Neyman-Scott-based water distribution network modelling

First posted July 18, 2023 For additional information, contact: Director, New England Water Science CenterU.S. Geological Survey10 Bearfoot RoadNorthborough, MA 01532 To determine the most relevant climatic and economic factors driving water demand for Providence, Rhode Island, and to further the understanding of human interactions with water availability, linear regression models were developed to estimate single-family and multifamily residential, commercial, and industrial water demand for the service area of Providence Water for 2014–21. Monthly water use delivery data were provided by Providence Water. An array of climatic and economic data, the drought index, and binary variables to represent seasonal water use and the onset of the coronavirus (COVID–19) were investigated as possible explanatory variables for the water demand models. The water demand model with the best fit with the least amount of error was the single-family residential water demand followed in descending order of accuracy by the commercial, multifamily residential, and industrial water demand. Seasonal variables were significant in all models, and the COVID–19 binary variable was significant in the commercial and industrial models. One or two economic variables were significant in all models and one climatic variable was significant in all models except the commercial model.Overall residential, commercial, and industrial water demand in the Providence, Rhode Island, service area has decreased during the study period most likely because of widescale drought conditions and policies designed to improve water efficiencies. The linear regression models developed for single-family and multifamily residential, commercial, and industrial water use explained 94, 85, 91, and 77 percent, respectively, of the variability in monthly water use. Multifamily residential water demand displayed a less distinct seasonal trend than that observed for single-family residential customers, likely because multifamily homes tend to use less water outdoors. The commercial water-demand model included no climatic variables, one economic variable, the COVID–19 pandemic variable, and the high and low water use seasonal variables—the latter two variables indicating the importance of seasonal fluctuations in water use. The COVID–19 pandemic and a concomitant State executive order had the immediate effect of severely reducing commercial water use. The industrial water-demand model did not perform as well as the other models because industrial water delivery data display a greater range of values, both seasonally and for the overall study period.

With the increasing adoption of demand management strategies and water conservation practices, domestic water consumption is projected to decline in the future. The subsequent consumer-side demand reductions are expected to result in increased residence times in water distribution networks (WDNs), and thus could have negative effects on the water quality (WQ) reaching the consumers’ taps. This study evaluates the impacts of the projected decrease in residential water demands on the deterioration of the WQ in WDNs. This deterioration will likely be most prominent in the dead-end branches at the perimeters of WDNs, where the flow is characteristically low and intermittent. The assessment of WQ deterioration in the dead-end branches requires the implementation of an accurate WQ simulation model. To this end, a new Python-based software package, WUDESIM_Py, is first introduced. The WQ simulation engine in WUDESIM_Py is based on an advection-dispersion-reaction model and accounts for the spatial distribution of water demands along dead-end pipes. WUDESIM_Py comprises various sets of functions that allow the users to set-up and conduct WQ simulations as well as obtain and visualize simulation results. A complete description of the different functions, together with examples of how these functions can be implemented in different applications, is provided. Through conducting extensive simulations of benchmark WDNs, the results revealed that widespread adoption of water conservation practices can lead to significant WQ deterioration in the dead-end branches. Additionally, the results suggested that neglecting dispersive transport and spatially aggregating demands may result in overestimating residual chlorine concentrations in the dead-end branches, which could mask the real impacts of demand reduction on WQ deterioration.

In this paper a model of indoor residential water demand for describing the instantaneous temporal and spatial variability of flow in a municipal water distribution system is presented.
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\nSuch a model can be a powerful tool for studying both problems of planning and managing water distribution networks. In particular, as it provides much more accurate information on the real water demands, it makes possible a better design of the system.
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\nMoreover a more efficient management of instantaneous water pressure heads is made also possible, allowing the control of water losses and of water quality along the whole distribution system.
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\nIn the field of water losses control, instantaneous water demand models can also be used to develop simplified techniques of district metering.

Abstract: Residential water demand is a function of several factors, some of which are within the control of water utilities (e.g., price, water restrictions, rebate programs) and some of which are not (e.g., climate and weather, demographic characteristics). In this study of Aurora, Colorado, factors influencing residential water demand are reviewed during a turbulent drought period (2000‐2005). Findings expand the understanding of residential demand in at least three salient ways: first, by documenting that pricing and outdoor water restriction policies interact with each other ensuring that total water savings are not additive of each program operating independently; second, by showing that the effectiveness of pricing and restrictions policies varies among different classes of customers (i.e., low, middle, and high volume water users) and between predrought and drought periods; and third, in demonstrating that real‐time information about consumptive use (via the Water Smart Reader) helps customers reach water‐use targets.

The American infrastructure report card in 2013 rated the US water system infrastructure with grade of ‘D’. The Canadian infrastructure report card in 2012 stated that around 15.4% of Canada’s water infrastructure has a condition of fair to very poor. Thus, there is a critical need to develop efficient inspection, maintenance and rehabilitation plans for water distribution networks. However, such plans require an assessment tool to evaluate the performance and condition of water distribution networks. Therefore, the main purpose of this paper is to develop an integrated performance assessment model for water distribution networks. Two modules were developed to assess the performance of water pipelines and accessories, respectively. A third module was developed to assess the performance of water segments that includes pipelines and accessories. Moreover, a fourth module was built to assess the performance of each sub-network and the entire network based on the segments’ connection type using a reliability-based approach. To assess the performance of the water distribution network, the critical factors affecting its pipelines and accessories were identified and studied. The fuzzy analytic network process technique was used to obtain the importance weights of the identified factors.

This paper reports an investigation of the impact of price policies (PP) and non-price policies (NPP) on residential water demand. Using a sample of US water utilities located in Wisconsin, residential water demand was estimated by taking into account the fact that some of the characteristics of local communities that determine PP and NPP choices may also influence residential water consumption levels. It is first shown that neglecting endogeneity of PP or NPP may lead to biased parameter estimates. Second, it is demonstrated that the policy mix (PP or NPP) may be as important as the level of prices for determining water consumption. Lastly, evidence is provided that dissemination efforts made by local communities to promote NPP drive the effectiveness of those policies.

This paper uses monthly data from eleven local governments to model residential water demand in Queensland, Australia from 1994 to 2004. In the sample, residential consumption is charged using a variety of structures including fixed charges without allowance, fixed charges with allowance and excess rates, two-part tariffs comprising an access charge and a flat consumption rate, and multi-part tariffs with an access charge and two or more limits with increasing consumption rates. Water demand is specified as average monthly household water consumption and the demand characteristics include the marginal and average price of water and daily average maximum temperatures and rainfall. The findings confirm residential water as price inelastic, more responsive to average than marginal prices, and more responsive to changes in temperature than rainfall. The results also suggest that cross-sectional variation in demand is related to local government-specific factors.

Stochastic point processes for rainfall are known to be able to preserve the temporal variability of rainfall on several levels of aggregation (e.g. hourly, daily), especially when the cluster approach is used. One major assumption in most of the applications todate is the stationarity of the rainfall properties in time, which must be reconsidered under a climate change hypothesis. Here, we propose new theoretical developments of a Poisson-based model with cluster, namely the Neyman–Scott Rectangular Pulses Model, which treats storm frequency as a nonstationary function. In this paper, storm frequency is modelled as a linear function of time in order to reproduce an increase (or decrease) of the mean annual precipitation. The basic theory is reconsidered and the moments are derived up to the third order. Then, a calibration method based on the generalized method of moments is proposed and discussed. An application to a rainfall time series from Uccle illustrates how this model can reproduce a trend for the average rainfall. This work opens new avenues for future developments on transient stochastic rainfall models and highlights the major challenges linked to this approach.

This study undertook the residential water demand analysis based on a panel data covered 31 provinces of China during the sample period from 2004 to 2013. Two models are employed in this study: Double-Log model and EDM model. The estimates of double-log model show that the different levels of income cannot impact the water price elasticities significantly but the fixed effects estimator gives a more appropriate estimate for the water demand system. In addition, both the water price and the income are inelastic. However, with the EDM model estimations, the results reveal that the water price is elastic for the residential water demand in the short run, and the partial price-supply elasticity is negative due to estimates of total elasticities. Both two models gave the result that the residential water is an inferior good in China.

This study sought to analyse trend in the monthly water demand data series in Kisumu city at both seasonal and non-seasonal levels using the parametric method of Ordinary Least Squares (OLS) and non-parametric methods of Mann-Kendall tau and Sen's T test. Sen’s test was applied to validate the Mann Kendall trend test and to estimate the magnitude of the trend and its direction. The significance of the slope of the OLS equation was tested using the F-Test based on the Analysis of Variance (ANOVA). Secondary monthly water consumption data obtained from KIWASCO for the period January 2004 to December 2013 were used. Using logarithmically transformed data, the study established by OLS that residential water demand in Kisumu City had a significant increasing trend (FCalc=(105.13) > F(1;119)(α=0:05)=(5.15)). Kendall's tau test corroborated the OLS results of a significant increasing trend. The Sens T test indicated that most of the months registered significant upward trend with Sen’s slope estimates showing positive rates of change in residential water demand for these months.

In this paper, we show that the rate structure endogeneity may result in a misspecification of the residential water demand function. We propose to solve this endogeneity problem by estimating a probabilistic model describing how water rates are chosen by local communities. This model is estimated on a sample of Canadian local communities. We first show that the pricing structure choice reflects efficiency considerations, equity concerns, and, in some cases, a strategy of price discrimination across consumers by Canadian communities. Hence estimating the residential water demand without taking into account the pricing structures' endogeneity leads to a biased estimation of price and income elasticities. We also demonstrate that the pricing structure per se plays a significant role in influencing price responsiveness of Canadian residential consumers.

In the first place, this paper presents an examination of the effects of missing cadastral information on the modeling and calibration of water distribution networks (WDN). For this purpose, cadastral errors were artificially introduced to the hydraulic models of the WDN of three cities of the province of Valle del Cauca (Colombia), with population in the range of 15,000–30,000 inhabitants. The cadastral errors were introduced by eliminating some pipes from the unaltered models using the resilience index as selection criterion. From this examination, typical responses observed during the calibration process were associated to specific topological and hydraulic errors and it was also noted that there is a direct relation between the resilience index and the effect of the removal of a certain pipe on the hydraulic performance of the model. In the second place, after identifying the effects of missing and erroneous cadastral and hydraulic information on the calibration of WDN models, the inverse procedure was carried out: the calibration process was used as a tool for detecting missing and erroneous cadastral and hydraulic information. This methodology was successfully applied to the WDN of Sector Estadio (250,000 inhabitants), which is a complex area of the city of Bucaramanga (Colombia); through the calibration of this WDN, errors such as failing water meters, pipes and valves that existed in the real system but were not in the model, significant errors in pipe diameters in the model, among others, were identified.

A climate model is essential for hydrological designs considering climate change, but there are still limitations in employing raw temporal and spatial resolutions for small urban areas. To solve the temporal scale gap, a temporal disaggregation method of rainfall data was developed based on the Neyman–Scott Rectangular Pulse Model, a stochastic rainfall model, and future design rainfall was projected. The developed method showed better performance than the benchmark models. It produced promising results in estimating the rainfall quantiles for recurrence intervals of less than 20 years. Overall, the analysis results imply that extreme rainfall events may increase. Structural/nonstructural measures are urgently needed for irrigation and the embankment of new water resources.

<p>Large areas of Campania (southern Italy) are characterized by steep slopes covered with shallow deposits of loose pyroclastic materials, laying upon bedrocks with different characteristics (i.e., limestones, dolomites, volcanic tuff). The pyroclastic covers, usually in unsaturated conditions, are frequently affected by rainfall-induced shallow landslides, which cause heavy damage to property and infrastructures and sometimes casualties. Owing to the brittle behavior of the involved soils, hardly exhibiting any deformation before failure, the occurrence of such landslides is not easily predictable, so that operational early warning systems for rainfall-induced landslides (LEWS) usually rely only on empirical thresholds based on precipitation information (i.e., intensity and duration of triggering rainfall event). Anyway, the reliability of landslide prediction would benefit from the inclusion of hydrological information about the condition of the slope cover before the onset of the triggering rainfall (e.g., Marino et al., 2020a).</p><p>Three years of continuous field monitoring carried out at the slope of Cervinara, located around 40 km north-east of the city of Naples, where a destructive flowslide occurred in December 1999, have provided insight of the hydrological processes controlling the water balance of the pyroclastic deposits, laying upon a densely fractured limestone bedrock, where a temporary perched aquifer develops during the rainy season (Marino et al., 2020b). This knowledge allowed setting up a physically based model capable of identifying the seasonality of the predisposing conditions leading to slope failure (Greco et al., 2018; Marino et al., 2021). Aiming at identifying the hydrological processes mostly affecting landslide triggering, the model is coupled with a stochastic rainfall generator (i.e., the Neyman-Scott rectangular pulse model), previously calibrated based on 20 years hourly rainfall data, obtaining a 1000 years long synthetic series of the slope cover response to precipitations (in terms of soil suction, water content, perched aquifer water level, and leakage through the soil-bedrock interface). The obtained synthetic dataset of rainfall and hydrological variables have been analyzed with machine-learning techniques, so to identify the most effective combination of variables for landslide predictions.</p><p>The analysis of the synthetic data allows identifying the most suitable variables to be monitored, for assessing the hydrologic conditions predisposing the slopes to failure. In fact, the obtained results are confirmed by the analysis of the available field monitoring data, indicating that coupling rainfall measurements with field and remote hydrological monitoring significantly improves landslide prediction.</p><p>References</p><p>Greco R, Marino P, Santonastaso GF, Damiano E (2018). Interaction between perched epikarst aquifer and unsaturated soil cover in the initiation of shallow landslides in pyroclastic soils. Water 10:948.</p><p>Marino P, Peres DJ, Cancelliere A, Greco R, Bogaard TA (2020a). Soil moisture information can improve shallow landslide forecasting using the hydrometeorological threshold approach. Landslides 17(9): 2041-2054.</p><p>Marino P, Comegna L, Damiano E, Olivares L, Greco R (2020b). Monitoring the Hydrological Balance of a Landslide-Prone Slope Covered by Pyroclastic Deposits over Limestone Fractured Bedrock. Water 12(12): 3309.</p><p>Marino P, Santonastaso GF, Fan X, Greco R (2021). Prediction of shallow landslides in pyroclastic-covered slopes by coupled modeling of unsaturated and saturated groundwater flow. Landslides 18(1): 31-41.</p>

The relationship between climate change and floods frequency is of great interest for addressing the complex analysis on the hydrologic cycle evolution. In this context, this study aims to assess, by a preliminary investigation, the climate change effects on the floods frequency in several basins of the upper Tiber River, whose area is ranging from 100 to 300 km2. For that, a continuous hydrological model coupled with a stochastic generation of rainfall and temperature has been used. Therefore, a long synthetic series of discharge were generated from which the annual maximum discharges were extracted and, hence, the flood frequency curves were defined. For the stochastic generation of precipitation, the Neyman-Scott Rectangular Pulse model was used, while for the synthetic generation of temperature, an ARIMA model with fractional differentiation was applied. The time series of discharge was assessed by applying a continuous hydrological model developed ad hoc for the investigated basins. The model structure was inferred by investigating the effects of antecedent wetness conditions on the outlet response of several experimental basins located in Central Italy. The analysis proposed here compares the actual time series of precipitation and temperature and the perturbed ones by assuming two different future scenarios obtained by the Global Circulation Model HadCM3. Results showed that geo-morphological and land-use characteristics of basins might have a paramount role in the changing of floods frequency.