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Journal articles on the topic 'Residential water demand'

Author: Grafiati

Published: 4 June 2021

Last updated: 20 February 2023

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1

Tricarico, C., G. de Marinis, R. Gargano, and A. Leopardi. "Peak residential water demand." Proceedings of the Institution of Civil Engineers - Water Management 160, no. 2 (June 2007): 115–21. http://dx.doi.org/10.1680/wama.2007.160.2.115.

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2

Bao, Keyu, Rushikesh Padsala, Daniela Thrän, and Bastian Schröter. "Urban Water Demand Simulation in Residential and Non-Residential Buildings Based on a CityGML Data Model." ISPRS International Journal of Geo-Information 9, no. 11 (October 28, 2020): 642. http://dx.doi.org/10.3390/ijgi9110642.

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Humans’ activities in urban areas put a strain on local water resources. This paper introduces a method to accurately simulate the stress urban water demand in Germany puts on local resources on a single-building level, and scalable to regional levels without loss of detail. The method integrates building geometry, building physics, census, socio-economy and meteorological information to provide a general approach to assessing water demands that also overcome obstacles on data aggregation and processing imposed by data privacy guidelines. Three German counties were used as validation cases to

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3

Palencia, Lamberto C. "RESIDENTIAL WATER DEMAND IN METRO MANILA." Journal of the American Water Resources Association 24, no. 2 (April 1988): 275–79. http://dx.doi.org/10.1111/j.1752-1688.1988.tb02984.x.

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4

Lopez-Mayan, Cristina. "Microeconometric Analysis of Residential Water Demand." Environmental and Resource Economics 59, no. 1 (September 5, 2013): 137–66. http://dx.doi.org/10.1007/s10640-013-9721-4.

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5

Gato, Shirley, Niranjali Jayasuriya, and Peter Roberts. "Forecasting Residential Water Demand: Case Study." Journal of Water Resources Planning and Management 133, no. 4 (July 2007): 309–19. http://dx.doi.org/10.1061/(asce)0733-9496(2007)133:4(309).

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6

Hung, Ming-Feng, Bin-Tzong Chie, and Tai-Hsin Huang. "Residential water demand and water waste in Taiwan." Environmental Economics and Policy Studies 19, no. 2 (April 13, 2016): 249–68. http://dx.doi.org/10.1007/s10018-016-0154-5.

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7

Metaxas, S., and E. Charalambous. "Residential price elasticity of demand for water." Water Supply 5, no. 6 (December 1, 2005): 183–88. http://dx.doi.org/10.2166/ws.2005.0063.

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This paper presents an analysis on price elasticity of demand for water as a consequence of price increases. The objective of this research study is to estimate the residential price elasticities of demand for water for different regions, which may have different income levels. The general conclusion is that price elasticity for residential water use is inelastic (i.e. a given percentage of price increase results in a proportionally smaller decrease in quantity demanded) and it varies by consumer class and type of water use. The elasticity is not significantly affected by demographic and other

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8

Lyman, R. Ashley. "Peak and off-peak residential water demand." Water Resources Research 28, no. 9 (September 1992): 2159–67. http://dx.doi.org/10.1029/92wr01082.

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9

Schleich, Joachim, and Thomas Hillenbrand. "Determinants of residential water demand in Germany." Ecological Economics 68, no. 6 (April 2009): 1756–69. http://dx.doi.org/10.1016/j.ecolecon.2008.11.012.

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10

Gargano, Rudy, Carla Tricarico, Giuseppe del Giudice, and Francesco Granata. "A stochastic model for daily residential water demand." Water Supply 16, no. 6 (June 20, 2016): 1753–67. http://dx.doi.org/10.2166/ws.2016.102.

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Residential water demand is a random variable which influences greatly the performance of municipal water distribution systems (WDSs). The water request at network nodes reflects the behavior of the residential users, and a proper characterization of their water use habits is vital for the hydraulic system modeling. This study presents a stochastic approach for the characterization of the daily residential water use. The proposed methodology considers a unique probabilistic distribution – mixed distribution – for any time during the day, and thus for any entity of the water demanded by the use

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11

Suárez-Varela, Marta. "Modeling residential water demand: An approach based on household demand systems." Journal of Environmental Management 261 (May 2020): 109921. http://dx.doi.org/10.1016/j.jenvman.2019.109921.

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12

Ghimire, Monika, Tracy A. Boyer, Chanjin Chung, and Justin Q. Moss. "Estimation of Residential Water Demand under Uniform Volumetric Water Pricing." Journal of Water Resources Planning and Management 142, no. 2 (February 2016): 04015054. http://dx.doi.org/10.1061/(asce)wr.1943-5452.0000580.

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13

Zapata, Oscar. "More Water Please, It's Getting Hot! The Effect of Climate on Residential Water Demand." Water Economics and Policy 01, no. 03 (September 2015): 1550007. http://dx.doi.org/10.1142/s2382624x15500071.

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Climate change is expected to alter the supply and demand for water in the residential sector. Existing studies exploit the differences in climate across seasons mostly in North America and Europe, and identify changes in consumption levels attributed only to households' short-term responses. The results from models that simulate household consumption of water are sensitive to the parameters that govern the behavior of climate variables and household responses in the upcoming decades, and fail to consider short-term determinants of water consumption. The findings in the literature suggest an i

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14

Lombardi, Francesco, Guglielmo Silvagni, Piero Sirini, Riccardo Spagnuolo, and Fabio Volpi. "Daily water demand." Ambiente e Agua - An Interdisciplinary Journal of Applied Science 13, no. 5 (October 1, 2018): 1. http://dx.doi.org/10.4136/ambi-agua.2239.

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This paper develops a model to characterize the demand for domestic water based on its end users' usage habits. The use of individual residential appliances (bathroom sink, toilet, shower, bath, etc.) is interpreted using a probabilistic approach. The paper also applies the model to the distribution network of the municipality of Sparanise, a small city in the province of Caserta, Italy. The results of this application are compared to the real output of the city's actual water reservoir. Flow variability during the day was successfully modelled. A comparison of the simulated and recorded data

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15

Al-Mohannadi, Hassan I., Chris O. Hunt, and Adrian P. Wood. "Controlling Residential Water Demand in Qatar: An Assessment." AMBIO: A Journal of the Human Environment 32, no. 5 (August 2003): 362–66. http://dx.doi.org/10.1579/0044-7447-32.5.362.

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16

Rathnayaka, Kumudu, Hector Malano, Shiroma Maheepala, Biju George, Bandara Nawarathna, Meenakshi Arora, and Peter Roberts. "Seasonal Demand Dynamics of Residential Water End-Uses." Water 7, no. 12 (January 7, 2015): 202–16. http://dx.doi.org/10.3390/w7010202.

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17

Ali, Elhadi Ramadan, Mabroka Mohamed Daw, and Mohd Ekhwan Toriman. "Determinants of urban residential water demand in Libya." International Journal of Innovation and Sustainable Development 15, no. 3 (2021): 261. http://dx.doi.org/10.1504/ijisd.2021.10038213.

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18

Daw, Mabroka Mohamed, Elhadi Ramadan Ali, and Mohd Ekhwan Toriman. "Determinants of urban residential water demand in Libya." International Journal of Innovation and Sustainable Development 15, no. 3 (2021): 261. http://dx.doi.org/10.1504/ijisd.2021.115963.

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19

Lee, Juneseok, and Stephanie A. Tanverakul. "Price elasticity of residential water demand in California." Journal of Water Supply: Research and Technology-Aqua 64, no. 2 (September 24, 2014): 211–18. http://dx.doi.org/10.2166/aqua.2014.082.

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20

Worthington, Andrew C., and Mark Hoffman. "AN EMPIRICAL SURVEY OF RESIDENTIAL WATER DEMAND MODELLING." Journal of Economic Surveys 22, no. 5 (July 24, 2008): 842–71. http://dx.doi.org/10.1111/j.1467-6419.2008.00551.x.

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21

Stevens, T. H., Jonathan Miller, and Cleve Willis. "EFFECT OF PRICE STRUCTURE ON RESIDENTIAL WATER DEMAND." Journal of the American Water Resources Association 28, no. 4 (August 1992): 681–85. http://dx.doi.org/10.1111/j.1752-1688.1992.tb01489.x.

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22

Kenney, Douglas S., Christopher Goemans, Roberta Klein, Jessica Lowrey, and Kevin Reidy. "Residential Water Demand Management: Lessons from Aurora, Colorado1." JAWRA Journal of the American Water Resources Association 44, no. 1 (February 2008): 192–207. http://dx.doi.org/10.1111/j.1752-1688.2007.00147.x.

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23

Gaudin, S. "Effect of price information on residential water demand." Applied Economics 38, no. 4 (March 10, 2006): 383–93. http://dx.doi.org/10.1080/00036840500397499.

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24

Kotagama, Hemesiri, Slim Zekri, Rahma Al Harthi, and Houcine Boughanmi. "Demand function estimate for residential water in Oman." International Journal of Water Resources Development 33, no. 6 (October 6, 2016): 907–16. http://dx.doi.org/10.1080/07900627.2016.1238342.

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25

Polebitski, Austin S., and Richard N. Palmer. "Seasonal Residential Water Demand Forecasting for Census Tracts." Journal of Water Resources Planning and Management 136, no. 1 (January 2010): 27–36. http://dx.doi.org/10.1061/(asce)wr.1943-5452.0000003.

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26

Sebri, Maamar. "A meta-analysis of residential water demand studies." Environment, Development and Sustainability 16, no. 3 (September 29, 2013): 499–520. http://dx.doi.org/10.1007/s10668-013-9490-9.

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27

Custódio and Ghisi. "Assessing the Potential for Potable Water Savings in the Residential Sector of a City: A Case Study of Joinville City." Water 11, no. 10 (October 4, 2019): 2074. http://dx.doi.org/10.3390/w11102074.

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The objective of this study is to evaluate the potential for potable water savings by using rainwater in the residential sector of Joinville, a city located in southern Brazil. Data on roof areas of residential buildings were obtained from the Joinville city council. By considering the roof areas and typologies of residential buildings, representative models were created. The following parameters were used to determine the rainwater tank capacity: the number of dwellers; the total daily water demand per capita; and the rainwater demand. To carry out the simulations for determining the optimal

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28

Alcocer Yamanaka, Víctor Hugo, and Velitchko G. Tzatchkov. "Neyman-Scott-based water distribution network modelling." Ingeniería e Investigación 32, no. 3 (September 1, 2012): 32–36. http://dx.doi.org/10.15446/ing.investig.v32n3.35937.

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Residential water demand is one of the most difficult parameters to determine when modelling drinking water distribution networks. It has been proven to be a stochastic process which can be characterised as a series of rectangular pulses having set intensity, duration and frequency. Such parameters can be determined using stochastic models such as the Neyman-Scott rectangular pulse model (NSRPM). NSRPM is based on resolving a non-linear optimisation problem involving theoretical moments of the synthetic demand series (equiprobable) and of the observed moments (field measurements) statistically

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29

Lavee, D., Y. Danieli, G. Beniad, T. Shvartzman, and T. Ash. "Examining the effectiveness of residential water demand-side management policies in Israel." Water Policy 15, no. 4 (March 26, 2013): 585–97. http://dx.doi.org/10.2166/wp.2013.146.

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Increasing global water shortage is enhancing the need for water management policies, such as water demand policies. This study presents the main water demand-side management policies implemented in Israel, designed to reduce water demand in the urban sector, and subsequently examines their effectiveness by an econometric model, based on residential water consumption data. The main findings indicate that, among the economic policy tools, a smooth increase of water tariffs was not effective, while a drought surcharge led to a significant reduction in residential water demand. Educational policy

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30

García-Valiñas, María Ángeles, and Sara Suárez-Fernández. "Are Economic Tools Useful to Manage Residential Water Demand? A Review of Old Issues and Emerging Topics." Water 14, no. 16 (August 18, 2022): 2536. http://dx.doi.org/10.3390/w14162536.

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The analysis of residential water demand has long attracted attention from researchers. However, the central topics at issue have evolved considerably, transitioning from estimating price and income elasticities to using experimental techniques that assess how to motivate households towards water conservation. In this literature review, we contribute to the existing literature by giving an updated overview of the state of the art in the central topics regarding residential water demand. Moreover, we present some interesting lines of research to be explored in the future. Thus, we first review

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31

Baerenklau, K. A., K. A. Schwabe, and A. Dinar. "The Residential Water Demand Effect of Increasing Block Rate Water Budgets." Land Economics 90, no. 4 (October 3, 2014): 683–99. http://dx.doi.org/10.3368/le.90.4.683.

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32

Hansen, Lars Garn. "Water and Energy Price Impacts on Residential Water Demand in Copenhagen." Land Economics 72, no. 1 (February 1996): 66. http://dx.doi.org/10.2307/3147158.

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33

Rauf, Tamkinat, and M. Wasif Siddiqi. "Price-setting for Residential Water: Estimation of Water Demand in Lahore." Pakistan Development Review 47, no. 4II (December 1, 2008): 893–906. http://dx.doi.org/10.30541/v47i4iipp.893-906.

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The population of Lahore has roughly doubled over the past twenty years, and an increase of two million is expected by the year 2020 [UN (2005)]. This has important implications for city planning as demand for housing, electricity, water, sanitation, public health, education, and infrastructure grows accordingly. Water and Sanitation Agency (WASA), the city’s official water supplier, has often responded to the growing demand by offering the supply-side solution: augmenting supply capacity by exploiting new water resources.1 Such investments are costly, but in view of the public good nature of

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34

Houk, Eric E. "Estimating residential water demand in the absence of volumetric water pricing." Global Business and Economics Review 12, no. 3 (2010): 196. http://dx.doi.org/10.1504/gber.2010.034893.

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35

Martin, Randolph C., and Ronald P. Wilder. "Residential Demand for Water and the Pricing of Municipal Water Services." Public Finance Quarterly 20, no. 1 (January 1992): 93–102. http://dx.doi.org/10.1177/109114219202000106.

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36

Zhang, B., K. H. Fang, and K. A. Baerenklau. "Have C hinese water pricing reforms reduced urban residential water demand?" Water Resources Research 53, no. 6 (June 2017): 5057–69. http://dx.doi.org/10.1002/2017wr020463.

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37

Pan, Wenxiang, Baodeng Hou, Ruixiang Yang, Xuzhu Zhan, Wenkai Tian, Baoqi Li, Weihua Xiao, et al. "Conceptual Framework and Computational Research of Hierarchical Residential Household Water Demand." Water 10, no. 6 (May 27, 2018): 696. http://dx.doi.org/10.3390/w10060696.

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Although the quantity of household water consumption does not account for a huge proportion of the total water consumption amidst socioeconomic development, there has been a steadily increasing trend due to population growth and improved urbanization standards. As such, mastering the mechanisms of household water demand, scientifically predicting trends of household water demand, and implementing reasonable control measures are key focuses of current urban water management. Based on the categorization and characteristic analysis of household water, this paper used Maslow’s Hierarchy of Needs t

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38

Reynaud, Arnaud, and Giulia Romano. "Advances in the Economic Analysis of Residential Water Use: An Introduction." Water 10, no. 9 (August 30, 2018): 1162. http://dx.doi.org/10.3390/w10091162.

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The aim of this Special Issue is to gather evidence on the impact of price policies (PP) and non-price policies (NPP) in shaping residential water use in a context of increased water scarcity. Indeed, a large body of the empirical economic literature on residential water demand has been devoted to measuring the impact of PP (water price increases, use of block rate pricing or peak pricing, etc.). The consensus is that the residential water demand is inelastic with respect to water price, but not perfectly. Given the low water price elasticity, pricing schemes may not always be effective tools

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39

Pieterse-Quirijns, E. J., E. J. M. Blokker, E. van der Blom, and J. H. G. Vreeburg. "Non-residential water demand model validated with extensive measurements." Drinking Water Engineering and Science Discussions 5, no. 1 (August 24, 2012): 455–71. http://dx.doi.org/10.5194/dwesd-5-455-2012.

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Abstract. Existing guidelines related to the water demand of non-residential buildings are outdated and do not cover hot water demand for the appropriate selection of hot water devices. Moreover, they generally overestimate peak demand values required for the design of an efficient and reliable water system. Recently, a procedure was developed based on the end-use model SIMDEUM® to derive design rules for peak demand values of both cold and hot water during various time steps for several types and sizes of non-residential buildings, i.e. offices, hotels and nursing homes. In this paper, the de

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40

Gutzler, David S., and Joshua S. Nims. "Interannual Variability of Water Demand and Summer Climate in Albuquerque, New Mexico." Journal of Applied Meteorology 44, no. 12 (December 1, 2005): 1777–87. http://dx.doi.org/10.1175/jam2298.1.

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Abstract The effects of interannual climate variability on water demand in Albuquerque, New Mexico, are assessed. This city provides an ideal setting for examining the effects of climate on urban water demand, because at present the municipal water supply is derived entirely from groundwater, making supply insensitive to short-term climate variability. There is little correlation between interannual variability of climate and total water demand—a result that is consistent with several previous studies. However, summertime residential demand, which composes about one-quarter of total annual dem

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41

Romano, Giulia, Nicola Salvati, and Andrea Guerrini. "Estimating the Determinants of Residential Water Demand in Italy." Water 6, no. 10 (September 30, 2014): 2929–45. http://dx.doi.org/10.3390/w6102929.

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42

Arbués, Fernando, Inmaculada Villanúa, and Ramón Barberán. "Household size and residential water demand: an empirical approach." Australian Journal of Agricultural and Resource Economics 54, no. 1 (January 2010): 61–80. http://dx.doi.org/10.1111/j.1467-8489.2009.00479.x.

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43

Ghavidelfar, Saeed, Asaad Y. Shamseldin, and Bruce W. Melville. "Future implications of urban intensification on residential water demand." Journal of Environmental Planning and Management 60, no. 10 (December 6, 2016): 1809–24. http://dx.doi.org/10.1080/09640568.2016.1257976.

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44

Martin, William E., and John F. Thomas. "Policy relevance in studies of urban residential water demand." Water Resources Research 22, no. 13 (December 1986): 1735–41. http://dx.doi.org/10.1029/wr022i013p01735.

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45

Mieno, Taro, and John B. Braden. "Residential Demand for Water in the Chicago Metropolitan Area1." JAWRA Journal of the American Water Resources Association 47, no. 4 (April 11, 2011): 713–23. http://dx.doi.org/10.1111/j.1752-1688.2011.00536.x.

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46

Binet, Marie-Estelle, Fabrizio Carlevaro, and Michel Paul. "Estimation of Residential Water Demand with Imperfect Price Perception." Environmental and Resource Economics 59, no. 4 (December 10, 2013): 561–81. http://dx.doi.org/10.1007/s10640-013-9750-z.

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47

Ferreira, Tiago de VG, and Orestes M. Goncalves. "Stochastic simulation model of water demand in residential buildings." Building Services Engineering Research and Technology 41, no. 5 (December 17, 2019): 544–60. http://dx.doi.org/10.1177/0143624419896248.

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Over the years, researchers have been conducting studies to investigate the water consumption profile in buildings; these studies have contributed to the accumulation of knowledge regarding the correct sizing of hydraulic systems in buildings. In the context of the methods for the characterization of system demand or loading values, the procedures commonly employed to obtain the project flow rate were primarily proposed in the mid-20th century. These models require revision and adaptation to the current water consumption values. In recent years, certain researchers have proposed simulation mod

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48

Rosenberg, David E. "Residential Water Demand under Alternative Rate Structures: Simulation Approach." Journal of Water Resources Planning and Management 136, no. 3 (May 2010): 395–402. http://dx.doi.org/10.1061/(asce)wr.1943-5452.0000046.

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49

Lee, Dongwoo, and Sybil Derrible. "Predicting Residential Water Demand with Machine-Based Statistical Learning." Journal of Water Resources Planning and Management 146, no. 1 (January 2020): 04019067. http://dx.doi.org/10.1061/(asce)wr.1943-5452.0001119.

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50

Bennett, Christopher, Rodney A. Stewart, and Cara D. Beal. "ANN-based residential water end-use demand forecasting model." Expert Systems with Applications 40, no. 4 (March 2013): 1014–23. http://dx.doi.org/10.1016/j.eswa.2012.08.012.

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