Prediction of urban residential end-use water demands by integrating known and unknown water demand drivers at multiple scales I: Model development

Abstract

Detailed prediction of water demand by their end-uses at multiple scales is essential to support planning of Integrated Urban Water Management, an increasingly applied approach to deal with the problem of water scarcity. This paper presents an urban residential water demand modeling framework that can predict end-use water demand at multiple scales, especially at small scales with a robust explanatory capacity. This is achieved by integrating the complex water demand dynamics of urban residential water use and their underlying variables into a single model. The model described in this study can predict shower, toilet, tap, dishwasher, clothes washer, irrigation, evaporative cooler, bath, and other uses which account for the entire household water use. The model aims to predict water demand at multiple spatial (household/cluster/suburb) and temporal scales (hourly, daily, weekly and seasonal) by considering behavioral differences triggered by factors such as seasonality and presence of people at home. The model incorporates an improved representation of spatial variability by considering behavioral differences between customer groups, and improves the capability to deal with areas with different demographic and housing characteristics. This research confirms the capacity of stochastic modeling methods to represent unexplained behavior of water consumers.

Analysis of the Structure of Water Demand with the Example of Selected Buildings

“…In this case, it had a significant impact on increasing the average value of water demand. However, for most buildings, the average daily demand was close to or lower than the values in the regulations [12] and in the literature [4,13]. q-average daily water consumption per customer, Qdśr-average daily water demand per building, qmax-maximum momentary volumetric flow, Nd-daily peak factor, Nh-hourly peak factor, the maximum results have been marked with a grey background.…”

“…This is due to the following factors influencing the structure of water distribution: differentiation of consumers in terms of water demand and the resulting varied volumetric water streams over time; residential buildings, hotels, restaurants, schools, industrial plants, shops, etc. ; a constant reduction in water consumption by consumers, due to the introduction of modern and more efficient equipment and fittings, as well as for economic reasons; the impact of weather and climate changes on water demand; the large variety of types and sizes of measuring devices [1][2][3][4][5].…”

Analysis of the Structure of Water Demand with the Example of Selected Buildings

“…In this case, it had a significant impact on increasing the average value of water demand. However, for most buildings, the average daily demand was close to or lower than the values in the regulation [25] and in the literature [4,27]. The lowest water demand was observed in buildings 6 and 9, built between the 1960s and 1970s.…”

Quantifying the water footprint of an urban agglomeration in developing economy

“…These approaches require massive trade data at the city level, which is not possible for every sector of a city. There has been much research on managing the direct WF of cities at global, national boundaries and also for India (Manzardo et al, 2016, Rathnayaka et al, 2016, Shaban and Sharma 2007aand B. A. George et al, 2009.…”