PEAK PERIOD DESIGN STANDARDS FOR SMALL WESTERN U.S. WATER SUPPLY SYSTEMS1

ABSTRACT: In order to determine design capacities for various components of municipal and rural domestic water supply systems, engineers must estimate water requirements for an entire year (water rights), for the peak season (reservoir storage), for the peak day (pump or treatment plant size), and for peak hour (pipeline sizes). Historically, per capita water use rates have varied greatly between systems, particularly in semiarid regions where outdoor demands are large. The resulting uncertainty in design capacity estimates can cause either inadequate capacities or premature investment. In order to minimize that uncertainty multiple regression and frequency analyses were made of the various water demand parameters mentioned above for 14 systems in Utah and Colorado. Specifically, demand functions are reported for average month, peak month, and peak day. Peak hour demands were also studied but are reported in a different paper. The independent variables which were significant for monthly and daily demands were price of water and an outdoor use index which includes the effect of variation in landscaped area and accounts for use of supplementary ditch or pressure irrigation systems. The demand functions were developed with data from systems varying in size from very small low density rural systems to Salt Lake City's water system. The correlation coefficients (R²) vary from 0.80 to 0.95.     

Analysis and Predictive Models of Single‐Family Customer Response to Water Curtailments During Drought1

Polebitski, Austin S. and Richard N. Palmer, 2012. Analysis and Predictive Models of Single‐Family Customer Response to Water Curtailments During Drought. Journal of the American Water Resources Association (JAWRA) 1‐12. DOI: 10.1111/j.1752‐1688.2012.00691.x Abstract: This research investigates customer response to demand management strategies during two drought periods in the City of Seattle. An analysis of customer response to voluntary water curtailments is conducted using k‐means clustering to identify like groups of customers and behavior patterns. The clustering method identified important variables (household income, lot size, living space, and family size) useful in determining customer response to water curtailments. Ordinary least squares and spatial lag regression models are estimated using the first and second principal components of variables identified in the clustering analysis. Larger values of income, lot size, and living space enhanced water reductions whereas larger family size tended to reduce the effectiveness of curtailments. Projections of changes in Seattle’s built environment and demographics between 2000 and 2030 were obtained from an urban simulation model (UrbanSim) and were processed through the regression models to investigate changes in future curtailment effectiveness. This research found that increasing household size hardened demands (decreased curtailment effectiveness) whereas decreasing household size increased per‐capita curtailment effectiveness. These results suggest that changes in the number of residents within a home is likely to be the most important factor in determining future curtailment effectiveness.     

THE IMPACT OF POLICY VARIABLES ON RESIDENTIAL WATER DEMAND AND RELATED INVESTMENT REQUIREMENTS1

Increasing population and urbanization necessitate very large investments in municipal water supply. These investments could be more efficiently deployed if the impact of policy variables such as marginal pricing, metering, by-laws on lawn watering and plumbing fixtures, and higher summer charges were known. The paper in particular advocates the replacement of the present declining block rate by an increasing block rate. In order to know the impact of policy variables, a multiple regression model is built; the fitted model is tested against some data not included in calibrating the model. Next the impact of selected policy variables on the target variable (residential water demand during summer) is worked out for a new urban community of 200,000 people. The investment requirements may decrease appreciably as a result of a price increase when marginal (or commodity) charges are low but the impact of price changes when commodity charges are already high is less evident and non-price policy variables may be more effective in maintaining high quality water and also satisfying the constraint of limited budgets for municipal services.     

WATER REDUCTIONS FROM RESIDENTIAL AUDITS1

ABSTRACT: The water reductions resulting from Contra Costa Water District's 1989 residential audit program are measured using a multivariate regression model. The model explains metered residential water use as a function of both conservation and other household variables. The principle conclusions drawn are that (1) installation of low-flow showerheads reduced indoor water use by 9.7 percent or 7.8 gallons per capita day, (2) the outdoor segment of the audit reduced irrigation needs by 18.7 percent, and (3) irrigation timers are being used inefficiently.     

Spatial and Seasonal Response of Municipal Water Use to Weather across the Contiguous U.S.

Weather variability has the potential to influence municipal water use, particularly in dry regions such as the western United States (U.S.). Outdoor water use can account for more than half of annual household water use and may be particularly responsive to weather, but little is known about how the expected magnitude of these responses varies across the U.S. This nationwide study identified the response of municipal water use to monthly weather (i.e., temperature, precipitation, evapotranspiration [ET]) using monthly water deliveries for 229 cities in the contiguous U.S. Using city‐specific multiple regression and region‐specific models with city fixed effects, we investigated what portion of the variability in municipal water use was explained by weather across cities, and also estimated responses to weather across seasons and climate regions. Our findings indicated municipal water use was generally well‐explained by weather, with median adjusted R² ranging from 63% to 95% across climate regions. Weather was more predictive of water use in dry climates compared to wet, and temperature had more explanatory power than precipitation or ET. In response to a 1°C increase in monthly maximum temperature, municipal water use was shown to increase by 3.2% and 3.9% in dry cities in winter and summer, respectively, with smaller changes in wet cities. Quantifying these responses allows urban water managers to plan for weather‐driven variability in water use.     

EFFECTIVENESS OF DROUGHT POLICIES FOR MUNICIPAL WATER MANAGEMENT1

During the 1976–77 drought, three principal mechanisms were used to reduce water use in Utah communities: price increases, maximum monthly use restrictions, and restrictions on outdoor watering times. A regression model was developed to explain observed changes in water use, with price, type of restriction, household size, and summer rainfall as independent variables. For an average system, a 1 percent increase in price would reduce water use by 0.07 to 0.09 percent. A 1 percent increase in outdoor watering time restriction reduces use by 0.064 to 0.075 percent. A 1 percent increase in quantity restrictions leads to a reduction in water use of 0.014 to 0.054 percent. The effectiveness of rationing policies is influenced by system characteristics. For example, outdoor watering time restrictions were less effective in systems with above average household size and below average monthly use.     

WATER USE RELATIONSHIPS AND PROJECTION CORRESPONDING WITH REGIONAL GROWTH,SEATTLE REGION1

ABSTRACT: The paper outlines both the methods used and the results obtained in a study of the demand for municipal and industrial water for the Seattle region. The study was made as part of a regional water management study program, one objective of which is to “… identify, quantify, and set priorities for all current and future water uses …”. A basic concept in the study of municipal and industrial water use is that the demand for water is derived from the demand for output and the direct services that water provides. Principal characteristics of the study are: (1) Water use is studied by type - residential, commercial, industrial and public -with identification of factors affecting each; (2) Water demands are studied by season as well as on an annual basis; (3) Projections of future water use are tied directly to projections of economic change in the service area; and (4) The effects of alternative policies on water use are estimated. Water use levels are projected under alternative regional growth assumptions provided by the Puget Sound Governmental Conference, a regional planning agency. Thus, the water use planning is consistent with other regional planning programs in this respect. The results can be varied according to changes in specific factors affecting water use. The factors considered in the present study include: single-family residential lot size, distribution of population between single- and multi-family units, per capita water use by multi-family unit residents, and industrial and commercial water use per employee. An income elasticity of demand was estimated for single-family residential water use.     

System dynamics modeling for municipal water demand estimation in an urban region under uncertain economic impacts

Accurate prediction of municipal water demand is critically important to water utilities in fast-growing urban regions for drinking water system planning, design, and water utility asset management. Achieving the desired prediction accuracy is challenging, however, because the forecasting model must simultaneously consider a variety of factors associated with climate changes, economic development, population growth and migration, and even consumer behavioral patterns. Traditional forecasting models such as multivariate regression and time series analysis, as well as advanced modeling techniques (e.g., expert systems and artificial neural networks), are often applied for either short- or long-term water demand projections, yet few can adequately manage the dynamics of a water supply system because of the limitations in modeling structures. Potential challenges also arise from a lack of long and continuous historical records of water demand and its dependent variables. The objectives of this study were to (1) thoroughly review water demand forecasting models over the past five decades, and (2) propose a new system dynamics model to reflect the intrinsic relationship between water demand and macroeconomic environment using out-of-sample estimation for long-term municipal water demand forecasts in a fast-growing urban region. This system dynamics model is based on a coupled modeling structure that takes into account the interactions among economic and social dimensions, offering a realistic platform for practical use. Practical implementation of this water demand forecasting tool was assessed by using a case study under the most recent alternate fluctuations of economic boom and downturn environments.     

IDENTIFICATION OF MONTHLY TRENDS IN URBAN WATER USE1

ABSTRACT: Monthly water use for the period 1960–1984 for the Columbus, Ohio, metropolitan area is analyzed to identify differential monthly trends in growth of water use. By associating water use activities with the identified trend months, inferences may be made as to the possible underlying causes of the observed trend in overall water use. Three methods were found useful in determining monthly trends: 1) regression analysis on the monthly percentage of annual use, 2) regression analysis on the monthly water use data itself, and 3) analysis of the slope of the monthly water use regression line. Agreement between the three methods is strong, but each provides some insight not found in the others. All three should be used in drawing final conclusions. For the case study, usage in the Winter months January-April has grown considerably relative to the other months, while the Summer-Fall months of June, August, September, and October show a relative decline. A possible explanation for the trend is aging of the distribution system, with consequent general leakage and increased water main breakage caused by freeze/thaw conditions. More research needs to be carried out linking water use activities to particular months or groups of months.     

RESIDENTIAL WATER CONSERVATION: CASA DEL AGUA1

ABSTRACT: A single-family residence in Tucson, Arizona, was retrofitted with water-conserving fixtures, rainwater harvesting, and graywater reuse systems. During a four-year study, efficient use of water was shown to significantly decrease demand for domestic water at the house without reducing the residents' quality of life. The use of municipal water was reduced by 66 percent to 148 gallons per day (gpd) and total household use was reduced by 27 percent to 245 gpd. Graywater reuse averaged approximately 77 gpd or 32 percent of the total household water use. Evaporative cooling required about 15 gpd. Water use for toilet flushing was only 9 gallons per capita per day (gpcd) or 14 percent of interior water use.     

Residential Demand for Water in the Chicago Metropolitan Area1

Mieno, Taro and John B. Braden, 2011. Residential Demand for Water in the Chicago Metropolitan Area. Journal of the American Water Resources Association (JAWRA) 47(4):713‐723. DOI: 10.1111/j.1752‐1688.2011.00536.x Abstract: This paper provides the first contemporary analysis of residential water demand in humid Northeastern Illinois, in the vicinity of Chicago, and explores seasonal and income‐based differentials in the responsiveness of water use to water prices. Using a panel of system‐level data for eight water systems and controlling for seasons, weather, incomes, and community characteristics, the analysis yields low estimates of price elasticity of demand for water in line with other studies. Furthermore, price response is greater in summer and less in higher income communities. We suggest that use of seasonal pricing can help mitigate equity issues arising from differential income elasticities while taking advantage of the greater price responsiveness of summertime water use.     

Mason Valley Groundwater Model: Linking Surface Water and Groundwater in the Walker River Basin,Nevada1

Carroll, Rosemary W.H., Greg Pohll, David McGraw, Chris Garner, Anna Knust, Doug Boyle, Tim Minor, Scott Bassett, and Karl Pohlmann, 2010. Mason Valley Groundwater Model: Linking Surface Water and Groundwater in the Walker River Basin, Nevada. Journal of the American Water Resources Association (JAWRA) 46(3):554-573. DOI: 10.1111/j.1752-1688.2010.00434.x Abstract: An integrated surface water and groundwater model of Mason Valley, Nevada is constructed to replicate the movement of water throughout the different components of the demand side of water resources in the Walker River system. The Mason Valley groundwater surface water model (MVGSM) couples the river/drain network with agricultural demand areas and the groundwater system using MODFLOW, MODFLOW’s streamflow routing package, as well as a surface water linking algorithm developed for the project. The MVGSM is capable of simulating complex feedback mechanisms between the groundwater and surface water system that is not dependent on linearity among the related variables. The spatial scale captures important hydrologic components while the monthly stress periods allow for seasonal evaluation. A simulation spanning an 11-year record shows the methodology is robust under diverse climatic conditions. The basin-wide modeling approach predicts a river system generally gaining during the summer irrigation period but losing during winter months and extended periods of drought. River losses to the groundwater system approach 25% of the river’s annual budget. Reducing diversions to hydrologic response units will increase river flows exiting the model domain, but also has the potential to increase losses from the river to groundwater storage.     

Application of best subset method for river water quality modeling: A study on Godavari River,India

The utilization of water quality analysis to inform optimal decision-making is imperative to achieve sustainable management of river water quality. A multitude of research works in the past has focused on river water quality modeling. Despite being a precise statistical regression technique that allows for fitting separate models for all potential combinations of predictors and selecting the optimal subset model, the application of best subset method in river water quality modeling is not widely adopted. The current research aims to validate the use of best subset method in evaluating the water quality parameters of the Godavari River, one of the largest rivers in India, by developing regression equations for different combinations of its physicochemical parameters. The study involves in formulating best subset regression equations to estimate the concentrations of river water quality parameters while also identifying and quantifying their variations. A total of 17 water quality parameters are analyzed at 13 monitoring sites using 13 years (1993–2005) of observed data for the monsoon (June–October) period and post-monsoon (November–February) period. The final subset model is selected among model combinations that are developed for each year's dataset through widely used statistical criteria such as R², F value, adjusted R²_a, AIC_c, and RSS. The final best subset model across all parameters exhibits R² values surpassing 0.8, indicating that the models possess the ability to account for over 80% of the variations in the concentrations of dependent parameters. Therefore, the findings demonstrated the appropriateness of this method in evaluating the water quality parameters in extensive rivers. This work is very useful for decision-making and in the management of river water quality for its sustainable use in the study area.     

Projected Freshwater Withdrawals Under Efficiency Scenarios for Electricity Generation and Municipal Use in the United States for 20301

Chen, Limin, Sujoy B. Roy, and Robert A. Goldstein, 2012. Projected Freshwater Withdrawals Under Efficiency Scenarios for Electricity Generation and Municipal Use in the United States for 2030. Journal of the American Water Resources Association (JAWRA) 1‐16. DOI: 10.1111/jawr.12013 Abstract: Water withdrawals in the United States (U.S.) have been relatively uniform over the past two decades on a nationally aggregated basis, although on a more highly resolved geographical basis, increases have occurred, largely associated with growth in population and the cooling needs for new electricity generation. Using recent county‐level water use data, we develop projections for five different scenarios, bracketing a range of future conditions, and representing different levels of efficiency in the municipal and electricity generation sectors, where the municipal sector includes public and self‐supplied domestic withdrawals. Starting with the 2005 estimate of 347 billion gallons per day (bgd) of freshwater withdrawal in the continental U.S., our analysis shows that under a business‐as‐usual scenario of growth, there will be a need for additional water over current levels: 11 bgd in the municipal sector, with a smaller requirement for new electricity generation (1 bgd). However, we also estimate that withdrawals could be reduced significantly over current levels, through increased water use efficiencies in the electric power and municipal sectors. The study shows that if water withdrawals are to be held at their current levels for the thermoelectric and municipal sectors individually at a county level over the next 25 years, large improvements in efficiency will be needed in many parts of the Southeast and Southwest.     

SPATIAL VARIABILITY IN WATER‐BALANCE MODEL PERFORMANCE IN THE CONTERMINOUS UNITED STATES1

ABSTRACT: A monthly water‐balance (WB) model was tested in 44 river basins from diverse physiographic and climatic regions across the conterminous United States (U.S.). The WB model includes the concepts of climatic water supply and climatic water demand, seasonality in climatic water supply and demand, and soil‐moisture storage. Exhaustive search techniques were employed to determine the optimal set of precipitation and temperature stations, and the optimal set of WB model parameters to use for each basin. It was found that the WB model worked best for basins with: (1) a mean elevation less than 450 meters or greater than 2000 meters, and/or (2) monthly runoff that is greater than 5 millimeters (mm) more than 80 percent of the time. In a separate analysis, a multiple linear regression (MLR) was computed using the adjusted R‐square values obtained by comparing measured and estimated monthly runoff of the original 44 river basins as the dependent variable, and combinations of various independent variables [streamflow gauge latitude, longitude, and elevation; basin area, the long‐term mean and standard deviation of annual precipitation; temperature and runoff; and low‐flow statistics (i.e., the percentage of months with monthly runoff that is less than 5 mm)]. Results from the MLR study showed that the reliability of a WB model for application in a specific region can be estimated from mean basin elevation and the percentage of months with gauged runoff less than 5 mm. The MLR equations were subsequently used to estimate adjusted R‐square values for 1,646 gauging stations across the conterminous U.S. Results of this study indicate that WB models can be used reliably to estimate monthly runoff in the eastern U.S., mountainous areas of the western U.S., and the Pacific Northwest. Applications of monthly WB models in the central U.S. can lead to uncertain estimates of runoff.     

A MUNICIPAL WATER SUPPLY INVESTMENT PROBLEM IN RHODE ISLAND1

ABSTRACT: The traditional “requirements” approach to water system planning presumes perfectly inelastic demand and arbitrarily selects a fixed water requirement per capita per day as a planning target. Economists have often pointed out that such a policy leads to over-investment in water supply facilities; a superior approach would maximize some measure of net benefits incorporating price-sensitive demand. Using a dynamic programming model to depict an investment problem in Rhode Island, we find that ambiguities about how to incorporate price-sensitive demand into a decision framework may make such an approach as arbitrary as the requirements approach. Water conservation responses may be a function of other social parameters than water price; if so, variations in these social parameters should be regarded as economic alternatives to water supply investments.     

AN EVALUATION OF WEEKLY AND MONTHLY TIME SERIES FORECASTS OF MUNICIPAL WATER USE1

ABSTRACT: A cascade model for forecasting municipal water use one week or one month ahead, conditioned on rainfall estimates, is presented and evaluated. The model comprises four components: long term trend, seasonal cycle, autocorrelation and correlation with rainfall. The increased forecast accuracy obtained by the addition of each component is evaluated. The City of Deerfield Beach, Florida, is used as the application example with the calibration period from 1976–1980 and the forecast period the drought year of 1981. Forecast accuracy is measured by the average absolute relative error (AARE, the average absolute value of the difference between actual and forecasted use, divided by the actual use). A benchmark forecast is calculated by assuming that water use for a given week or month in 1981 is the same as the average for the corresponding period from 1976 to 1980. This method produces an AARE of 14.6 percent for one step ahead forecasts of monthly data and 15.8 percent for weekly data. A cascade model using trend, seasonality and autocorrelation produces forecasts with AARE of about 12 percent for both monthly and weekly data while adding a linear relationship of water use and rainfall reduces the AARE to 8 percent in both cases if it is assumed that rainfall is known during the forecast period. Simple rainfall predictions do not increase the forecast accuracy for water use so the major utility of relating water use and rainfall lies in forecasting various possible water use sequences conditioned on sequences of historical rainfall data.     

STATISTICAL ANALYSIS OF WATER CONSUMPTION1

The purpose of this study was to determine the degree of influence of various factors on municipal water consumption in Illinois. For the collection of basic data, questionnaires were sent to all public water works of incorporated towns. The questionnaire was designed to obtain information on factors which may have any effect on water use. The effects of the different parameters on water consumption were based on several correlation and regression combinations of predictands and predictors. It was found that in the Chicago region the percent of services and water used for commercial and industrial purposes and the age of the water works were the most important parameters influencing water consumption (gallons per capita per day) when pumpage is metered at the water works as well as at the customers. For the State, excluding the Chicago region, percent of public water use, persons per service, population and commercial and industrial water use were the most important parameters. It has been recommended that similar statistical analysis be conducted periodically to establish a trend or law of change from the influencing parameters.     

WATER PRICE RESPONSIVENESS OF COMMERCIAL ESTABLISHMENTS1

ABSTRACT: Pricing policy in water allocation has become of more concern as some areas find water is indeed a scarce resource. Demand estimates, where the quantity purchased-value in use relationships are of concern, have been made in other studies for residential, industrial, and agricultural uses in many areas of the country. The price-quantity relations for water use in commercial firms are estimated and discussed for several different types of stores in this study. A derived demand model is used to estimate commercial demand in the Miami, Florida, area. The price elasticity was generally low (inelastic) for all groups studied except for department stores. This group was found to have an elastic demand for water at all prices above $0.93 per thousand gallons purchased per month, where the mean price for this part of the sample was $1.24. The major implication of the study is that commercial establishments may be responsive to price changes over the long run, much as has already been shown for other types of user groups in other studies.     

The Effects of Irrigation and Climate on the High Plains Aquifer: A County‐Level Econometric Analysis

The High Plains Aquifer (HPA) underlies parts of eight states and 208 counties in the central area of the United States (U.S.). This region produces more than 9% of U.S. crops sales and relies on the aquifer for irrigation. However, these withdrawals have diminished the stock of water in the aquifer. In this paper, we investigate the aggregate county‐level effect on the HPA of groundwater withdrawal for irrigation, of climate variables, and of energy price changes. We merge economic theory and hydrological characteristics to jointly estimate equations describing irrigation behavior and a generalized water balance equation for the HPA. Our simple water balance model predicts, at average values for irrigation and precipitation, an HPA‐wide average decrease in the groundwater table of 0.47 feet per year, compared to 0.48 feet per year observed on average across the HPA during this 1985–2005 period. The observed distribution and predicted change across counties is in the (?3.22, 1.59) and (?2.24, 0.60) feet per year range, respectively. The estimated impact of irrigation is to decrease the water table by an average of 1.24 feet per year, whereas rainfall recharges the level by an average of 0.76 feet per year. Relative to the past several decades, if groundwater use is unconstrained, groundwater depletion would increase 50% in a scenario where precipitation falls by 25% and the number of degree days above 36°C doubles. Editor’s note : This paper is part of the featured series on Optimizing Ogallala Aquifer Water Use to Sustain Food Systems. See the February 2019 issue for the introduction and background to the series.