W{W}}}}}}}_{m,n,i}^{2014}\) ):

adjusted by the mean annual change in water withdrawal from 2000 to 2050 (WWR s , m , n , i ), calculated using the global water withdrawal for 2000 ( \({{{{{\mathrm{W{W}}}}}}}_{{{{{\mathrm{NIER}}}}},m,n,i}^{2000}\) ) and 2050 ( \({{{{{\mathrm{W{W}}}}}}}_{{{{{\mathrm{NIER}}}}},s,m,n,i}^{2050}\) ) provided by the NIER 34 :

Based on the assumption of a constant ratio of water consumption to water withdrawal in each catchment, water consumption in 2050 ( \({{{{{\mathrm{W{C}}}}}}}_{s,m,n,i}^{2050}\) ) was calculated as:

where \({{{{{\mathrm{W{C}}}}}}}_{m,n,i}^{2014}\) denotes water consumption in 2014. Due to a lack of data, we specified that water withdrawal for livestock remained constant between 2014 and 2050, and used water withdrawal simulation under SSP3&RCP6.0 provided by the National Institute of Environmental Research in Japan to approximate SSP3&RCP7.0.

To estimate water availability, we calculated available water resources ( \({R}_{s,m,i}^{2041-2050}\) ) for each catchment i and month m under scenario s for the period of 2041–2050 as:

based on the amount of available water resources with 10-year ordinary least square regression from 2005 to 2014 ( \({R}_{m,i}^{{{{{\mathrm{ols}}}}},\,2005-2014}\) ) from AQUEDUCT3.0 (Supplementary Table  1 ). \({\overline{R}}_{m,i}^{2005-2014}\) and \({\overline{R}}_{s,m,i}^{2041-2050}\) denote the multi-year average of runoff (i.e., surface and subsurface) from 2005 to 2014, and from 2041 to 2050, respectively, calculated using the average values of simulation results from 10 global climate models (GCMs) (Supplementary Table  2 ).

We then identified water-scarce catchments based on the WSI. Two thresholds of 0.4 and 1.0 have been used to identify water-scarce areas from WSI (Supplementary Table  4 ). While the 0.4 threshold indicates high water stress 49 , the threshold of 1.0 has a clearer physical meaning, i.e., that water demand is equal to the available water supply and environmental flow requirements are not met 36 , 37 . We adopted the value of 1.0 as a threshold representing extreme water stress to identify water-scarce areas. The catchments with annual WSI >1.0 were identified as perennial water-scarce catchments; the catchments with annual WSI equal to or <1.0 and WSI for at least one month >1.0 were identified as seasonal water-scarce catchments.

Estimation of global urban water scarcity

Based on the corrected global urban population data and the identified water-scarce areas, we evaluated urban water scarcity at the global and national scales via a spatial overlay analysis. The urban population exposed to water scarcity in a region (e.g., the whole world or a single country) is equal to the sum of the urban population in perennial water-scarce areas and that in seasonal water-scarce areas. Limited by data availability, we used water-scarce areas in 2014 and the urban population in 2016 to estimate current urban water scarcity. Projected water-scarce areas and urban population in 2050 under four scenarios were then used to estimate future urban water scarcity. In addition, we obtained the location information of large cities (with population >1 million in 2016) from the United Nations’ World Urbanization Prospects 1 (Supplementary Table  1 ) and identified those in perennial and seasonal water-scarce areas.

Uncertainty analysis

To evaluate the uncertainty across the 10 GCMs used in this study (Supplementary Table  2 ), we identified water-scarce areas and estimated urban water scarcity using the simulated runoff from each GCM under four scenarios. To perform the uncertainty analysis, the runoff in 2050 for each GCM was calculated using the following equation:

where \({R}_{s,g,m,i}^{2050}\) denotes the runoff of catchment i in month m in 2050 for GCM g under scenario s . \({R}_{g,m,i}^{2005-2014}\) and \({R}_{s,g,m,i}^{2041-2050}\) denote the multi-year average runoff from 2005 to 2014, and from 2041 to 2050, respectively, calculated using the simulation results from GCM g . Using the runoff for each GCM, the WSI in 2050 for each catchment was recalculated, water-scarce areas were identified, and the urban population exposed to water scarcity was estimated.

Contribution analysis

Based on the approach used by McDonald et al. 2 and Munia et al. 50 , we quantified the contribution of socioeconomic factors (i.e., water demand and urban population) and climatic factors (i.e., water availability) to the changes in global urban water scarcity from 2016 to 2050. To assess the contribution of socioeconomic factors ( \({{{{{\mathrm{Co{n}}}}}}}_{s,{{{{\mathrm{SE}}}}}}\) ), we calculated global urban water scarcity in 2050 while varying demand and population and holding catchment runoff constant ( \({{{{{\mathrm{UW{S}}}}}}}_{s,{{{{\mathrm{SE}}}}}}^{2050}\) ). Conversely, to assess the contribution of climate change ( \(Co{n}_{s,CC}\) ), we calculated scarcity while varying runoff and holding urban population and water demand constant ( \({{{{{\mathrm{UW{S}}}}}}}_{s,{{{{\mathrm{CC}}}}}}^{2050}\) ). Socioeconomic and climatic contributions were then calculated as:

Feasibility analysis of potential solutions to urban water scarcity

Potential solutions to urban water scarcity involve two aspects: increasing water availability and reducing water demand 2 . Approaches to increasing water availability include groundwater exploitation, seawater desalination, reservoir construction, and inter-basin water transfer; while approaches to reduce water demand include water-use efficiency measures (e.g., new cultivars for improving agricultural water productivity, sprinkler or drip irrigation for improving water-use efficiency, water-recycling facilities for improving domestic and industrial water-use intensity), limiting population growth, and virtual water trade 2 , 3 , 18 , 32 . To find the best ways to address urban water scarcity, we assessed the feasibility of these potential solutions for each large city (Supplementary Fig.  2 ).

First, we divided these solutions into seven groups according to scenario settings and the scale of implementation of each solution (Supplementary Fig.  2 ). Among the solutions assessed, water-use efficiency improvement, limiting population growth, and climate change mitigation were included in the simulation of water demand and water availability under the ScenarioMIP SSPs&RCPs simulations 34 . Here, we considered the measures within SSP1&RCP2.6 which included the lowest growth in population, irrigated area, crop intensity, and greenhouse gas emissions; and the largest improvements in irrigation, industrial, and municipal water-use efficiency 34 .

We then evaluated the feasibility of the seven groups of solutions according to the characteristics of water-scarce cities (Supplementary Fig.  2 ). Of the 526 large cities (with population >1 million in 2016 according to the United Nations’ World Urbanization Prospects), we identified those facing perennial or seasonal water scarcity under at least one scenario by 2050. We then selected the cities that no longer faced water scarcity under SSP1&RCP2.6 where the internal scenario assumptions around water-use efficiency, population growth, and climate change were sufficient to mitigate water scarcity. Following McDonald et al. 2 , 3 and Wada et al. 18 , we assumed that desalination can be a potential solution for coastal cities (distance from coastline <100 km) and groundwater exploitation can be feasible for cities where the groundwater table has not significantly declined. For cities in catchments facing seasonal water scarcity and with suitable topography, reservoir construction was identified as a potential solution. Inter-basin water transfer was identified as a potential solution for a city if nearby basins (i.e., in the same country, <1000 km away [the distance of the longest water transfer project in the world]) were not subject to water scarcity and had sufficient water resources to address the water scarcity for the city. Domestic virtual water trade was identified as a potential solution for a city if it was located in a country without national scale water scarcity. International water transfer or virtual water trade was identified as a feasible solution for cities in middle and high-income countries. Based on the above assumptions, we identified potential solutions to water scarcity in each city (see Supplementary Table  1 for the data used).

Data availability

All the data created in this study are openly available and the download information of supplementary data can be found in Github repositories with the identifier https://github.com/zfliu-bnu/Urban-water-scarcity . Other data are available from the corresponding author upon reasonable request.

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Acknowledgements

We thank Prof. N. Hanasaki (National Institute for Environmental Studies, Tsukuba, Japan) and Dr. Rutger W. Hofste (World Resources Institute, Washington, DC, USA) for providing global water demand/availability data. This work was supported by the Second Tibetan Plateau Scientific Expedition and Research Program (Grant No. 2019QZKK0405) and the National Natural Science Foundation of China (Grant No. 41871185 & 41971270). It was also supported by the project from the State Key Laboratory of Earth Surface Processes and Resource Ecology, China.

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Chunyang He, Zhifeng Liu, Jianguo Wu, Xinhao Pan & Zihang Fang

School of Natural Resources, Faculty of Geographical Science, Beijing Normal University, Beijing, China

School of Life Sciences and School of Sustainability, Arizona State University, Tempe, AZ, USA

School of Environmental and Geographical Sciences (SEGS), Shanghai Normal University, Shanghai, China

Centre for Integrative Ecology, Deakin University, Melbourne, Australia

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C.H., Z.L., J.W., and B.B. designed the study and planned the analysis. Z.L., X.P., Z.F., and J.L. did the data analysis. C.H., Z.L., and B.B. drafted the manuscript. All authors contributed to the interpretation of findings, provided revisions to the manuscript, and approved the final manuscript.

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Correspondence to Zhifeng Liu .

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He, C., Liu, Z., Wu, J. et al. Future global urban water scarcity and potential solutions. Nat Commun 12 , 4667 (2021). https://doi.org/10.1038/s41467-021-25026-3

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Received : 30 October 2020

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DOI : https://doi.org/10.1038/s41467-021-25026-3

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