I. Pieterse-Quirijns’s research while affiliated with Amsterdam University of Applied Sciences and other places

Many researchers have developed drinking water demand models with various temporal and spatial scales. A limited number of models is available at a temporal scale of 1 s and a spatial scale of a single home. The reasons for building these models were described in the papers in which the models were introduced, along with a discussion on their potential applications. However, the predicted applications are seldom re-examined. SIMDEUM, a stochastic end-use model for drinking water demand, has often been applied in research and practice since it was developed. We are therefore re-examining its applications in this paper. SIMDEUM's original purpose was to calculate maximum demands in order to design self-cleaning networks. Yet, the model has been useful in many more applications. This paper gives an overview of the many fields of application for SIMDEUM and shows where this type of demand model is indispensable and where it has limited practical value. This overview also leads to an understanding of the requirements for demand models in various applications.

Many researchers have developed drinking water demand models with various temporal and spatial scales. A limited number of models are available at a temporal scale of one second and a spatial scale of a single home. Reasons for building these models were described in the papers in which the models were introduced, along with a discussion on potential applications. However, the predicted applications are seldom re-examined. As SIMDEUM, a stochastic end-use model for drinking water demand, has often been applied in research and practice since it was developed, we are re-examining its applications in this paper. SIMDEUM’s original purpose was to calculate maximum demands in order to be able to design self-cleaning networks. Yet, the model has been useful in many more applications. This paper gives an overview of the many fields of application of SIMDEUM and shows where this type of demand model is indispensable and where it has limited practical value. This overview also leads to an understanding of requirements on demand models in various applications.

Climate change poses new challenges to prevent exceeding the maximum allowed temperature in the drinking water distribution system (DWDS). The objective of this article is to evaluate the feasibility of using weather forecast data to predict the temperature in the DWDS. The water temperature was modeled using actual meteorological records and historical weather forecast data for a Dutch city for a warm period during the summer 2006. Results showed a maximum absolute error of 0.87 °C. These results indicate that it is possible to use weather forecast information as an “early warning system” to predict temperature in the DWDS.

Several applications of a new method for flow pattern analysis called the Comparison of Flow Pattern Distributions (CFPD) are presented. This method allows the user to compare flow patterns of a supply area and distinguish consistent from inconsistent changes in them. The so-called consistent changes are mainly caused by modifications in the characteristics of the population. The so-called inconsistent changes are generally related to new large volume customers, new types of water use and/or changes in leakage. Detailed knowledge of the supply area allows quantitative statements to be made about leakage. The method presented here is simple, not computationally intensive, independent of any model assumptions and easily implemented. The automated application of this method on long time series, called CFPD block analysis, is applied on data from three different areas. These applications illustrate, respectively, the identification and pinpointing (in time) of a small leak, the independent quantification of concurrent different types of changes with opposite signs in a supply pattern, and the difficulties of interpretation in cases in which climate has an overwhelming influence on demand patterns. In each case, discriminative quantification and visualization by the CFPD method result in features and trends in complicated time series becoming apparent at a glance.

According to the Dutch Drinking Water Directive, the maximum temperature of drinking water should be 25 degrees C. Occasionally, samples at the tap exceed this limit. With climate change, this limit may be exceeded more often. This article describes a model that predicts water temperatures in drinking water distribution systems (DWDSs). Soil temperature is influenced by weather conditions including atmospheric temperature and radiation and environmental conditions such as the soil's thermal conductivity and heat capacity. DWDS water approaches soil temperature at a rate that depends on flow velocity and the main's heat conductivity. In practice, the heating time required for drinking water to reach the soil temperature is shorter than the residence time in the DWDS. Two practical examples confirm the hypothesis that soil temperature predicts water temperature in the DWDS.

This paper presents a combination of two new approaches to leakage determination. The first approach is based on the classic bottom-up leakage determination, but uses a sophisticated demand model, SIMDEUM R, to estimate night demand. The second approach is a new analysis method for flow patterns, the Comparison of Flow Pattern Distributions (CFPD). This method allows distinguishing so called consistent from so called inconsistent changes in flow patterns. The latter includes leakage, the former does not. In an application of the first method, simulated demand is compared to measured demand for four different supply areas/DMAs. These examples illustrate cases with and without leakage, and difficulties related to the night demand of a large volume customer. CFPD analysis is applied to data from two areas. These applications illustrate, respectively, the identification and pinpointing (in time) of a small leak and the independent quantification of concurrent different types of changes with opposite signs in a supply pattern. In each case, the power of the CFPD block analysis is illustrated: discriminative quantification and visualization result in features and trends in complicated time series becoming apparent at a glance.