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Article

Assessment of the Effects of COVID-19 Pandemic Stay-at-Home Measures on Potable Water Consumption Patterns, Location, and Financial Impacts for Water Utilities in Colombian Cities

1
Water Distribution and Sewerage Systems Research Center, Universidad de los Andes, Bogotá 111711, Colombia
2
Department of Civil and Architectural Engineering and Mechanics, University of Arizona, Tucson, AZ 85721, USA
3
Civil and Environmental Engineering Department, Universidad de los Andes, Bogotá 111711, Colombia
*
Author to whom correspondence should be addressed.
Water 2022, 14(19), 3004; https://doi.org/10.3390/w14193004
Submission received: 10 September 2022 / Revised: 17 September 2022 / Accepted: 19 September 2022 / Published: 24 September 2022
(This article belongs to the Section Urban Water Management)

Abstract

:
Several studies suggest that social distancing measures due to the COVID-19 pandemic have affected the water sector, specifically regarding its demand and supply. Given the importance of hygiene practices, this effect is heightened by the role that potable water availability has in tackling the spread of the virus. This study aimed to assess the impact of the pandemic on the water consumption patterns and location in four Colombian cities known for their important commercial, industrial, academic, and touristic features. Results exhibit diverse diminishing water consumption trends alongside COVID-19 because of different attributes of the cities (e.g., size, environmental, socioeconomic, and sociocultural characteristics). For instance, the touristic case study has been the most affected because of travel restrictions, with an average commercial demand drop of 32%. In contrast, industrial case studies have had a rapid recovery in water demand, with average industrial drops of 11–14% compared to 20–25% in non-industrial cities. These water demand changes do not affect only the operation of water utilities, but also their finances. Economic losses were estimated at 3.7%, 2.4%, and 6.4% of the expected incomes for the first 14 months of the pandemic for the case studies in this paper. Under a changing environment, understanding these changes and challenges is fundamental for ensuring that water systems are resilient in any unexpected situation.

1. Introduction

The prompt spread of the Severe Acute Respiratory Syndrome-Coronavirus-2 (SARS-CoV-2 virus), responsible for the COVID-19 pandemic, has imposed challenges on the entire world since late 2019. As the extent of the pandemic remains indeterminate, even with the continuing vaccination process, governments have been forced to apply measures to tackle the propagation of the virus. The actions taken to minimize the consequences of the SARS-CoV-2 virus spread differ in every city according to specific characteristics such as population and healthcare system capacity. Social distancing, mandatory face masks, frequent handwashing, and the disinfection of regularly manipulated surfaces have been the most common and effective measures. In this context, the aim of restricting social interactions has sought to avoid the collapse of healthcare systems, delaying the spread of the virus. Consequently, the habits of people have been altered, triggering variations in the water consumption patterns of cities. As water sector workers worldwide have reported [1,2,3], these sudden variations have impacted the operation and finances of water utilities. Hence, understanding how water demand changed and how the systems responded is crucial to prepare for any coming unexpected event and enhance emergency contingency plans.
Several investigations in cities around the world evidenced that sudden variations in the lifestyles of people modified water consumption patterns, potentially affecting the water supply systems. For instance, 86% of responses to a survey in Saudi Arabia indicated an increase of around 50% in their water demand [4]. Moreover, Abu-Bakar, Williams, and Hallett studied the specific effect of changes in households’ water usage practices in England by analyzing clusters of water consumption patterns and studying how the pandemic modified them. Among their results, users who typically consumed more water in the late morning augmented water demand by 37% on average, and households who usually used more water in the early morning reduced their morning peak contribution from 40% to 20% [5]. In Hamburg, Germany, the daily water consumption increased by 14.3% in households according to results from linear mixed models. A shift of 1.5 h was noticed in the morning water usage peak, in addition to a more extensive evening peak [6]. Similar observations were obtained for five towns in the Puglia region in Italy when comparing 2019 to 2020 water demand records. For small towns, the morning peaks were delayed by 2–2.5 h, sporadically merging with the afternoon peaks. In larger towns, a drop in the peak and base water demands was evidenced due to reduced incoming commuters because of the pandemic restrictions [7].
Not only has domestic water usage been affected, but also other economic sectors. A study conducted in Henderson, Nevada, demonstrated an increase of 11.7–13.1% in residential demand and a drop of 34.1–35.7% and 55.8–66.2% in commercial and schools’ water average daily demand, respectively, by comparing 2020 records to previous years [8]. In the study of Kalbusch et al. for Joinville, Brazil [9], an increase of 11% in domestic water demand was demonstrated using statistical tests, while a decrease of 42% was estimated for daily commercial water use. Similarly, a reduction of 53% was evidenced for industrial water demand and 30% for public demand [9]. Analogously, Li et al. [10] evaluated the pandemic response exclusively by neglecting the effect of other water consumption variation drivers, such as precipitation or population growth. The authors found an average decrease of 7.9% in the total urban water use in California using multivariate regression models. The total reduction in water demand is mainly due to a decrease in water usage in the commercial, industrial, and institutional sectors of 11.2%, while there was a slight increase in the residential sector of 1.4% [10].
These water consumption variations have affected the operations and financial performance of water utilities. Numerous utilities have had to work in non-design conditions and with decreased workforce and financial capabilities [2]. Water supply systems are designed according to an estimation of the future water demand according to historical consumption patterns. However, the pandemic has triggered different scenarios that the systems must confront [11]. Thus, sudden demand variations, which imply a relocation of water use, might have added more pressure to certain network elements. Furthermore, depending on the tariff structure, the proportion of domestic and non-domestic users, and the water usage changes, utilities were likely to experience revenue losses, heightening the strained circumstances arising from higher operational costs and non-payment from economically affected customers [12]. For instance, significant drops in industrial and commercial water demand substantially reduce water utility revenues [13]. Cities dependent on affected economic activities, such as tourism, have been the most affected [3].
As the World Health Organization (WHO) states, the availability of fresh water is crucial to preserve human health and reduce the spread of the virus, particularly during the COVID-19 pandemic [14]. Therefore, water supply systems must be resilient to unstable and uncertain situations. Based on the latter, long-term planning is essential for adapting water systems, especially as many cities are facing or expecting to handle water stress due to population growth, gentrification, and climate change. The pandemic experience gives an insight into upcoming water demand variations and revenue modification due to these phenomena [2]. The analysis of actual water usage changes demonstrates its capability to provide valuable information for water agencies, managers, and policymakers [15]. In this way, a set of actions can be taken consciously and determinedly to secure present water needs and adapt operations and infrastructure for future water supply requirements. Hence, this study aimed to assess the impact of the pandemic on the water consumption patterns and location in four Colombian cities and its financial consequences on water utilities. Consumption data collected by water utilities were employed for this aim, demonstrating that it should be used as a tool for enhancing water distribution systems. Obtaining and organizing water demand information was a relevant challenge, indicating how water utilities should prioritize good data management to facilitate system analysis, operation, and control.
Following this introduction, the materials and methods used in the analysis are presented, including a detailed description of the case studies. For this study, the data from each city differed significantly among utilities, but it was possible to organize and unify it for the analysis, resulting in valuable outcomes. The results for water demand variations, spatial differences, and financial impacts are presented and discussed in the next section. Financial impacts were found to be one of the most important contributions, showing how water utilities are highly vulnerable to water demand variations. Finally, the last section presents the most relevant conclusions from the study.

2. Materials and Methods

This study consisted of comparing a projection of water demand using pre-pandemic data versus the actual water demand during the pandemic to determine the impact of the COVID-19 response measures on water consumption patterns. The description of the methodology is summarized in Figure 1, and is detailed in this section as follows: first, the chosen case studies and the data used for the analysis are presented. Then, the models employed for estimating the notional non-pandemic water usage scenario are explained, and, finally, the explicative variables are described for each model. Figure 1 shows how water demand data were used for this aim. The initial dataset was divided into two sets: historical and pandemic data. The historical data were used for demand modeling, where the model is indicated in the dashed square and the employed explicative variables are listed. The modeled demand and the original pandemic data were the tools used for analyzing the effects of the pandemic on water demand patterns and the financial consequences.

2.1. Case Studies

In addition to many other countries, Colombia has been affected by the COVID-19 pandemic. On 24 March 2020, the Colombian presidency, supported by experts from the Ministry of Health, the National Institute of Health, and the Pan American Health Organization (PAHO), declared “obligatory preventive isolation” throughout the country [16]. These social distancing measures entailed that only activities considered “essential” were allowed to operate tangibly to guarantee the production and catering of food and medicines. Afterwards, a second isolation phase was established from 1 June until 31 August, whose objective was to recover productivity while continuing to limit social interactions [17]. Then, the phase starting on 1 September included the consent to perform more activities, such as commuting and the re-opening of restaurants [17]. Despite social distancing measures being extended until the first four months of 2021 [18], a set of limitations was put in place during the holidays to avoid the spread of the virus, including early closure of businesses and curfews.
Several factors, such as size, climate, tourism, and principal economic activities, were expected to influence the response of water consumption to these social distancing measures. Thus, four Colombian cities were selected in this study to assess the effect of the pandemic in municipalities, considering diverse backgrounds in terms of their social, environmental, and economic characteristics. Additionally, many studies regarding the effects of the pandemic on water consumption can be found in the literature for developed countries, but none for developing countries. Therefore, this study aimed to contribute to the progress in the water field research for these nations and analyze the possible differences from developed countries. In this way, Table 1 presents the main information of the case studies. The geographic and demographic information was obtained from the national statistics agency (DANE) [19]. The average number of users refers to the mean total number of households per billing period from January 2016 to February 2020 and is presented to illustrate the size of water utilities before the pandemic.
In Colombia, Law 142 of 1994, which regulates domiciliary public services, states that tariffs are defined for every type of user: domestic, commercial, industrial, official, and special. Additionally, residential users are billed according to their socioeconomic strata [20]. Residential properties are classified in accordance with the characteristics of the households and their urban or rural environment, resulting in six classes, locally known as strata, as an approximation to a hierarchical socioeconomic difference [21]. Strata 1 users are those with the lowest purchasing power, whereas Strata 6 users are those with the highest.
Water utilities classify their users according to the designation explained above. The official category includes educational establishments, such as schools and universities, and public establishments that do not permanently develop commercial or industrial activities, such as hospitals, clinics, nursing homes, and orphanages. Special users are non-profit entities and charitable, cultural, and social service institutions [22]. Not all water utilities separate customers into official and special categories since they manage the same water tariffs. Therefore, for the purpose of this research and to handle the same categories among all the case studies, the public category presented here is the sum of these last subcategories.
The distribution of water consumption in each city analyzed is shown in Figure 2. In all case studies, the predominant proportion of water usage corresponds to the domestic category. Households account for 90.1% of total customers in City A, 92.6% in City B, 95% in City C, and 94.1% in City D. The predominance of non-domestic water demand differs according to the characteristics of each city. The “other” category includes additional users that some utilities manage and do not correspond to any of those mentioned above.
First, given the large size of City A and its economic importance, the main activities performed within the urban area are diverse. Hence, non-domestic customers are mixed, reflecting a dispersed water consumption where commercial demand is the largest after household water usage. The second case study, City B, comprises various municipalities that add a large urban area and number of users, as Table 1 shows. The industrial, commercial, and service industries are major economic drivers in the city, representing a large proportion of industrial and commercial water demand. City C is primarily residential but is also an important touristic city in Colombia. Therefore, Figure 2 shows the significance of commercial water consumption over the rest of the non-domestic demand. Given the importance of tourism in this case study, the impact of travel restrictions due to the pandemic was expected to be highly significant. Finally, City D is the smallest case study city and is recognized as a college town. Accordingly, it is the city with the largest proportion of public users, but the non-domestic water usage is similarly distributed among commercial, industrial, and public customers. As activities in educational institutions were particularly disturbed, the effect of stay-at-home orders in City D can also be substantial.

2.2. Data Description and Preparation

Water utilities of the selected study cases provided water consumption volumes and the number of users per billing period since January 2016. Utilities collect these data for commercial management purposes, but they were exploited for this research. Each utility manages its records in a different way, making the unified analysis more complicated. Hence, the data samples were adjusted for differentiating water consumption by zones (district metered areas or administrative supply zones) covering the entire service areas. Table 2 summarizes the data collected from each case study, with the specification of temporal and spatial scales.
Water tariffs (fixed and volumetric rates), commuting population incoming and outgoing, maximum temperature, and precipitation data were used for the research. In this context, the commuting population refers to the people entering or leaving a specific city per month. These records were obtained from the governmental entity in charge of migratory regulation [23]. The data were collected from migratory controls within the service areas, but City D records were unavailable. Regarding weather, precipitation and maximum temperature data were obtained from the Institute of Hydrology, Meteorology and Environmental Studies (IDEAM) [24]. Based on this information, monthly precipitation and maximum temperature values for the entire service areas were calculated using Thiessen Polygons with multiple meteorological stations scattered throughout the four cities.
The complete analysis presented in this study was performed using MATLAB R2020b [25]. Foremost, pre-pandemic water volumes and the number of users time series were cleaned by removing anomalous values due to data loss or errors in the information management. The anomalous values were identified with a moving method as those elements outside three local standard deviations within six months. This process allowed the acquisition of smoother and more consistent water demand patterns, making the data reliable for further analysis. It is important to mention that the records from the pandemic periods were not modified since the study aimed to inspect if stay-at-home orders affected water use.

2.3. Estimation of Non-Pandemic Water Consumption Scenario

The effect of the COVID-19 pandemic on the water demand in the Colombian cities was analyzed by comparing two scenarios: the observed data during the pandemic and an estimated non-pandemic scenario. The latter water consumption scenario was computed by fitting water demand models using historic non-pandemic records (January 2016 to February 2020). Then, water demand was forecasted for the period during which the pandemic restriction measures were implemented, meaning from March 2020 until the date for which water utilities provided the information in each city (see Table 2).
Water demand was modeled for domestic, commercial, industrial, public, and total categories. For the modeling approach, the primary reference was the study performed by [10] for 395 water utilities in California using a multivariate ordinary least squares linear regression model. Their model used 24 predictor variables to control natural and human-derived influences on water consumption. As Figure 1 shows, this regression model was also used for the current study, employing the MATLAB code published by Li as guidance [26]. Nonetheless, different variables were considered in this research according to local conditions and user-specific characteristics. These considerations allowed accurately measuring the pandemic effect on water demand, ignoring other regular water demand variation drivers. Since the aim of this study was to show a specific case study, and multivariate regression models are well-known, no deep details are presented, but more information can be found in [26].

Explanatory Variables

Water consumption determinants vary significantly due to different aspects related to the type of water demand assessed (domestic, commercial, industrial) and the socio-demographic, economic, and environmental characteristics within a study area. For instance, Bich-Ngoc and Teller showed for a city in Belgium that, under normal conditions, residential water consumption increases due to population growth, urbanization, and higher living standards, whereas it decreases because of technological progress or mandatory water restrictions [27]. Hence, variations in water demand depend substantially on local circumstances due to differences in manners (cultural, community norms, religion), climate, environmental conditions, education, technology advancement, and water pricing structures and legislation. Furthermore, Bich-Ngoc and Teller [28] exposed that evaluating meteorological variables, such as daily maximum temperature and precipitation, has been frequently used for forecasting short-term variations in water demand. On the other hand, socioeconomic factors, such as income and land-use adaptations, are more relevant when modeling demand in a long-term period.
The selection of explanatory variables for modeling water consumption plays a crucial role in obtaining consistent and reliable results. Thus, different predictor variables were used for the water demand models according to the volumes under consideration. A list of the explicative variables for each model is shown in Figure 1. For all the models, seasonality was evaluated using binary variable coding for each period, where 1 represents if it applies to the month/bimester and 0 otherwise. Hence, six binary parameters were used for City A, as water volumes are reported every 2 months, and twelve for Cities B, C, and D with monthly data. In addition, for domestic and commercial water demand, six continuous variables were employed: precipitation, maximum temperature, fixed tariff, number of users, and commuting incoming and outgoing population. The inclusion of tariffs and commuting variables was key since they are socio-demographic determinants that have been demonstrated to influence water use [27]. For industrial and public demand, the same variables were applied except for the commuting population, totaling four continuous variables, since industrial and public water usage is not expected to be affected by that floating population. Lastly, the same variables as for domestic and commercial demand were selected for total water demand, excluding fixed tariffs, totaling five continuous variables. Tariffs were not considered for total demand as the water rate structure is divided by each type of user and cannot be generalized. Further, it is possible that these explicative variables do not adequately represent the variations in water usage or that the correlation between them and other missing parameters would affect the model results. However, these variables were chosen due to two reasons: their relevance to the evaluated water consumption and information accessibility. In addition, as water demand is forecasted only for 14 months, these variables were considered to be enough for the aim of the study.

2.4. Financial Impacts Assessment

The most common response to the pandemic emergency worldwide has been the moratoriums on shutoffs and the suspension of a segment of water billing for low-income customers [13]. In Colombia, specific initiatives were announced to ensure the access and continuity of water in homes, and the cleansing and disinfection of public places [29], which directly affected the financial status of water utilities countrywide. For example, utilities were forced to reconnect the water service to those users who had it suspended due to non-payment. In this way, the pandemic measures have imposed financial challenges for water utilities, such as suspensions on cut-offs, commercial income reduction, delays in water bill payments, and reduced customer growth [30].
In this study, the pandemic financial impacts on water utilities’ revenues were assessed by comparing the incomes that would have been received in a non-pandemic scenario with the actual profits. The actual revenues correspond to those billed in reality, meaning the pandemic affected demand. The expected revenues were calculated for the non-pandemic water volumes using the corresponding tariffs. These calculations were distinguished by domestic, commercial, industrial, and public categories. However, revenues are larger in reality because these classifications do not add up to the total water usage as the analysis did not include other categories managed by water utilities. Notably, since the results presented here correspond to billed revenues, they may differ from the actual earnings and the estimated financial losses. As the investigation conducted by Eastman et al. clarified, users’ misbehavior and non-payment tariffs are a significant source of uncertainty for the actual water utility’s income [31]. Actually, billed values do not correspond to the received income because users were allowed to defer their payments as the government ordered water utilities not to cut off the service to illegal customers as a strategy for controlling the spread of the virus.

3. Results and Discussion

This section presents the non-pandemic water consumption scenario estimation for each case study obtained through multivariate regressions. The outcomes were analyzed concerning water demand changes and spatial differences. Additionally, the impact on water utilities’ revenues was assessed by comparing the expected income for the theoretical non-pandemic scenario to the actual income for water consumed during the COVID-19 pandemic.

3.1. Water Demand Alteration

Initially, two sets of historical data were prepared: the 2016–2018 records for adjusting the models and the 2019 records for validating them. Nonetheless, the results were not satisfactory. Since the time series of available data is short, it was concluded that the entire series was essential to capture the water demand changes and patterns. Hence, using all the information as a training set was preferable. The regression models were fitted independently for each user category in each zone of the case studies (see Spatial Scale in Table 2), resulting in multiple regression models. Aggregated regression results were estimated to evaluate the prediction capability and the significance of the explanatory variables at a significance level of 5%. The explanatory variables’ significance level is briefly described here, and more details can be found in the Supplementary Materials.
For domestic water demand, the number of users is a significant predictor variable for most regression models. In contrast, precipitation is not a significant parameter in any case study, whereas temperature, tariffs, seasonality, and commuting population are. The maximum temperature presents a significant influence in City B since the weather presents high variations, and household water usage seems to increase when the temperature rises. Similarly, tariffs in City C are a good explanatory variable, suggesting a savings-oriented culture. The floating population is also significant due to the importance of tourism. Seasonality is a relevant factor in City D, which might be due to the educational activities performed in the city, so the academic calendar influences water demand patterns.
Regarding non-domestic demand, the number of users is significant for commercial demand in Cities A, B, and C, and seasonality and fixed tariffs in City D. Since this category includes the most common businesses visited by travelers, the commuting population was expected to be influential. However, many commuters could either live or arrive at residencies, increasing domestic water demand rather than commercial demand. Further, it is challenging to establish variables that could explain the variations in industrial and public water demand, considering that this profoundly relies on industries and public institutions’ operations, which are usually confidential information. For industrial water demand, the variables evaluated show that the number of users is a significant variable only in City C. For public demand, the number of users is determinant in all cities except City D. As observed with the domestic demand, seasonality in City D significantly explains the public water demand variations, confirming that the academic schedule may influence water consumption patterns.
Finally, total water demand could be determined by a considerably extensive list of parameters since it is the aggregation of water usage. Nonetheless, only the number of users had a statistically significant influence in all cities. These results evidence the struggle to find variables that could explain the variations in water consumption, especially for industrial and public categories, and suggest the need to explore more predictor variables. For instance, output elasticity measures, such as the number of employees or employee hours, are meaningful for explaining commercial and industrial demands [32]. Related to public demand, the size of buildings, the number of students, the average time spent in universities or schools, and the number of beds in hospitals, among other factors, could affect water usage. Therefore, more accurate models could be computed with this information. However, it was not possible to build these models due to the availability of data and the aggregation of water usage categories presented in this paper.
Thus, although only some of the explanatory variables are statistically significant, the prediction capacity of the models is acceptable. Figure 3 is presented to show the general results, including the accuracy of the models.
The model’s general accuracy is illustrated in Figure 3 by comparing the predictions (Modeled) and the actual water consumption (Reported) in the four cities for pre-pandemic records (before black vertical lines). The corresponding determination coefficients (R2) for calibration data are shown on the right of each plot. The prediction capacity of the models is good for almost all models, especially for domestic, commercial, and total water demand. These results allow the estimation of the pandemic’s impact on water demand; however, it is not possible to rely entirely on the obtained values since there is an implicit uncertainty. If the estimations were to be improved, adding new variables and modifying the parameters used in the regression models would be necessary. Furthermore, even though selecting an appropriate modeling method is challenging for academics and researchers, changing the modeling technique could be tested since the autocorrelation between the prediction variables employed for water demand forecasting affects the results [27]. Nonetheless, for the purpose of this research, the methods used provide reasonable outcomes that allow estimating the effect of the pandemic on water demand, so no further models were investigated.
Moreover, Figure 3 also presents the actual and the forecasted pandemic water demand (after black vertical lines). The comparison between both time series shows the effects of the pandemic in each case. This pandemic effect assessment is detailed in the following sections for domestic demand by socioeconomic strata; commercial, industrial, public, and total water demand; differences regarding zones in the cities; and financial effects for water utilities.

3.2. Socioeconomic Differences in Domestic Water Demand Changes

Based on the availability of information for City A, the results of the demand assessment cannot be accurately compared with those obtained for the other cities. Data from Cities B, C, and D comprise periods of the pandemic for which stay-at-home measures were less severe. Nonetheless, consistent results for the four cities show different effects of the pandemic on domestic water demand according to the socioeconomic level of users. Figure 4 shows the percentage difference between the actual and the estimated water demand volumes for each case study in the periods for which data was available. Notably, the water demand pattern for Strata 6 users in City A shows a rare tendency with abnormal peaks going down and up in May–June and July–August, respectively. These peaks may be attributed to data aggregation on a bimonthly basis due to errors in data management, yet the exact cause remains uncertain. Furthermore, between January and February 2020, a 0% difference would be expected because the data were used for fitting the models. Nonetheless, the differences are minor but not zero, which is attributed to the model’s forecasting capacities. Thus, it is not possible to study the obtained values precisely, but the outcomes are still a reasonable estimation of the pandemic impacts.
As seen in Figure 3, the net pandemic domestic water consumption volumes are slightly larger than the estimated non-pandemic scenario for all the case studies. Hence, results show how COVID-19 provoked increased domestic water consumption in Colombian cities, as seen in multiple case studies worldwide [4,5,6,7,8,9,10,15]. This increase is particularly appreciable for Strata 1, 2, and 3 users, which are the most abundant among utility customers. Thus, these changes generated a general increase in domestic demand.
A common interesting observation for the four cities is that the high-strata users had an average diminishing water consumption since the pandemic started. This observation indicates an unusual behavior explained by wealthy people leaving the cities to spend quarantine in calmer places, such as farms, vacation houses, or small towns. A similar phenomenon was observed in the United States, especially in large cities such as New York. For instance, evidence from Federal Reserve Bank of New York/Equifax Consumer Credit Panel (CCP) data shows an increase in out-migration in several urban neighborhoods across counties [33]. Hence, taking advantage of remote work and study, people relocated to avoid contact with other people, to spend time with their families in less populated areas, or to save money on housing. In this way, their water consumption also relocated, triggering variations in the water demand patterns of big cities, as for the case studies from this research.
These changes in domestic water consumption have direct implications for water utilities. Based on the results, the average water demand increase did not exceed 7% for any case study (see Table 3), so no operational challenges were expected. However, as high-strata users left the cities, the major revenues dropped, leading to economic losses.

3.3. General Changes in Water Demand

In contrast to the results for domestic demand by socioeconomic strata, the consumption changes for the commercial, industrial, and public categories vary from one case study to another. This effect is associated with the cultural and economic characteristics of the cities. A comparison between the actual and the estimated COVID-19 pandemic water consumption volumes for each user category is presented in Figure 5. Moreover, the average variations for the available periods are presented in Table 3.
As Figure 5 shows, monthly domestic water consumption increased slightly or remained practically untouched for all case studies. The cities where domestic demand increased the most (Cities A and C) are where the proportion of high-strata users is the smallest. Therefore, the differences in the magnitude of domestic consumption increase are explained by the reduction in water usage by high-strata users, as previously mentioned. Nonetheless, the changes in domestic water demand are much smoother than the drops in non-domestic water consumption, as occurred in other case studies such as those presented by Irwin et al. [8], Kalbusch et al. [9], and Li et al. [10]. Commercial demand is the category that decreased the most. Here, it is important to consider that City A’s outcomes correspond to a period having more drastic pandemic response measures, so the values presented in Table 3 seem more drastic. Nonetheless, when considering only the cities with the same data time frame, it is possible to notice how the strongest effects on commercial demand were in City C. This observation is consistent as commercial users are predominant among all the non-domestic users for this case study, corresponding mainly to hotels due to the economic importance of tourism. In this way, results for Colombian case studies are consistent with those of Boyle et al. [3], indicating that touristic cities have been the most affected during the pandemic.
Regarding industrial water usage, the effect of the pandemic also showed a severe reduction in consumption as only essential activities in industries were allowed to develop normally. Comparing the same time frame (from March to October 2020), the average drop was 25% in City A, 21% in City C, 16% in City B, and 12% in City D. The cities with the largest proportion of industrial water consumption, Cities B and D, had the lowest reduction in industrial demand. Therefore, due to the financial value of industries, it can be presumed that the companies rapidly applied measures to be allowed to operate as usual or at a reduced capacity, so the water demand was less affected. In contrast, the effect for Cities A and C was stronger because of the slower recovery of industrial activities.
Concerning public water demand, the general effect was also an average decrease, as Figure 5 and Table 3 present. A limitation appears in analyzing the main reasons for variations in public demand since this category considers diverse institutions and cannot be studied accurately. Nonetheless, the large drop can be associated with the closure of academic institutions, especially for City D, since it is considered a college town. For instance, the sustained reduced monthly consumption appreciated in Figure 5 for all cities could be related to a significant acceptance of remote studying. Further, the water demand variations are influenced by other institutional activities. In this way, a remarkable observation was the rise in consumption during the first months of the pandemic in City B, which might be due to increased procedures in hospitals and hygiene controls.
Finally, the net demand depends essentially on the proportion of domestic and non-domestic users [15]. As domestic water consumption is predominant in all the cities, the mild increase in the demand also implied a rise in the total demand. However, as occurred in Henderson, Nevada [8], Joinville, Brazil [9], and some Californian cities [10], this effect was neutralized due to larger commercial, industrial, and public water consumption changes. Therefore, total demand for the Colombian case studies had a small average demand change, as Table 3 shows.

3.4. Analysis of Spatial Variation

As people stopped consuming water in their workplaces and public spaces due to lockdowns, and performed their entire water-consuming practices within their homes, a portion of non-domestic demand was expected to relocate to residential households [11]. Based on the results, the fact that total water usage remained almost untouched allows hypothesizing that non-domestic consumption moved to households. Hence, information from water demand was analyzed by spatial zones to study this displacement of water consumption. Figure 6 is presented to show the distribution of the average variations in water demand in each of the zones in the case studies.
As Table 2 shows, the data from Cities B, C, and D are divided into multiple district metered areas that allow observing spatial differences. Nevertheless, City A records were unavailable in detailed geographic divisions, so the results are presented for only five administrative supply zones. These zones correspond to the areas the water utility operates for commercial management. Considering these data, Figure 6 illustrates water demand changes in each zone of the cities through the average relation between actual and expected non-pandemic consumption. Values over 1 correspond to zones where the demand increased and, conversely, where water usage decreased.
Concerning the domestic category, more than 50% of the zones showed a slight rise in water usage. In comparison, another significant proportion (40% in City A, 43% in City B, and 42% in City C) evidenced almost no change. In the case of City D, 62.5% of the zones had almost no change in water demand, and 30% had increased consumption. In Cities B, C, and D, where the size of the zones allows a better understanding of the results, the locations with the lowest proportion of residential users within the cities are where domestic consumption increased the most. Hence, the changes in each zone are related to the proportion of users from each socioeconomic strata, where demand increased in places having more low-strata customers.
Furthermore, left-skewed histograms in Figure 6 were obtained for almost all the alterations in non-domestic water demand patterns, indicating a decline in consumption in most of the zones. Specifically, commercial water consumption decreased abruptly in the first months of the pandemic in the areas with abundant commercial premises, but it started to return to normal levels in late 2020. However, in the zones with fewer commercial users, consumption remained low. In some areas, the demand was not affected at the beginning of the pandemic and progressively increased when restriction measures were relaxed, resulting in higher average consumption than expected. These findings are more appreciable in City C as the touristic destinations were more demanded in late 2020 when people were allowed to travel.
Regarding the industrial water demand, different affectations can be noticed in Figure 6 for each case study. As discussed before, the importance of industries generated a rapid consumption recovery in City B, represented by a high proportion of users with average unchanged water demand. An analogous observation was obtained for City D, with a high proportion of industrial average untouched demand. In contrast, the decreasing effect was more significant in Cities A and C, especially for zones with abundant industries. The consumption increased in zones having a small proportion of industrial users. As some utilities reported in the study conducted by Zechman et al. [1], this effect may have resulted from higher production of essential goods in those places because of commuting limitations, such as food and beverage manufacturing. Similarly, the effect on public water usage differed regarding the predominance of schools, universities, hospitals, and other institutions. For example, the consumption increased during July–August 2020 in zones with healthcare facilities for all case studies and January–February 2021 for Cities B, C, and D. Both terms coincide with periods where contagions of COVID-19 peaked in Colombia, represented in increased hospital water usage.
Finally, the total water consumption histograms in Figure 6 evidence the general pattern of the classified water demands. In City A, the increases in domestic consumption and reductions in non-domestic consumption balance the net changes completely in each zone. In Cities B, C, and D, most zones also show a net equilibrium between the decreases in non-domestic usage and the rises in domestic water usage. Nonetheless, for Cities B and C, a considerable proportion of zones presented an average increase in demand (41.1% and 44.5%, respectively). These zones correspond to the places with increased industrial and public demand, additional to the domestic rises. This effect in City D is balanced due to the non-increments in industrial demand and the predominance of academic institutions’ water usage drops for public water consumption.

3.5. Financial Impacts Estimation

Even though the net consumption changes are mostly counterbalanced between domestic and non-domestic demand in all cities, the relocation of water demand implies a shift in the type of user consuming water. In this way, income for utilities was significantly modified since tariffs are differentiated for the type of user in Colombia. Therefore, Figure 7 shows the difference between actual and expected revenues for every water demand category.
In general, the first two months of the pandemic in each case study showed positive net revenues. For Cities A, C, and D, this is due exclusively to the increased domestic water demand. In contrast, the increase in public water consumption in City B also generated higher income than predicted. Afterward, revenues suffered a reduction from the expected values because of the drop in non-domestic water consumption. The slight increases in domestic demand reduced the effect of the total water demand reduction; however, the outcomes showed net revenue losses in all case studies. This observation is closely related to that exposed by Nemati [15], who explained that cities with tiered or budget-based rate structures were the most affected by the pandemic. In Colombia, according to Decree 394 of 1987, the water tariff structure consists of a fixed charge for every billing period, and a volumetric charge for the water volume consumed during the same term [34]. Tariffs are defined for every type of user, and, additionally, residential customers are billed depending on their socioeconomic strata. Hence, water consumption billing differs for subsidizing Strata 1, 2, and 3 users, reaching discounts up to 20% of the total value. At the same time, Strata 5 and 6 are known as contributors, given that their users have higher rates for covering the excess costs [20]. Strata 4 is the only one that is neither subsidized nor a contributor.
Thus, the non-domestic and the high-strata domestic water tariffs are the highest. These users are less abundant among utilities’ customers, meaning that water consumption changes have rapid implications for water companies’ income. In this way, since results revealed that these users diminished their normal water usage during the COVID-19 pandemic, the water utilities’ revenues were affected accordingly. Table 4 presents the results showing this pandemic’s effect on Colombian water utilities. The simple mean water demand average changes for subsidized and contributor users are presented, in addition to the net revenue losses obtained as the addition of the difference between actual and expected revenues shown in Figure 7. The subsidized users show an increasing trend, while contributors present a decrease for all the cities. In addition, the losses are also shown as the corresponding percentage of the expected income to assess the size of the financial impact on water companies. City C was the most affected case study because of its touristic features, and City B experienced the most rapid recovery due to the prioritization of industrial production activities.
Further, these losses are reinforced by non-payment and billing exceptions, so the financial effect on water utilities was certainly worse. Alterations in water utilities’ finances may have severe effects on the planning of water supply systems, even forcing utilities to cut or delay investments in infrastructure and operational aspects. Therefore, studying water demand changes is crucial for water utilities, and a re-definition of the water rates structure may even be required.

4. Conclusions

In the COVID-19 pandemic context, drinking water availability is essential for ensuring human health and reducing the spread of the virus. The COVID-19 pandemic has undeniably affected the regular behavior of people and communities in many aspects, including water usage practices. An increase in domestic water consumption and a drastic decrease in non-domestic water consumption has been witnessed in multiple cities worldwide. Correspondingly, results from this research evidenced consistent outcomes for four Colombian cities. However, outcomes revealed that specific water demand variations highly depend on the proportion of domestic and non-domestic water usage and the predominant economic sector within a city. In this way, from the case studies of this research, the touristic city was the most affected due to a severe drop in commercial water use. In contrast, the industrial case studies experienced a rapid recovery in water demand because industrial activities were prioritized for the return from stay-at-home orders to normal conditions. Evidently, these changes in water demand also affected wastewater production. For this study, it was not possible to analyze wastewater volumes because all sewer systems in the case studies are combined. Therefore, for cities with combined systems, it is not possible to estimate the effect on wastewater volumes and the corresponding relation with the events modifying drinking water patterns.
Furthermore, the outcomes from this research proved that utilities’ finances are vulnerable to water demand variations, possibly altering investment plans to improve the infrastructure and operations of water supply systems. For Colombian cities, where the tariff structure is based on budget—domestic demand is partially subsidized and non-domestic users have the highest rates—the changes in water demand triggered significant economic losses. Hence, differentiated tariffs diminish the commercial resilience of water utilities and make them more vulnerable to unexpected events, such as the pandemic. In this way, evaluating changes in water demand is crucial so that a continuous and safe water supply service can be secured. To adapt the infrastructure and utilities’ investment plans to changing water demand patterns, evaluating the persistence of water consumption alterations is necessary. Therefore, later investigations should focus on predicting future water demand patterns, and considering the pandemic-driven water demand modification experience, in addition to other unexpected events that might risk the resilience of the water supply systems.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/w14193004/s1, Table S1: Regression model explanatory variables’ results for domestic water consumption (Strata 1). Table S2: Regression model explanatory variables’ results for domestic water consumption (Strata 2). Table S3: Regression model explanatory variables’ results for domestic water consumption (Strata 3). Table S4: Regression model explanatory variables’ results for domestic water consumption (Strata 4). Table S5: Regression model explanatory variables’ results for domestic water consumption (Strata 5). Table S6: Regression model explanatory variables’ results for domestic water consumption (Strata 6). Table S7: Regression model explanatory variables’ results for commercial water consumption. Table S8: Regression model explanatory variables’ results for industrial water consumption. Table S9: Regression model explanatory variables’ results for public water consumption. Table S10: Regression model explanatory variables’ results for total water consumption.

Author Contributions

Conceptualization, C.O. and J.S.; methodology, C.O.; software, C.O.; validation, C.O., C.S. and J.S.; formal analysis, C.O., C.S. and J.S.; investigation, C.O., C.S. and J.S.; data curation, C.O.; writing—original draft preparation, C.O and C.S.; writing—review and editing, C.S. and J.S.; visualization, C.O.; supervision, J.S. and C.S.; project administration, J.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy issues from water utilities.

Acknowledgments

The authors would like to thank the water utilities of the four case studies for providing access to water use data, and especially Andrés Fernández and collaborators from Aguas de Cartagena, to Daniel Giraldo, Claudia Vargas, and contributors from Aguas de Manizales, and the Empresa de Acueducto y Alcantarillado de Bogotá operators.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Berglund, E.Z.; Buchberger, S.; Cunha, M.; Faust, K.M.; Giacomoni, M.; Goharian, E.; Kleiner, Y.; Lee, J.; Ostfeld, A.; Pasha, F.; et al. Effects of the COVID-19 Pandemic on Water Utility Operations and Vulnerability. J. Water Resour. Plan. Manag. 2022, 148, 04022027. [Google Scholar] [CrossRef]
  2. Spearing, L.A.; Thelemaque, N.; Kaminsky, J.A.; Katz, L.E.; Kinney, K.A.; Kirisits, M.J.; Sela, L.; Faust, K.M. Implications of Social Distancing Policies on Drinking Water Infrastructure: An Overview of the Challenges to and Responses of U.S. Utilities during the COVID-19 Pandemic. ACS ES&T Water 2020, 1, 888–899. [Google Scholar] [CrossRef]
  3. AWWA Management and Leadership Division. 2020: Looking Back on the Year of COVID. J. Am. Water Work. Assoc. 2021, 113, 60–65. [Google Scholar] [CrossRef] [PubMed]
  4. 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. [Google Scholar] [CrossRef]
  5. Abu-Bakar, H.; Williams, L.; Hallett, S.H. Quantifying the impact of the COVID-19 lockdown on household water consumption patterns in England. NPJ Clean Water 2021, 4, 13. [Google Scholar] [CrossRef]
  6. Lüdtke, D.U.; Luetkemeier, R.; Schneemann, M.; Liehr, S. Increase in Daily Household Water Demand during the First Wave of the COVID-19 Pandemic in Germany. Water 2021, 13, 260. [Google Scholar] [CrossRef]
  7. Balacco, G.; Totaro, V.; Iacobellis, V.; Manni, A.; Spagnoletta, M.; Piccinni, A. Influence of COVID-19 Spread on Water Drinking Demand: The Case of Puglia Region (Southern Italy). Sustainability 2020, 12, 5919. [Google Scholar] [CrossRef]
  8. Irwin, N.B.; McCoy, S.J.; McDonough, I.K. Water in the time of corona(virus): The effect of stay-at-home orders on water demand in the desert. J. Environ. Econ. Manag. 2021, 109, 102491. [Google Scholar] [CrossRef] [PubMed]
  9. Kalbusch, A.; Henning, E.; Brikalski, M.P.; de Luca, F.V.; Konrath, A.C. Impact of coronavirus (COVID-19) spread-prevention actions on urban water consumption. Resour. Conserv. Recycl. 2020, 163, 105098. [Google Scholar] [CrossRef] [PubMed]
  10. Li, D.; Engel, R.A.; Ma, X.; Porse, E.; Kaplan, J.D.; Margulis, S.A.; Lettenmaier, D.P. Stay-at-Home Orders during the COVID-19 Pandemic Reduced Urban Water Use. Environ. Sci. Technol. Lett. 2021, 8, 431–436. [Google Scholar] [CrossRef]
  11. Sowby, R.B.; Lunstad, N.T. Considerations for Studying the Impacts of COVID-19 and Other Complex Hazards on Drinking Water Systems. J. Infrastruct. Syst. 2021, 27, 02521002. [Google Scholar] [CrossRef]
  12. Cooley, H.; Gleick, P.; Abraham, S.; Cai, W. Water and the COVID-19 Pandemic: Impacts on Municipal Water Demand; Pacific Institute: Oakland, CA, USA, 2020. [Google Scholar]
  13. Butler, G.; Pilotto, R.G.; Hong, Y.; Mutambatsere, E. The Impact of COVID-19 on the Water and Sanitation Sector; International Finance Corporation—IFC: Washington, DC, USA, 2020. [Google Scholar]
  14. Water, Sanitation, Hygiene, and Waste Management for SARS-CoV-2, the Virus that Causes COVID-19: Interim Guidance. Available online: https://www.who.int/publications/i/item/WHO-2019-nCoV-IPC-WASH-2020.4 (accessed on 27 June 2022).
  15. Nemati, M. COVID-19 and Urban Water Consumption. University of California, Giannini Foundation of Agricultural Economics. ARE Update 2020, 24, 9–11. [Google Scholar]
  16. Actions Taken by the Government: Preventive Isolation. Available online: https://coronaviruscolombia.gov.co/Covid19/acciones/acciones-de-aislamiento-preventivo.html (accessed on 27 June 2022).
  17. Actions Taken by the Government: Selective Isolation. Available online: https://coronaviruscolombia.gov.co/Covid19/acciones/acciones-de-aislamiento-selectivo.html (accessed on 27 June 2022).
  18. Decree 580 of 2021. Available online: https://www.minsalud.gov.co/Normatividad_Nuevo/Decreto%20580%20de%202021.pdf (accessed on 27 June 2022).
  19. DANE. Population Projections at the Municipal Level. Period 2018–2035; National Statistics Agency: Bogotá, DC, Colombia, 2018.
  20. Law 142 of 1994. Available online: http://www.secretariasenado.gov.co/senado/basedoc/ley_0142_1994.html (accessed on 27 June 2022).
  21. DANE. Urban Socioeconomic Stratification Methodology for Residential Public Services; National Statistics Agency: Bogotá, DC, Colombia, 2015.
  22. CRA. Resolution CRA 151; Commission for the Regulation of Drinking Water and Basic Sanitation: Bogotá, DC, Colombia, 2001. [Google Scholar]
  23. Migratory Flows Tableau. Available online: https://www.migracioncolombia.gov.co/planeacion/estadisticas/content/223-tableau (accessed on 27 June 2022).
  24. IDEAM Consulta y Descarga de Datos Hidrometeorológicos. Available online: http://dhime.ideam.gov.co/atencionciudadano/ (accessed on 10 October 2020).
  25. MATLAB Version 9.9.0 (R2020b); The MathWorks Inc.: Natick, MA, USA, 2020.
  26. Li, D. Impacts of Behavioral Changes Associated with the COVID-19 Stay-at-Home Orders On Urban Water Use. Available online: https://doi.org/10.1021/acs.estlett.0c00979 (accessed on 27 June 2022).
  27. Bich-Ngoc, N.; Teller, J. A review of residential water consumption determinants. In Computational Science and Its Applications—ICCSA 2018; Lecture Notes in Computer Science; Springer: Berlin/Heidelberg, Germany, 2018; pp. 685–696. [Google Scholar]
  28. Bich-Ngoc, N.; Teller, J. Potential Effects of the COVID-19 Pandemic through Changes in Outbound Tourism on Water Demand: The Case of Liège (Belgium). Water 2020, 12, 2820. [Google Scholar] [CrossRef]
  29. Actions Taken by the Government: Public Services. Available online: https://coronaviruscolombia.gov.co/Covid19/acciones/acciones-de-servicios-publicos.html (accessed on 27 June 2022).
  30. Berglund, E.Z.; Thelemaque, N.; Spearing, L.; Faust, K.M.; Kaminsky, J.; Sela, L.; Goharian, E.; Abokifa, A.; Lee, J.; Keck, J.; et al. Water and Wastewater Systems and Utilities: Challenges and Opportunities during the COVID-19 Pandemic. J. Water Resour. Plan. Manag. 2021, 147, 02521001. [Google Scholar] [CrossRef]
  31. Eastman, L.; Smull, E.; Patterson, L.; Doyle, M. COVID-19 Impacts on Water Utility Consumption and Revenues: Preliminary Results; Rafteils/Duke University: Durham, NC, USA, 2020. [Google Scholar]
  32. Worthington, A. Commercial and Industrial Water Demand Estimation: Theoretical and Methodological Guidelines for Applied Economics Research; Griffith University: Queensland, Australia, 2010. [Google Scholar]
  33. Whitaker, S.D. Did the COVID-19 Pandemic Cause an Urban Exodus? Cfed Dist. Data Briefs 2021. [Google Scholar] [CrossRef]
  34. Decree 394 of 1987. Available online: http://www.suin-juriscol.gov.co/viewDocument.asp?id=1088443 (accessed on 27 June 2022).
Figure 1. Research methodology scheme.
Figure 1. Research methodology scheme.
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Figure 2. Distribution of water consumption for domestic, commercial, industrial, and public categories in the four case studies.
Figure 2. Distribution of water consumption for domestic, commercial, industrial, and public categories in the four case studies.
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Figure 3. Actual water consumption (Reported) and model predictions (Modeled) by water demand category for Cities A and B. Actual water consumption (Reported) and model predictions (Modeled) by water demand category for Cities C and D (in cubic meters). The multivariate regression model’s accuracy is shown through pre-pandemic data (before black vertical lines) and the corresponding R-squared values. Pandemic data and predictions are presented after the black vertical lines.
Figure 3. Actual water consumption (Reported) and model predictions (Modeled) by water demand category for Cities A and B. Actual water consumption (Reported) and model predictions (Modeled) by water demand category for Cities C and D (in cubic meters). The multivariate regression model’s accuracy is shown through pre-pandemic data (before black vertical lines) and the corresponding R-squared values. Pandemic data and predictions are presented after the black vertical lines.
Water 14 03004 g003aWater 14 03004 g003b
Figure 4. Difference between normal and estimated COVID-19 pandemic domestic water consumption by socioeconomic strata.
Figure 4. Difference between normal and estimated COVID-19 pandemic domestic water consumption by socioeconomic strata.
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Figure 5. Difference between normal and estimated COVID-19 pandemic water consumption for domestic, commercial, industrial, public, and total categories.
Figure 5. Difference between normal and estimated COVID-19 pandemic water consumption for domestic, commercial, industrial, public, and total categories.
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Figure 6. Comparison histograms of the water consumption changes due to the COVID-19 pandemic by geographic divisions within each case study. The 0.9–1.05 range, presented in a darker tone, represents zones where the consumption was nearly the same between normal and pandemic-affected data.
Figure 6. Comparison histograms of the water consumption changes due to the COVID-19 pandemic by geographic divisions within each case study. The 0.9–1.05 range, presented in a darker tone, represents zones where the consumption was nearly the same between normal and pandemic-affected data.
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Figure 7. Difference between actual and expected revenues for domestic, commercial, industrial, and public water consumption in each case study. The left axis shows the revenues in millions of Colombian pesos and the right axis in thousands of US dollars. Commas are used for indicating thousands and periods for decimals.
Figure 7. Difference between actual and expected revenues for domestic, commercial, industrial, and public water consumption in each case study. The left axis shows the revenues in millions of Colombian pesos and the right axis in thousands of US dollars. Commas are used for indicating thousands and periods for decimals.
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Table 1. Case study information summary.
Table 1. Case study information summary.
Case StudyAreaUrban Area InhabitantsAverage Number of Users
City A1775 km27,804,6602,219,262
City B1157 km23,924,4961,150,104
City C709 km2926,747220,822
City D571.8 km2425,181104,694
Table 2. Summary of the data used for the analysis.
Table 2. Summary of the data used for the analysis.
DataCase StudyTime ScaleTime PeriodSpatial ScaleSource
Water supply
Water volumes and number of usersCity ABimonthly01/2016–10/20205 administrative supply zonesWater utility
City BMonthly01/2016–04/202190 district metered areas
City CMonthly01/2016–04/2021197 district metered areas
City DMonthly07/2016–04/202140 district metered areas
Tariffs (fixed and volumetric rate)City ABimonthly01/2016–12/2020Entire service area
City BMonthly01/2016–04/2021Municipalities
City CMonthly01/2016–04/2021Entire service area
City DMonthly07/2016–04/2021Entire service area
Socioeconomics
Commuting Population Incoming and OutgoingCity AMonthly01/2016–10/2020Closest migratory
controls
Ministry of
Foreign Affairs
City B01/2016–04/2021
City C01/2016–04/2021
Weather
Maximum
Temperature
City AMonthly01/2016–10/20206 meteorological stationsIDEAM
City B01/2016–04/20213 meteorological stations
City C01/2016–04/20211 meteorological station
City D07/2016–04/20212 meteorological stations
PrecipitationCity AMonthly01/2016–10/202010 meteorological stations
City B01/2016–04/20219 meteorological stations
City C01/2016–04/20213 meteorological stations
City D07/2016–04/20213 meteorological stations
Table 3. Average water demand changes by case study.
Table 3. Average water demand changes by case study.
Case StudyTime FrameAverage Water Demand Change
DomesticCommercialIndustrialPublicTotal
City AMarch–October 20206%−38%−25%−21%−2%
City BMarch 2020–April 20213%−23%−11%−13%−2%
City C7%−32%−20%−24%−2%
City D3%−27%−14%−21%−2%
Table 4. Average water demand change for subsidized and contributor users, and the corresponding net revenue losses for water utilities in every case study. Subsidized users correspond to Strata 1, 2, and 3 domestic users, and contributors are commercial, industrial, public, and Strata 5 and 6 domestic users.
Table 4. Average water demand change for subsidized and contributor users, and the corresponding net revenue losses for water utilities in every case study. Subsidized users correspond to Strata 1, 2, and 3 domestic users, and contributors are commercial, industrial, public, and Strata 5 and 6 domestic users.
Case StudyAverage Water Demand ChangeNet Revenue Losses
Subsidized UsersContributor UsersThousand USDPercentage of Expected Income
City A4%−15%$5472.13.7%
City B7%−11%$4383.52.4%
City C7%−18%$2238.76.4%
City D4%−14%$514.63.7%
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Ortiz, C.; Salcedo, C.; Saldarriaga, J. Assessment of the Effects of COVID-19 Pandemic Stay-at-Home Measures on Potable Water Consumption Patterns, Location, and Financial Impacts for Water Utilities in Colombian Cities. Water 2022, 14, 3004. https://doi.org/10.3390/w14193004

AMA Style

Ortiz C, Salcedo C, Saldarriaga J. Assessment of the Effects of COVID-19 Pandemic Stay-at-Home Measures on Potable Water Consumption Patterns, Location, and Financial Impacts for Water Utilities in Colombian Cities. Water. 2022; 14(19):3004. https://doi.org/10.3390/w14193004

Chicago/Turabian Style

Ortiz, Catalina, Camilo Salcedo, and Juan Saldarriaga. 2022. "Assessment of the Effects of COVID-19 Pandemic Stay-at-Home Measures on Potable Water Consumption Patterns, Location, and Financial Impacts for Water Utilities in Colombian Cities" Water 14, no. 19: 3004. https://doi.org/10.3390/w14193004

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