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Citation: Almulhim, A.I.; Aina, Y.A.
Understanding Household
Water-Use Behavior and
Consumption Patterns during
COVID-19 Lockdown in Saudi
Arabia. Water 2022,14, 314.
https://doi.org/10.3390/w14030314
Academic Editor: Maria Mimikou
Received: 16 December 2021
Accepted: 18 January 2022
Published: 20 January 2022
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water
Article
Understanding Household Water-Use Behavior and Consumption
Patterns during COVID-19 Lockdown in Saudi Arabia
Abdulaziz I. Almulhim 1, * and Yusuf A. Aina 2
1
Department of Urban and Regional Planning, College of Architecture and Planning, Imam Abdulrahman Bin
Faisal University, Dammam 31451, Saudi Arabia
2Department of Geomatics Engineering Technology, Yanbu Industrial College, Yanbu 41912, Saudi Arabia;
ainay@rcyci.edu.sa
*Correspondence: aialmulhim@iau.edu.sa
Abstract:
With the COVID-19 lockdown impacting the livelihood of people globally, changes in
household behaviors, water consumption patterns, etc., have implications on sanitation, hygiene,
and disease control. An online questionnaire survey was conducted, and officials were interviewed
to assess the impact of the lockdown on water consumption patterns in the Dammam Metropolitan
Area, Saudi Arabia. The multiple regression analysis on responses from the survey indicates that
water consumption increased by 50% in 86% of the respondents, leading to higher utility bills.
Socioeconomic factors also influenced water consumption. The officials interviewed emphasized
the need for integrating water policies with disaster management actions. This study contributes
to the prospering empirical literature on the pandemic COVID-19 and water consumption/usage
behavioral practices by exploring the behavior of household water during COVID-19 in Saudi Arabia.
This study can help decision-makers in Saudi Arabia and other developing countries in boosting
awareness related to water management in crisis time.
Keywords:
COVID-19; water management; water utilization; urban water; waterpolicy;
water security
1. Introduction
The coronavirus disease is caused by the novel pathogen SARS-CoV2. The outbreak
of the disease began in 2019 in Wuhan, China, and by the mid-summer of 2020, human
interactions and human contact soon led to its spread globally [
1
]. The World Health
Organization (WHO) declared the disease a pandemic after it had spread to 114 countries
across the world in under three months [
2
,
3
]. By the mid of January 2022, the virus had
infected over 300 million people worldwide [
4
]. The COVID-19 pandemic brought out the
importance of pure water and hygienic facilities to safeguard the health of humans. The
pandemic also drew attention to water insecurities in households around the world [5,6].
The COVID-19 pandemic led to substantial changes in professions and lifestyles across
the world as countries enforced minimum travel and socialization [
7
,
8
]. Most governments
initiated “stay-at-home” orders necessitating businesses not categorized as “important”
to limit socialization. Company staff started working from home instead of in offices.
Increased unemployment, changes in transportation patterns, and decreased economic
activities led to drastic socioeconomic variations [
9
]. These disturbances in patterns of
life and work had a crucial impact on where and how water was consumed during the
pandemic [10].
The global community was forced to improve behavioral practices [
11
,
12
], such as
washing hands with soap and water to enhance hygiene [
13
]. Sustainable Development
Goals (SDGs) 3 and 6 aim to ensure good health, pure water, and proper sanitation. Proper
handwashing is addressed in these goals to achieve good health [
14
]. In order to prevent the
spread of COVID-19, the WHO suggested frequent handwashing with soap and water [
15
].
However, effective handwashing requires easy access to water [
16
], which is challenging
Water 2022,14, 314. https://doi.org/10.3390/w14030314 https://www.mdpi.com/journal/water
Water 2022,14, 314 2 of 16
for people in developing countries living in rural districts and areas far from cities [
17
,
18
].
They face water scarcity due to high demand and limited supply [
19
]. Since water supply
is crucial, especially amid the pandemic, for people to maintain hygiene [
20
], high water
demand can lead to inadequate water distribution [
21
]. Therefore, it can be very challenging
to run a household without access to a clean and adequate water supply [22].
Access to hygiene infrastructure and clean water decreases the possibility of trans-
mission of COVID-19 by facilitating periodic washing of hands and by decreasing contact
with viruses [
23
,
24
]. However, about 75% of low- and middle-income households do not
have proper facilities for handwashing with soap and water [
25
], and over half the world’s
population lack adequate sanitation [
25
]. Studies have recently linked SARS-CoV-2 with
the dismal census on household water stress in low-income and middle-income nations,
especially in regions with a high population in urban slums [26–30].
However, household water insecurity is not confined to developing countries. Highly
developed countries such as the United States are also affected by household water in-
security [
6
,
31
,
32
]. The aggregate and general demand peaks of water usage were the
focus of research studies and investigations on the effect of COVID-19 on water usage.
For example, a notable change was observed in the total demand peak in Germany from
7:10 am
(before lockdown) to 9:40 am (over lockdown), changing to weekend patterns [
33
].
Lüdtke et al. [34]
reported about a 14% increase in daily water consumption in northern
Germany. According to Marshallsay [
35
], a 35% increase was observed in the peak daily
use in certain areas of the United Kingdom during the lockdown. Investigation data from
Brazil revealed that an 11% increase in domestic water usage was observed between
26 days
before the lockdown and 26 days during the lockdown [
20
]. Similarly, Kim et al. [
36
] high-
lighted a higher usage of domestic hot water during the COVID-19 lockdown. Although
this rise in water consumption is attributed to people staying at home, an increase in pre-
ventative measures such as handwashing also contributed to the rise of household water
consumption [
37
]. The increased time people spent at their homes and the consequent
changes in behaviors impacted the demand for water in households aggravating ongoing
pressure on the network supplying water.
Countries are now faced with the challenge of assuring adequate and clean water
supply in the ongoing COVID-19 pandemic [
24
]. Behavioral changes that can reduce the
impact of the pandemic provide can be a source of information to achieve sustainable water
supply and production [
38
,
39
]. Understanding how measures to control the spread of
COVID-19 can affect water usage provides well-grounded facts and data for legislatures to
frame policy decisions that can overcome the challenges of water supply in emergencies.
The first case of COVID-19 disease in the Kingdom of Saudi Arabia was reported
on
2 March 2020
, in the Qatif region, which is in the eastern part of Saudi Arabia. By
April 2021, 396,758 confirmed cases were identified, of which 6728 was the number of
deaths [
40
,
41
]. By the end of March 2020, a complete lockdown was imposed to prevent
the spread of the virus. Travel between the different regions of the country was restricted
to control the spread of the disease as the main mode of transmission was through droplets
from those infected by the virus. Besides the elementary measures of travel restriction by
the government of Saudi Arabia, including the ban on travel from one city to another, the
Ministry of Health also imposed strict laws to limit outdoor activities, suspending schools,
banning prayers in mosques, and minimizing social interactions. The same was imposed
in 140 countries across the world to avoid the spread of disease [42,43].
In developing countries such as Saudi Arabia, there is limited research on predictors
of variations in water usage under crises such as the COVID-19 pandemic. Countries in
the Middle East are already water-scarce regions (<500 m
3
/capita year1) [
44
], with Saudi
Arabia having a potable groundwater supply of ~30% and a desalinized water supply of
~70%. It is predicted that within 50 years, the groundwater source might be completely
depleted. Therefore, there is a great challenge of water provision in Saudi Arabia, and
water is rapidly becoming a scarce asset in the nation [45].
Water 2022,14, 314 3 of 16
This study addresses two main questions: How does water usage vary under the
conditions of the COVID-19 pandemic (before and after March 2020)? What characteristics
of household-related water usage have increased or decreased under the circumstances of
the COVID-19 pandemic (before and after March 2020)? Thus, the main objective of this
study is to evaluate how urban water consumption is affected by the spread-prevention
action for COVID-19. Understanding the socioeconomic and demographic predictors of
variation in household water usage can be used to inform effectual prediction and design
interventions for a fair supply of water during times of crisis, such as the COVID-19
pandemic. Investigating the elements that lead to changes in water use during emergency
circumstances is important to formulate and execute appropriate policies and interventions
for reducing inequality in water distribution during emergencies.
2. Materials and Methods
2.1. Study Area/Setting
Dammam Metropolitan Area (DMA), comprising three major cities, Dammam, Khobar,
and Dhahran, is considered a case study in this research (Figure 1). Dammam is the capital
of Saudi Arabia’s Eastern Province. It is around 400 km from Riyadh on the Arabian Gulf
coast. In 2014, DMA had a population of around 1.66 million, the third-highest in Saudi
Arabia [
46
]. As a global hub for petrochemical refining activities, DMA is a rapidly growing
economic center because it influences the global oil market [
47
]. From a small fishing town
in the 1950s, it grew to about 1050 km
2
by 2017 [
48
]. As one of the seven largest cities
of the country, it now accommodates around half the country’s population [
49
]. Water
shortages, high water consumption, and poor management are some of the problems in
Saudi cities, and the municipality is making all efforts to expand its industrial base for
economic growth [
50
]. Water usage is an important aspect of the city owing to the dry
and hot climate. MOMRA [
48
] highlighted the pressure of water scarcity in Dammam
despite sourcing water from desalination plants, which leads to an increase in greenhouse
gas emissions.
Water 2022, 14, x FOR PEER REVIEW 3 of 17
In developing countries such as Saudi Arabia, there is limited research on predictors
of variations in water usage under crises such as the COVID-19 pandemic. Countries in
the Middle East are already water-scarce regions (<500 m
3
/capita year1) [44], with Saudi
Arabia having a potable groundwater supply of ~30% and a desalinized water supply of
~70%. It is predicted that within 50 years, the groundwater source might be completely
depleted. Therefore, there is a great challenge of water provision in Saudi Arabia, and
water is rapidly becoming a scarce asset in the nation [45].
This study addresses two main questions: How does water usage vary under the
conditions of the COVID-19 pandemic (before and after March 2020)? What characteristics
of household-related water usage have increased or decreased under the circumstances of
the COVID-19 pandemic (before and after March 2020)? Thus, the main objective of this
study is to evaluate how urban water consumption is affected by the spread-prevention
action for COVID-19. Understanding the socioeconomic and demographic predictors of
variation in household water usage can be used to inform effectual prediction and design
interventions for a fair supply of water during times of crisis, such as the COVID-19 pan-
demic. Investigating the elements that lead to changes in water use during emergency
circumstances is important to formulate and execute appropriate policies and interven-
tions for reducing inequality in water distribution during emergencies.
2. Materials and Methods
2.1. Study Area/Setting
Dammam Metropolitan Area (DMA), comprising three major cities, Dammam, Kho-
bar, and Dhahran, is considered a case study in this research (Figure 1). Dammam is the
capital of Saudi Arabia’s Eastern Province. It is around 400 km from Riyadh on the Ara-
bian Gulf coast. In 2014, DMA had a population of around 1.66 million, the third-highest
in Saudi Arabia [46]. As a global hub for petrochemical refining activities, DMA is a rap-
idly growing economic center because it influences the global oil market [47]. From a small
fishing town in the 1950s, it grew to about 1050 km
2
by 2017 [48]. As one of the seven
largest cities of the country, it now accommodates around half the country’s population
[49]. Water shortages, high water consumption, and poor management are some of the
problems in Saudi cities, and the municipality is making all efforts to expand its industrial
base for economic growth [50]. Water usage is an important aspect of the city owing to the
dry and hot climate. MOMRA [48] highlighted the pressure of water scarcity in Dammam
despite sourcing water from desalination plants, which leads to an increase in greenhouse
gas emissions.
Figure 1. Location of Dammam metropolitan area, Saudi Arabia.
Water 2022,14, 314 4 of 16
2.2. Data Collection and Analysis
In this study, two approaches were adopted for data collection to understand the
changes in household behavior and water consumption. The first approach involved
the administration of questionnaires to residents in the three major cities of DMA. The
second approach entailed semi-structured interviews with the officials and policymakers
of the water department of DMA. The survey was conducted to investigate the effect
of COVID-19 on water usage in DMA households. The questionnaire consisted of four
sections: (i) demographic information (residential location, marital status, number of family
members, gender, age, nationality), (ii) socioeconomic information (education, employment,
household type, income, housing tenure), (iii) water consumption information (working
type during the pandemic, change in household water consumption, water bill, type of
increase), and (iv) water usage behaviors. Most of the questions in the questionnaire were
to be responded on a 5-point Likert scale (strongly agree, agree, neutral, disagree, and
strongly disagree), and the remaining included ranking responses, yes/no questions, or
neutral options. The questions were in English with Arabic translation included. The
questionnaire was pilot tested among the colleagues of the researchers before mailing them
to the participants. The feedback from the pilot survey was used to revise and improve
the questions.
A cross-sectional survey was applied using QuestionPro (www.questionpro.com,
accessed on 1 February 2021) between February and May 2021. This method was preferred
since online surveys are quicker, more economical, and anonymous [
51
]. Snowball sampling
method was used to distribute the survey link by email, where the participants shared the
link with others in the area through email and WhatsApp groups [
52
–
54
]. The number of
participants reached 810 at the end of three months. According to Cochran’s formula, this
number was sufficient for a city with 1.17 million residents and a 0.05 significance level [
55
].
Participation was voluntary, and the confidentiality of the participants was guaranteed.
The snowball sampling method allows studying a society anonymously when the response
rate is high [
54
]. The online survey helped reach a maximum number of participants since
distributing the survey to the public, especially female participants, would have been
difficult [52,53].
The second approach for collecting data was through interviews conducted with five
managers and government officials to investigate key issues in the water policies of DMA
during the COVID-19 pandemic. The number of interviewees was limited to five because
only senior officials who were knowledgeable about policies and water management issues
were chosen. The interviews were conducted via telephone, and the questions were framed
in the form of open-ended queries to allow for comprehensive responses. The interview
questions were designed to obtain information on the changes in water consumption, the
capacity of the water system to meet the increased demand during the lockdown, changes
in water policy during the lockdown, and suggestions for revising the water policy.
For data analysis, the statistical package SPSS (version 26) was used to describe the
basic features of the data in the study through frequencies and percentages. Pearson’s
Chi-squared test (
χ2
) was used to find the association between the level of increasing
water usage behavior and the demographic variables; T-independent sample test and
ANOVA test were used to find the mean differences in increasing water usage behavior
based on sociodemographic variables and household water consumption. Finally, multiple
regression tests were used to identify the predictors of increasing water usage behavior.
The independent variables of this study are the socioeconomic variables (education, work
organization, income, housing tenure, and housing type) and the dependent variable is
the (water usage behavior). The equation for the model of this study would be presented
as below:
Y=X1+X2+ X3−X4+ X5
where;
Y: Water usage behavior;
X1: Education;
Water 2022,14, 314 5 of 16
X2: Work organization;
X3: Income;
X4: Housing tenure;
X5: Housing type.
2.3. Bias and Limitation
While the survey procedure sought to ensure that the samples represent the population
by distributing the questionnaires among different groups in the society, the nature of the
survey (online) might introduce a bias towards the educated members of the populace.
An attempt was made to cross the 67.3% college degree attainment of the respondents
with the degree attainment in DMA. However, the latest data on college degree attainment
(
23.6% of the population
) were national data compiled in 2016 [
56
]. Efforts have been
made by the government since 2015 to improve the proportion of university graduates by
increasing foreign university scholarships and local universities. Moreover, recent similar
studies reported 72.3% [46] and 84.6% [57] college degree attainment.
One of the limitations of this study is that the problem was mainly investigated
through an online questionnaire survey. This was due to limited access to data on water
demand and supply. Most previous studies on the impact of the lockdown on water
consumption used data from the relevant water supply organization [
20
,
58
,
59
]. In order
to quantify the changes in water consumption, the respondents were asked to indicate
if there was an increase in their water bills during the lockdown. In Saudi Arabia, the
water supply is solely handled by the National Water Company, and the tariff has been
0.027 USD/m3
since 2015 [
60
]. Therefore, an increase in water bills might indicate an
increase in consumption. Moreover, similar studies successfully used online surveys to
understand the impact of COVID-19 lockdown on water and energy use [39,61].
3. Results
3.1. Descriptive Analysis of Demographic and Socioeconomic Features
Among the 810 respondents, 424 were male (52.3%), and 386 were female (47.7%). Fur-
ther, 582 (71.9%) were Saudi citizens, while 228 (28.1%) were expatriates. Most participants
(350; 43.2%) were from Dammam, while 285 (35.2%) were from Khobar and 175 (21.6%)
were from Dhahran. This is not surprising since Dammam has the highest population in
DMA, followed by Khobar, while Dhahran is the least populated region (MOMRA, 2019). A
majority of the respondents (567; 70%) were from households with 6 to 10 members. About
387 (47.8%) respondents were married, while 200 (24.7%) were single, and 111 (13.7%) were
divorced. Among the respondents, 688 (84.9%) were between the ages of 26 and 65. They
were part of the working-class group, implying that most of the population living in DMA
were potentially a part of the country’s workforce. Participants under the age of 25 years
were 72 (8.9%) in number, and those above 65 were 50 (6.2%).
Table 1presents the socioeconomic background of the participants, indicating respon-
dents’ education level, the type of organizations they worked for, the total family income,
and the kind of housing in which they resided. Family sizes ranged from moderate to
large. In general, the participants belonged to the working class, with middle to high
income, and homeownership. This population profile has the potential to increase their
water consumption during the lockdown since most of them would either not be able to go
to work or would work from home. The change from a daily water consumption profile
to a weekend water consumption profile due to the stay-at-home rule reflects the spike in
water consumption during the lockdown [33].
Water 2022,14, 314 6 of 16
Table 1. Socioeconomic features.
n%
Level of Education
Primary or lower 50 6.2
Intermediate 76 9.4
Secondary 139 17.2
College/university and above 545 67.3
Which organization do you
work for?
Government 462 57.0
Private 187 23.1
Self-employed 72 8.9
Unemployed 89 11.0
Total family income
Less than 5000 SAR * 54 6.7
5000–10,000 SAR 109 13.5
More than 10,000 SAR 647 79.9
Residence type Villa 710 87.7
Flat 100 12.3
Housing tenure
Own 581 71.7
Rented 126 15.6
Provided by employer 72 8.9
Other 31 3.8
* Note: 1 USD = 3.75 SAR (Saudi Arabian Riyal).
3.2. Analysis of Household Water Consumption and Water Use Behavior/Patterns
The results of the survey showed that a high percentage (94.9%) of respondents
from the total sample (810) worked from home during the lockdown. Likewise, most
participants (95.9%) reported an increase in their water consumption during the COVID-19
lockdown. Nearly 86.3% of the respondents reported more than a 50% increase in their
water consumption, with the highest percentage of water consumption for kitchen and
cooking (82.6%) purposes, followed by hygiene needs (81.7%), watering plants (74%),
recreation (swimming pool) (71.5%), and washing vehicles (50.2%). Consequent to the
increase in water usage, about 97.2% of the participants affirmed that their water bills
were higher during April–May 2020 than April–May 2019 and that the increment in the
water bill during April–May 2020 was more than 100 riyals for 75.7% of the respondents.
Nevertheless, about 4.1% of the participants reported no change (2.6%) or a decrease (1.5%)
in water consumption.
Table 2and Figure 2show the overall water use behavior/patterns. The total mean
score of 3.65 out of 5 points with a standard deviation of 0.813 corresponded to a high
level as per the 5-point Likert scale [
62
], indicating the high level of water consumption
during COVID-19 (Table 2). A breakdown of the results shows that the participants neither
considered adopting water-saving measures during the pandemic nor were they willing to
adopt it now. The respondents did not reveal much concern regarding the increase in water
consumption during the pandemic (Table 2).
Table 2. Water-use behavior/patterns.
N Minimum Maximum Mean Std.
Deviation Level Rank
I did not adopt any water-saving measures
during the COVID-19 lockdown 810 1 5 3.83 1.384 High 1
Water 2022,14, 314 7 of 16
Table 2. Cont.
N Minimum Maximum Mean Std.
Deviation Level Rank
I do not consider an increase in water
consumption during COVID-19 lockdown
as a concern
810 1 5 3.37 1.501
Moderate
5
I agree that more water was consumed during
the COVID-19 lockdown 810 1 5 3.82 1.504 High 2
My electricity bill was higher during the
COVID-19 lockdown 810 1 5 3.69 1.440 High 3
I do not plan to adopt any measures to reduce
water and electricity consumption/bill 810 1 5 3.52 1.275 High 4
Water-Use Behavior/Patterns 810 1.20 5.00 3.65 0.813 High
Water 2022, 14, x FOR PEER REVIEW 7 of 17
Figure 2. Water-use behavior patterns.
Table 2. Water-use behavior/patterns.
N Minimum
Maxi-
mum Mean Std. Devia-
tion Level Rank
I did not adopt any water-sav-
ing measures during the
COVID-19 lockdown
810 1 5 3.83 1.384 High 1
I do not consider an increase in
water consumption during
COVID-19 lockdown as a con-
cern
810 1 5 3.37 1.501 Moderate 5
I agree that more water was
consumed during the COVID-
19 lockdown
810 1 5 3.82 1.504 High 2
My electricity bill was higher
during the COVID-19 lock-
down
810 1 5 3.69 1.440 High 3
I do not plan to adopt any
measures to reduce water and
electricity consumption/bill
810 1 5 3.52 1.275 High 4
Water-Use Behavior/Patterns 810 1.20 5.00 3.65 0.813 High
3.3. Association Between Demographic Features and Water Use Behavior/Patterns
Pearson’s Chi-squared test (χ
2
) is a statistical test applied to sets of categorical data
to test the independence of two variables, expressed in a contingency table. Independence
implies that the value of the row variable does not change the probabilities of the column
variable (and vice versa) or that the row percentages (or column percentages) remain con-
stant from row to row (or column to column) [63]. Chi-Square results show that there is a
statistically significant association between water-use behavior and demographic features
(p < 0.01) (See Table 3). Visual display of the results can be seen in Figure 3.
Figure 2. Water-use behavior patterns.
3.3. Association between Demographic Features and Water Use Behavior/Patterns
Pearson’s Chi-squared test (
χ2
) is a statistical test applied to sets of categorical data to
test the independence of two variables, expressed in a contingency table. Independence
implies that the value of the row variable does not change the probabilities of the column
variable (and vice versa) or that the row percentages (or column percentages) remain
constant from row to row (or column to column) [
63
]. Chi-Square results show that there is
a statistically significant association between water-use behavior and demographic features
(p< 0.01) (See Table 3). Visual display of the results can be seen in Figure 3.
Water 2022,14, 314 8 of 16
Table 3. Association between demographic features and water-use behavior/patterns.
Water Use Behavior/Patterns
Total
Low Medium High Chi Square p-Value
Gender Male 26.4% 19.6% 54.0% 100.0% 138.975 0.000
Female 0.0% 13.0% 87.0% 100.0%
Nationality Saudi 6.9% 12.5% 80.6% 100.0% 126.018 0.000
Non-Saudi 31.6% 26.3% 42.1% 100.0%
Residential location
Dammam 7.1% 8.6% 84.3% 100.0%
112.306 0.000
Khobar 27.0% 24.9% 48.1% 100.0%
Dhahran 5.7% 18.3% 76.0% 100.0%
Marital status
Single 14.5% 15.0% 70.5% 100.0%
30.911 0.000
Married 6.3% 5.4% 88.4% 100.0%
Divorced 8.1% 18.0% 73.9% 100.0%
Widowed 17.3% 19.9% 62.8% 100.0%
Resident family
members
Under 5 20.5% 23.5% 56.1% 100.0%
54.974 0.000
6–10 9.2% 13.9% 76.9% 100.0%
11–20 29.7% 20.7% 49.5% 100.0%
Age
Under 25 18.3% 22.5% 59.2% 100.0%
38.221 0.000
26–35 15.0% 14.5% 70.5% 100.0%
36–45 2.0% 2.0% 96.0% 100.0%
46–55 17.5% 17.5% 65.0% 100.0%
55–65 2.6% 26.9% 70.5% 100.0%
66 or more 15.3% 13.9% 70.8% 100.0%
Water 2022, 14, x FOR PEER REVIEW 9 of 17
Figure 3. Relationship between behavior patterns and demographic features.
3.4. Differences in Water-use Behavior Based on Household Water Consumption
From a quick look at the data, it is not possible to determine whether the data samples
are independent or dependent, and since there is the need for a statistical difference be-
tween the means, a one-way ANOVA test is performed [64]. ANOVA results show that
there is a statistically significant difference in water-use behavior based on household wa-
ter consumption (p < 0.01). Post hoc comparison tests show that this significant difference
was found to support working from home, increase in water use, more than 50% increase
in water use, higher water bills during April–May 2020 than April–May 2019, and water
bill increment of more than 100 riyals (See Table 4 and Figure 4). Therefore, working from
home led to significantly different water-use behavior compared with working in essen-
tial services and staying at home without working. The differences between those work-
ing from home and those at home but not working could be attributed to the schedules
that stay-at-home workers must follow to complete their work. While those who were not
working might sleep for longer hours, those working could not. That is, longer working
hours lead to higher use of water.
Table 4. ANOVA test for differences in water-use behavior based on household water consumption.
N Mean Std. Deviation F Sig.
How did you adapt to
working during COVID-
19?
Worked from home 769 3.6809 0.79008
12.646 0.000
Worked in essential service 27 3.1185 1.02321
Stayed at home without
working 14 2.9000 0.93397
Was there any change in
your water consumption
during COVID-19?
Increase in water use 777 3.7048 0.75818
54.709 0.000
No change 21 2.5810 0.75075
Decrease in water use 12 1.8833 1.09032
By what percentage did
your water consumption
change?
Less than 20% 18 2.9889 0.96643
36.157 0.000
20–50% 71 3.4225 0.74185
More than 50% 699 3.7345 0.76139
Not sure 22 2.1909 0.74955
Figure 3. Relationship between behavior patterns and demographic features.
Water 2022,14, 314 9 of 16
The results reveal that the water-use level of females was significantly higher than
that of males; the water use of Saudi citizens was higher than that of expatriates. Dammam
respondents used more water than those from Khobar and Dhahran. Respondents who
were married participants showed a higher level of water use than those who were not. A
family with 6–11 members used more water than those with fewer members. Similarly,
young adults between the ages of 36 and 45 showed a propensity to use more water than
other age groups. These results provide information for further exploring demographic
variables in the interventions to reduce water consumption.
3.4. Differences in Water-Use Behavior Based on Household Water Consumption
From a quick look at the data, it is not possible to determine whether the data samples
are independent or dependent, and since there is the need for a statistical difference between
the means, a one-way ANOVA test is performed [
64
]. ANOVA results show that there
is a statistically significant difference in water-use behavior based on household water
consumption (p< 0.01). Post hoc comparison tests show that this significant difference
was found to support working from home, increase in water use, more than 50% increase
in water use, higher water bills during April–May 2020 than April–May 2019, and water
bill increment of more than 100 riyals (See Table 4and Figure 4). Therefore, working from
home led to significantly different water-use behavior compared with working in essential
services and staying at home without working. The differences between those working
from home and those at home but not working could be attributed to the schedules that
stay-at-home workers must follow to complete their work. While those who were not
working might sleep for longer hours, those working could not. That is, longer working
hours lead to higher use of water.
Table 4.
ANOVA test for differences in water-use behavior based on household water consumption.
N Mean Std. Deviation F Sig.
How did you adapt to
working during COVID-19?
Worked from home 769 3.6809 0.79008
12.646 0.000
Worked in essential service
27 3.1185 1.02321
Stayed at home
without working 14 2.9000 0.93397
Was there any change in
your water consumption
during COVID-19?
Increase in water use 777 3.7048 0.75818
54.709 0.000
No change 21 2.5810 0.75075
Decrease in water use 12 1.8833 1.09032
By what percentage did your
water consumption change?
Less than 20% 18 2.9889 0.96643
36.157 0.000
20–50% 71 3.4225 0.74185
More than 50% 699 3.7345 0.76139
Not sure 22 2.1909 0.74955
My water bill was higher
during April–May 2020 than
April–May 2019?
Yes 787 3.6933 0.77090
47.304 0.000
Not sure 16 2.2500 0.72480
No 7 1.8286 0.72506
Approximate increment in
my water bill during
April–May 2020:
Less than 20 riyals 11 2.8000 0.90333
22.945 0.000
20–50 riyals 50 3.3360 0.76285
51–100 riyals 109 3.6128 0.68056
More than 100 riyals 613 3.7465 0.77215
Not sure 27 2.4963 1.03682
Water 2022,14, 314 10 of 16
Water 2022, 14, x FOR PEER REVIEW 10 of 17
My water bill was higher
during April–May 2020
than April–May 2019?
Yes 787 3.6933 0.77090
47.304 0.000
Not sure 16 2.2500 0.72480
No 7 1.8286 0.72506
Approximate increment
in my water bill during
April–May 2020:
Less than 20 riyals 11 2.8000 0.90333
22.945 0.000
20–50 riyals 50 3.3360 0.76285
51–100 riyals 109 3.6128 0.68056
More than 100 riyals 613 3.7465 0.77215
Not sure 27 2.4963 1.03682
Figure 4. Changes in household water consumption.
A t-test is a type of inferential statistics used widely to demonstrate the difference in
the mean of the two groups [65]. T-test results show that there is a statistically significant
difference in water-use behavior based on the increasing consumption; the highest mean
score of water wastage was for kitchen and cooking by 3.7986, followed by watering the
garden by 3.7699, recreation by 3.7614, washing vehicles by 3.7494, and hygiene and
shower by 3.7178 (See Table 5 and Figure 5).
Table 5. T-test for significant differences in water-use behavior based on ways in which water usage
increases.
Please Tick the Option
that Best Describes Your
Increased Usage of Wa-
ter Consumption
N Mean Std. Deviation t p-value
Hygiene and frequent
showers
No 148 3.3392 0.82203 −5.207 0.000
Yes 662 3.7178 0.79477
Kitchen and cooking No 141 3.2727 0.93909 −9.022 0.000
Yes 669 3.7986 0.73599
Watering the garden No 211 3.3043 0.84365 −7.058 0.000
Yes 599 3.7699 0.76602
Recreation (swimming
pool)
No 231 3.1135 0.83982 −8.286 0.000
Yes 579 3.7614 0.75138
Washing vehicles no 403 3.5469 0.88933 −3.568 0.000
yes 407 3.7494 0.71569
Figure 4. Changes in household water consumption.
At-test is a type of inferential statistics used widely to demonstrate the difference in
the mean of the two groups [
65
]. T-test results show that there is a statistically significant
difference in water-use behavior based on the increasing consumption; the highest mean
score of water wastage was for kitchen and cooking by 3.7986, followed by watering the
garden by 3.7699, recreation by 3.7614, washing vehicles by 3.7494, and hygiene and shower
by 3.7178 (See Table 5and Figure 5).
Table 5.
T-test for significant differences in water-use behavior based on ways in which water
usage increases.
Please Tick the Option that Best Describes
Your Increased Usage of Water Consumption N Mean Std. Deviation t p-Value
Hygiene and frequent showers No 148 3.3392 0.82203
−5.207 0.000
Yes 662 3.7178 0.79477
Kitchen and cooking No 141 3.2727 0.93909
−9.022 0.000
Yes 669 3.7986 0.73599
Watering the garden No 211 3.3043 0.84365
−7.058 0.000
Yes 599 3.7699 0.76602
Recreation (swimming pool) No 231 3.1135 0.83982
−8.286 0.000
Yes 579 3.7614 0.75138
Washing vehicles no 403 3.5469 0.88933
−3.568 0.000
yes 407 3.7494 0.71569
3.5. Multiple Regression of Predictors of Increasing Water Usage Behavior
Multiple regression tests were performed to discover the predictors/factors that
influence the increase in water-use behavior. The predictor of the dependent variable (DV)
is the water use behavior as a continuous variable which out from the overall mean score
of the items of water use behavior, which ranged between 1.20 and 5, and the independent
variables were education, work organization, income, home type and housing tenure.
Water 2022,14, 314 11 of 16
Water 2022, 14, x FOR PEER REVIEW 11 of 17
Figure 5. Water consumption based on household activities.
3.5. Multiple Regression of Predictors of Increasing Water Usage Behavior
Multiple regression tests were performed to discover the predictors/factors that in-
fluence the increase in water-use behavior. The predictor of the dependent variable (DV)
is the water use behavior as a continuous variable which out from the overall mean score
of the items of water use behavior, which ranged between 1.20 and 5, and the independent
variables were education, work organization, income, home type and housing tenure.
A multi-collinearity test was conducted for all regression models. The results re-
vealed that the VIF (the variance inflation factor) for all models was <3 indicating there
was no multi-collinearity problem. Further, all models’ residual was normally distributed.
Thus, the assumptions for regression analysis were met. The model was well fit (R = 0.413,
R Square = 0.170, F = 33.046, p = 0.000 p < 0.05). The results show the significant effect of
education, work organization, total family income, and residence type on water-use be-
havior (Table 6).
Table 6. Multiple regression of predictors of increasing water use behavior.
Model Unstandardized Coefficients Standardized Coeffi-
cients t Sig.
B Std. Error Beta
1
(Constant) 2.491 0.287 8.692 0.000
Education 0.062 0.030 0.068 2.087 0.037
Work organization 0.252 0.026 0.315 9.699 0.000
Total family income 0.282 0.063 0.199 4.492 0.000
Residence type −0.328 0.101 −0.133 −3.238 0.001
Housing tenure 0.073 0.038 0.073 1.932 0.054
R = 0.413, R Square = 0.170, F = 33.046, Sig. = 0.000
The results indicate that those with higher level of education have a tendency to use
more water (Beta = 0.062, p = 0.037 *) and the same applies to those whose high family
income is high (Beta = 0.282, p = 0.000 **) and have big houses (villas) (Beta= −0.328, p =
0.001 **). Those that are unemployed showed a higher propensity to use more water than
the employed. Housing tenure did not have a significant effect on water-use behavior (p
> 0.05). Based on the results of multiple regression, the equation for the model would be
presented as follow:
Figure 5. Water consumption based on household activities.
A multi-collinearity test was conducted for all regression models. The results revealed
that the VIF (the variance inflation factor) for all models was <3 indicating there was no
multi-collinearity problem. Further, all models’ residual was normally distributed. Thus,
the assumptions for regression analysis were met. The model was well fit (R = 0.413,
R Square = 0.170, F = 33.046, p= 0.000 p< 0.05). The results show the significant effect
of education, work organization, total family income, and residence type on water-use
behavior (Table 6).
Table 6. Multiple regression of predictors of increasing water use behavior.
Model
Unstandardized Coefficients Standardized Coefficients
tSig.
B Std. Error Beta
1
(Constant) 2.491 0.287 8.692 0.000
Education 0.062 0.030 0.068 2.087 0.037
Work organization 0.252 0.026 0.315 9.699 0.000
Total family income 0.282 0.063 0.199 4.492 0.000
Residence type −0.328 0.101 −0.133 −3.238 0.001
Housing tenure 0.073 0.038 0.073 1.932 0.054
R = 0.413, R Square = 0.170, F = 33.046, Sig. = 0.000
The results indicate that those with higher level of education have a tendency to use
more water (Beta = 0.062, p= 0.037 *) and the same applies to those whose high family
income is high (Beta = 0.282,
p= 0.000 **
) and have big houses (villas) (Beta=
−
0.328,
p= 0.001 **
). Those that are unemployed showed a higher propensity to use more water
than the employed. Housing tenure did not have a significant effect on water-use behavior
(p> 0.05). Based on the results of multiple regression, the equation for the model would be
presented as follow:
Y = 2.491 + 0.062 (X1) + 0.252 (X2) +0.282 (X3)−0.328 (X4) + 0.073(X5)
where,
Y: Water usage behavior (DV Predicted);
X1: Education;
Water 2022,14, 314 12 of 16
X2: Work organization;
X3: Income;
X4: Housing tenure;
X5: Housing type.
In the interviews that were conducted, the interviewees highlighted the following
points in their responses.
i. Water consumption increased during the lockdown;
ii. The existing water system needs to be improved to meet emergency demands;
iii.
Government policies were not revised during the lockdown;
iv.
Experts suggest changing the water policy and legislation during crises or emer-
gency lockdown situations by a temporary reduction in consumption tariffs or no
disconnection of water supply if payment is delayed;
v.
Some operational faults were also mentioned, such as the reduction in water dis-
tributed to customers.
4. Discussion
As per the results and the participant responses, water consumption increased by
more than 50% in DMA during the lockdown, with a corresponding increase in utility bills.
This buttresses the report by Abu-Bakar et al. [
58
] that water consumption increased by
46% in England during the lockdown. In another study, the increase in water consumption
in households was between 15 and 20% percent, while commercial water consumption
decreased by 30–50% [
59
]. In the GCC, Abulibdeh [
66
] and Rizvi et al. [
67
] reported higher
usage of water in the residential sector during the COVID-19 lockdown in Qatar and UAE.
The percentage increase found in Qatar was a minimum of 9%, depending on the building,
either villa or flat. Further, this study revealed that water-use behavior was influenced by
both demographic and economic factors. Factors such as gender, nationality, age, house
type, employment, and education affected patterns of water consumption. Balacco et al. [
68
]
examined the impact of restrictive measures on demand for water during the lockdown by
inspecting the instant flow data of five towns in Apulia, Italy, focusing on user habits. The
population water-use behavior and awareness, climate, and the price of the water were
found to influence the demand for drinking water.
A significant number of respondents in this study mentioned that they were not ready
to adopt water-saving methods. People need to adopt new behavioral patterns to minimize
water-related bills related to water. Therefore, it is pertinent to raise awareness on how
one can conserve water and at the same time be protected from the virus by regular hand
washing. This will also have an impact on service delivery. Policymakers can adopt policies
that reward “good behaviour” as highlighted by Ashour et al. [
69
] and Lusk et al. [
6
]. The
differential tariff already in place can be reviewed to increase the prices for consumptions
that are more than the average. A policy should be enacted to aid the deployment of
smart water management technologies as part of the smart city initiatives [
70
]. Above all,
citizenship enlightenment and engagement are very important in promoting sustainable
consumer behavior.
Understanding the new variations in the use of water during, before, and after the
COVID-19 pandemic will help in preparing strategies to improve the supply of water.
These strategies are important since the interviewees mentioned instances of operational
failures that led to reductions in the water supply. Behavioral practices such as attaining
higher education and gaining knowledge about conservation were stated to be correlated
with good hygiene practices such as handwashing [
71
]. Moreover, decision-makers can
explore the information on the relationship between the demographic profile and water-use
behavior in targeted awareness campaigns and intervention actions.
Apart from the operational failures, the interviewed experts also highlighted the need
for system improvement, especially during emergencies. Government interventions in the
form of policy changes are essential to reduce the impact of operational failures on the
populace. Thus, long- and short-term goals are needed to enhance the supply of water and
Water 2022,14, 314 13 of 16
establish a flexible water system that could reduce the shocks from future emergencies or
pandemics. This is crucial in safeguarding the millions living in densely populated cities.
With COVID-19 constantly spreading in developing countries where there is no adequate
supply of clean water, the population of these areas continues to be highly vulnerable [
72
].
It has been well-established that in developing countries, there is a lack of adequate supply
of water and provision for handwashing [
73
]. Under such conditions, flexible governance
and planning responses are needed to achieve proper management against this shock and
its consequences [74].
One of the findings was that working from home is a major factor in increased water
consumption during the lockdown. Even the number of respondents reporting washing
vehicles is still high despite staying at home. Most people continue to wash their cars
since they have already paid for the car to be washed. Moreover, sandstorms in the desert
environment can make the car very dirty. People did not travel to work, but they still
obtained permission to shop and perform essential tasks. Saudi has a youthful population,
and some of them take car washing as recreation. Similarly, recent studies on water
demand have clarified the relocation of water usage from public spaces to homes, with
high water consumption practices, at the beginning of the COVID-19 pandemic, but this
has reduced over time [
75
]. Several demand strategies rely on existing sociodemographic
and socioeconomic household changes and behaviors self-reported through surveys [
76
,
77
].
The findings of this study will help draft more accurate interventions and forecasting when
enriched with socioeconomic and sociodemographic variables.
5. Conclusions
This paper contributes to the empirical literature on the water consumption/usage
behavioral practices associated with the COVID-19 pandemic by exploring the utilization
of household water during COVID-19 in the Kingdom of Saudi Arabia. By considering
the probable continuation of the threat of the pandemic, it is necessary to understand the
preparations made by individuals and the government in terms of essential services that
must be available under emergency circumstances. The study findings reveal that the
lockdown due to the COVID-19 pandemic had a significant impact on household water
consumption behavior influenced by demographic and economic factors. Moreover, there
was a reduction in water distribution during the period and a lack of policy interventions
to reduce the impacts.
There is a need to incorporate disaster management strategies into water policies
to manage public health emergencies, such as the COVID-19 pandemic, in a better way.
Antwi et al. [
78
] highlighted that government intervention and policy in Europe helped to
alleviate the impact of the lockdown on water usage. Further studies can investigate the use
of water consumption data from the water corporation to validate the water consumption
pattern derived from the questionnaire survey.
Author Contributions:
Conceptualization, A.I.A. and Y.A.A.; Data curation, A.I.A.; Formal analy-
sis, A.I.A.; Funding acquisition, A.I.A.; Investigation, A.I.A. and Y.A.A.; Methodology, A.I.A. and
Y.A.A.; Resources, A.I.A. and Y.A.A.; Visualization, A.I.A.; Writing—original draft, A.I.A. and Y.A.A.;
Writing—review and editing, A.I.A. and Y.A.A. All authors have read and agreed to the published
version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Not applicable.
Acknowledgments:
The first author would like to acknowledge the support from the Deanship of
Scientific Research at Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia.
Conflicts of Interest: The authors declare no conflict of interest.
Water 2022,14, 314 14 of 16
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