Changes in water demand resulting from pandemic mitigations in Southeast Michigan

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Abstract

The COVID‐19 pandemic impacted many dimensions of daily life; including how and where people used water. These changes have important implications for water management and utility planning. This study quantifies regional water demand changes across 75 utilities in Southeast Michigan throughout 2020. Deviations were not uniform, with both increases and decreases noted in indoor base flow and average day demands. Effects on diurnal patterns included peak use decreasing but occurring later in the morning and lasting longer. Utility demand changes are statistically correlated to community characteristics of income, population density, job density, and daytime population as well as altered movement trends in response to shutdowns. Commuter communities and those with employment centers experienced greater demand deviations. Understanding the socio‐demographic factors behind the changes will help determine which patterns will be permanent or prevail post‐pandemic. Utilities will be able to better plan for water supplies, capital improvements, operations, and finances.

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Most cited references 19

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Principal component analysis: a review and recent developments.

Ian Jolliffe, Jorge Cadima (2016)

Large datasets are increasingly common and are often difficult to interpret. Principal component analysis (PCA) is a technique for reducing the dimensionality of such datasets, increasing interpretability but at the same time minimizing information loss. It does so by creating new uncorrelated variables that successively maximize variance. Finding such new variables, the principal components, reduces to solving an eigenvalue/eigenvector problem, and the new variables are defined by the dataset at hand, not a priori, hence making PCA an adaptive data analysis technique. It is adaptive in another sense too, since variants of the technique have been developed that are tailored to various different data types and structures. This article will begin by introducing the basic ideas of PCA, discussing what it can and cannot do. It will then describe some variants of PCA and their application.

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Influence of COVID-19 Spread on Water Drinking Demand: The Case of Puglia Region (Southern Italy)

Gabriella Balacco, Vincenzo Totaro, Vito Iacobellis … (2020)

The COVID-19 pandemic affected the lives of millions of people, radically changing their habits in just a few days. In many countries, containment measures prescribed by national governments restricted the movements of entire communities, with the impossibility of attending schools, universities, workplaces, and no longer allowing for traveling or leading a normal social life. People were then compelled to revise their habits and lifestyles. In such a situation, the availability of drinking water plays a crucial role in ensuring adequate health conditions for people and tackling the spread of the pandemic. Lifestyle of the population, climate, water scarcity and water price are influent factors on water drinking demand and its daily pattern. To analyze the effect of restriction measures on water demand, the instantaneous flow data of five Apulian towns (Italy) during the lockdown have been analyzed highlighting the important role of users’ habits and the not negligible effect of commuters on the water demand pattern besides daily volume requested.

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Quantifying the impact of the COVID-19 lockdown on household water consumption patterns in England

Halidu Abu-Bakar, Leon Williams, Stephen Hallett (2021)

The COVID-19 lockdown has instigated significant changes in household behaviours across a variety of categories including water consumption, which in the south and east regions of England is at an all-time high. We analysed water consumption data from 11,528 households over 20 weeks from January 2020, revealing clusters of households with distinctive temporal patterns. We present a data-driven household water consumer segmentation characterising households’ unique consumption patterns and we demonstrate how the understanding of the impact of these patterns of behaviour on network demand during the COVID-19 pandemic lockdown can improve the accuracy of demand forecasting. Our results highlight those groupings with the highest and lowest impact on water demand across the network, revealing a significant quantifiable change in water consumption patterns during the COVID-19 lockdown period. The implications of the study to urban water demand forecasting strategies are discussed, along with proposed future research directions.