decreasing climate-induced water supply risk through improved
TRANSCRIPT
Sectoral Applications Research Program
Climate Program Office
Oceanic and Atmospheric Research
National Oceanic and Atmospheric
Administration
American Water Works Association
Kearns & West
George Washington University
University of Colorado-Boulder
Hazen and Sawyer
July 2013
Decreasing Climate-Induced Water Supply Risk Through Improved Municipal Water Demand Forecasting
i
Table of Contents
Acknowledgements ...................................................................................................................................... iii
I. Executive Summary ................................................................................................................................... 1
II. Introduction and Overview ....................................................................................................................... 4
Forecasting and Uncertainty ..................................................................................................................... 5
Climate Change as a Source of Forecast Uncertainty ............................................................................... 6
III. Reasons to Examine Water Demand ....................................................................................................... 9
Background ............................................................................................................................................... 9
The Importance of Water Demand for Operations and Planning ............................................................. 9
Role of Demand Forecasting in Management and Planning ................................................................... 10
Implications of Climate-Induced Changes in Demand to Strategic Planning ......................................... 14
Summary ................................................................................................................................................. 15
IV. Project Approach and Methods............................................................................................................. 16
Project Team ........................................................................................................................................... 16
Pre-workshop interviews..................................................................................................................... 16
Surveys and Workshops in Two Regions ............................................................................................... 18
East Coast Focus in Washington, DC ................................................................................................. 18
Midwest/Western Focus in Denver, Colorado .................................................................................... 20
V. Current State of Water Demand Forecasting ......................................................................................... 22
Basics of Water Demand Forecasting ..................................................................................................... 22
Demand Forecasting Methodologies ...................................................................................................... 22
How Factors that Affect Demand Are Addressed in Models ................................................................. 24
How Uncertainty Is Addressed in Models .............................................................................................. 25
Current State of Water Demand Forecasting Models ............................................................................. 26
Summary ................................................................................................................................................. 28
VI. Risks Associated with Models and Methods ........................................................................................ 29
ii
Limitations of Existing Models .............................................................................................................. 29
One Potential Approach to Identify Risks—Extreme Value Analysis ................................................... 30
Summary ................................................................................................................................................. 31
VII. What Utilities Should Be Doing Now ................................................................................................. 32
Collect Additional Weather and Demand Data ....................................................................................... 35
Analyze the Data and Translate It into Actionable Information ............................................................. 38
Evaluate Potential Changes in Demand .................................................................................................. 39
Evaluate Potential Changes in Demographics in the Service Area ......................................................... 43
Understand and Incorporate Uncertainty into Forecasting ..................................................................... 44
Plan for Drought So the System Can Cope ............................................................................................. 47
Summary ................................................................................................................................................. 47
VIII. Recommendations for Future Research ............................................................................................. 49
Understanding Baseline Conditions and Potential Changes ................................................................... 49
Potential Impacts of Demand on Appropriate System Design ................................................................ 50
System Data ............................................................................................................................................ 51
System Revenues .................................................................................................................................... 52
Data and Research Integration ................................................................................................................ 52
Historical Drought/Water Shortage Analyses ......................................................................................... 52
Value of Information Studies .................................................................................................................. 53
Social Science Research ......................................................................................................................... 53
Tools for Investment Decisions .............................................................................................................. 53
Summary ................................................................................................................................................. 54
IX. Summary and Conclusions ................................................................................................................... 55
X. References .............................................................................................................................................. 57
iii
Acknowledgements
We would like to acknowledge NOAA for providing the funding for this project and we would
like to thank Nancy Beller-Simms from NOAA for her support. We would also like to thank the
participants in the two project workshops and the five webinars for their time and expertise:
Washington, DC, Workshop Denver, CO, Workshop
Alison Adams, Tampa Bay Water Sarah Deslauriers, Carollo
Veronica Blette, EPA Ben Dziegelewski, University of Illinois
Erica Brown, AMWA Rick Holmes, SNWA
Jim Chelius, American Water Pam Kenel, Black & Veatch
Roger Cooke, Resources for the Future Alfredo Rodriguez, Aurora Water
Pat Davis, OWASA Sean Senascall, Tacoma Water
Bill Davis, CDMSmith Lorna Stickel, Portland Water Bureau
Ron Harris, Newport News Waterworks David Yates, NOAA-NCAR
Rick Palmer, University of Massachusetts
Paul Peterson, Arcadis
Tom Rockaway, University of Louisville
Thurlough Smyth, New York City DEP
Roland Steiner, WSSC
Jennifer Warner, Water Research Foundation
Doug Yoder, Miami Dade Water and Sewer
Project Team
Alan Roberson and Craig Aubuchon, American Water Works Association
Abby Arnold, Elana Kimbrell, and Dani Ravich, Kearns & West
Emmanuel Donkor, Refik Soyer, and Tom Mazzuchi, GWU
Erik Haagenson, Balaji Rajagopalan, and Scott Summers, CU
Jack Kiefer, Hazen and Sawyer
1
I. Executive Summary
Water demand forecasts are critical tools for water system managers
and planners. Water system managers (and their planning and
engineering staff) have to contend with many uncertainties in
planning, designing, and operating a water system to meet
customers’ demands. Water demand and the resultant water sales
generate the revenues that are the economic engine for any water
system, whether large or small, urban or rural. Since accurate water
demand forecasting is inextricably linked to a water system’s
finances and the system’s long-term sustainability, the financial
implications of water demand forecasting to a system are significant.
Accurate water demand forecasting depends on a variety of factors. Weather conditions,
economic/business cycles, and new connections or the loss of a large industrial/commercial
customer can affect short-term demands that impact day-to-day system operations. Over longer
terms, other factors can influence demand, including trends in population, housing, density of
land use, employment, mix of industries, water efficiency and conservation programs, and
climate change and variability.
The focus of this project was to develop a better understanding of the potential risk posed by
climate change and variability to demand forecasting for a water system; and then, to develop
recommendations to help reduce climate-induced risk arising from inaccurate forecasts. What
has been realized, however, is that long-term weather trends, caused by climate change and/or
weather cycles, are only part of the picture, and water system planners, engineers, management,
and water boards must invest adequate time and resources to understand all the factors that
influence water demand forecasts, and the interplay between them. Moreover, the
interrelationship between the different factors is system-specific; therefore, stakeholders of each
water system need to develop a better understanding of the appropriate factors for their system.
Although there is no simple answer and no clear path to reducing climate-induced risk in
developing demand forecasts for water systems, this project yielded several recommendations
2
that water systems can implement now in order to develop a better understanding of climate-
induced risk in water demand forecasting. Many of these recommendations involve improved
collection and analysis of typical water system demand data. Some of the recommendations are
time and resource intensive while other are less so. The recommendations fall into six general
categories that need further investigation by water system managers to determine how to
appropriately implement them in their systems, noting that many system-specific factors would
impact implementation:
1. Collect additional weather and demand data.
2. Analyze the data and translate it into actionable information.
3. Evaluate potential changes in demand.
4. Evaluate potential changes in demographics in the service area.
5. Understand and incorporate uncertainty into forecasting.
6. Plan for drought so that the system can cope with it.
A water system manager faces many competing priorities in operating and managing a water
system, including managing the system finances and optimizing future capital investments. The
above recommendations offer a starting point for the water system manager when considering
the investment of time and resources necessary to improve and optimize a system’s long-term
water demand forecast.
Many utilities today still develop demand forecasts using the simple product of estimated per-
capita demand and a projection of population—known as the ―per-capita‖ or ―gpcd‖ method.
However, increasingly complex constraints in source water availability, variability in water sales
and revenues, and concerns about climate change and other emerging uncertainties, have led to
more emphasis on evaluating, understanding, and modeling the factors that influence water use
over both short-term and long-term intervals. In particular, traditional per-capita approaches to
forecasting water demand neglect—and are incapable of—measuring the effects of principal
factors that can produce variability in water use, such as weather and climate, the price of water,
land use, and several socioeconomic variables other than population. Additionally, the observed
reductions in per-capita use, for example, due to increases in water efficiency, the effects of
pricing, and recessionary economic pressures have been largely unanticipated by some systems.
3
While few could predict the impacts of the latest recession, reliance on simple forecasting
methods is partly to blame for some systems not being adequately prepared for decreases in
water sales and revenues.
Long-term water demand forecasts have traditionally
assumed long-term normal weather (or stationary
mean) patterns for future forecast scenarios. However,
if ―stationarity is dead,‖ (i.e., past weather patterns
may not be the same in the future), then climate change
may be particularly problematic for long-term water
demand forecasts (Milly 2008). New and ―drifting‖
climate regimes may lead to changes in outdoor
watering patterns that would ultimately impact water demand, as well as other structural shifts in
water use. Along the way, greater variability in precipitation and temperature in the future may
also increase demand uncertainty over short-term planning horizons.
Improving forecasts is critical to optimizing future investments, as most water systems are faced
with a myriad of investment decisions, ranging from finding new water sources to supply a
growing population and economic growth; to additional treatment to comply with new
regulations; to rehabilitating and/or replacing distribution system pipes that have reached the end
of their useful life. Water system managers need to make the investment in developing a better
understanding of the potential risk posed by climate change and variability in order to improve
demand forecasting for their systems so that future capital investments will be optimized.
“If ‘stationarity is dead,’
then climate change may
be particularly
problematic for long-term
water demand forecasts”
4
II. Introduction and Overview
The basic mission of a water utility is to meet the water demands of a community. Water demand
and water sales are the primary source of a utility’s revenue. As such, water demand and the
resultant revenues serve as the economic engine for a water system. Expectations, or forecasts, of
water demand therefore play a crucial role in a system’s financing, as well as its short- and long-
term operations. But a water utility’s role has a much greater impact on the community it serves
in that the benefits of water service are fundamental for the community’s well-being and its long-
term viability. Those benefits include protection of public health and safety, quality of life, and
economic viability. In short, water demand forecasting also affects a community’s long-term
viability, so it must be done accurately.
Every year, a water system planner predicts how much water its customers will buy (water sales)
and subsequently predicts the resultant revenues as part of the annual budgeting process. The
financial implications of this prediction are felt every year and a system planner finds out
throughout the year the accuracy of the predictions. Lower-than-expected water sales result in
lower-than-expected revenues and vice versa. Many water systems are seeing declining water
sales and revenues in the face of rising operation and maintenance costs, especially for
infrastructure repair and replacement (AWE 2012). Persistent errors in short-term forecasts and
revenue instabilities can result in the need to make frequent changes in rates and can also affect
bond ratings and a utility’s ability/cost to borrow.
Forecasting water demands over medium and longer terms is critical for developing and funding
Capital Improvement Plans (CIPs) and for planning future investments in system infrastructure.
Errors and inaccuracies in long-term forecasts can result in large costs to utilities and rate payers,
in the form of investments in stranded capital assets, insufficient supply reliability, or a reduced
level of service due to supply and/or system capacity limitations. In some cases, state regulatory
agencies could end up making decisions on future supply potentials that could significantly
impact a system’s supply portfolio. In some private water rights cases, exactly who ends up
holding the ―empty bucket‖ is only likely to be resolved in court. These types of problems can
take considerable time and resources to resolve.
5
Because of these implications, system managers should be thoughtful about evaluating demand
and invest more time and resources for the development of additional technical capacity (both
internal and external) for developing more accurate demand forecasts for both the short-term and
the long-term.
Forecasting and Uncertainty
Forecasts of water demand depend on a number of
factors that are assumed to influence water use. If a
forecast is based on a mathematical model, this means
that a predicted value of demand is some function of
predicted future values of important variables, such as
population or job creation.
Forecasts can be inaccurate for a number of reasons.
The two most fundamental reasons involve errors in
assumptions of the forecasting model and errors in predicting the future values of variables
contained in the forecasting model. For example, if future water demand is assumed to be a
function solely of population, but other factors influence water use, then even a perfect
prediction of future population will not result in an accurate forecast. On the other hand, if it
turns out that demand really does depend only on population, then an imperfect prediction of
future population will also lead to inaccuracies in forecasting water use.
In reality, both of these sources of uncertainty are likely to occur, which typically makes perfect
forecast accuracy unattainable (except by chance). Making predictions about the future always
involves uncertainties—the key is identifying and understanding the sources of those
uncertainties. This amounts to learning more about why water demands vary, making
improvements in how forecasting models are designed, and identifying the best ways to portray
forecasts that are inherently uncertain.
“Making predictions about
the future always involves
uncertainties—the key is
identifying and
understanding the sources
of those uncertainties.”
6
Climate Change as a Source of Forecast Uncertainty
Historically, several factors and trends have made forecasting water demands difficult: increased
indoor plumbing efficiency; economic boom and bust cycles; fluctuations in prices for land and
housing; new and emerging industries; evolving water-use attitudes; and normal day-to-day and
month-to-month variability in weather. Looking forward, another complicating factor will likely
be climate change and/or weather extremes.
One only needs to review the weather statistics for 2012 to see examples of climate change and
extreme weather patterns. The year 2012 was the warmest of any year in the 1895-2012 period of
record for the United States (NCDC 2012). Every state had above-average annual temperatures
and 19 states had record warm annual averages. Much of the United States was drier than
average for 2012. The area of drought in the United States during 2012 roughly equaled the
drought of the 1950s. The drought peaked in July, when according to the Palmer Drought
Severity Index (PDSI), 61.8 percent of the United States was in moderate drought.
The 2013 Draft Climate Assessment Report from the National Climate
Assessment and Development Advisory Committee (NCADAC) provides
some insight into future weather conditions (NCADAC 2013a). In this
report, the NCADAC concludes that U.S. temperatures will continue to
rise, with an increase of 2o – 4
oF predicted for most areas. The report also
concludes that the chances of record-breaking high temperature extremes
will continue to increase as the climate continues to change.
For water resources and precipitation, the NCADAC concluded that precipitation and runoff
increases have been observed in the Midwest and the Northeast and are predicted to continue or
develop in the northern states (NCADAC 2013b). Parallel decreases have been observed and are
projected to continue in the southern states. Droughts are predicted to intensify in most of the
United States, with long-term reductions in water resources in the Southwest, Southeast, and
Hawaii in response to both rising temperatures and changes in precipitation.
7
Changes in both average and extreme temperatures will impact demand forecasting. Long-term
water demand forecasts have traditionally assumed long-term normal weather (or stationary
mean) patterns for future forecast scenarios. If, in fact, ―stationarity is dead‖ as previously
mentioned, then climate change may be particularly problematic for long-term water demand
forecasts. New and ―drifting‖ climate regimes may lead to long-term changes in outdoor
watering patterns that would ultimately impact water demand, as well as other structural shifts in
water use. Along the way, greater variability in precipitation and temperature in the future may
also increase demand uncertainty over short-term planning horizons.
However, for water utilities, it should be noted that continued data collection and long-term
statistics are needed before a change can be demonstrated to be attributable to climate. For some
data elements such as stream flow and temperature, long-term datasets are available, but are not
available for other data elements. It should also be noted that the U.S. Geologic Survey (USGS)
is under significant budget pressures (like all federal agencies) and maintaining its existing
network of stream gages in the future will be challenging. More data collection, research, and
statistics are needed to better understand the relationship between climate and weather at a
watershed level to factor into water resource planning and water demand forecasting.
The range of possible future climate conditions and weather extremes is uncertain at this time.
However, it is prudent to evaluate potential changes to future water demand and demand
forecasts that may be attributable to differences in future climate.
Goals and Objectives
The goal of this report is to improve water demand forecasting by increasing the awareness of
water system managers and demand forecasters to the potential implications of climate change
for water demand forecasting. This report is not intended to resolve the debates surrounding
climate change and all of the potential implications of climate change for water systems. Rather,
the objectives of this research were to:
Conduct a literature search and review of the existing research on water demand
Conduct case studies using extreme value analysis on the potential impacts of climate
change to water demand at two water systems (Aurora, Colorado, and Tampa, Florida)
Identify knowledge gaps and research needs related to demand forecasting
8
Develop a list of recommendations for what water system managers should be doing now
to improve their own demand forecasts
Conduct outreach to water systems on the need to improve their own demand forecasts
In order to address the above objectives, this report is organized as follows:
Section III examines the primary reasons to look at water demand (or why improving
demand forecasts is important) and addresses the implications of climate change on
water demand.
Section IV details the project approach and research methods for conducting outreach
on demand issues to water system managers and experts.
Section V presents a review of the current state of water demand forecasting, existing
research, and example approaches.
Section VI provides results of the Aurora and case study, and examines risks
associated with model methods and climate change.
Section VII summarizes what systems are doing now and what they can be doing
better to improve their own demand models.
Section VIII provides recommendations for future research.
Section IX provides a summary and conclusions for this research.
Section X lists the references used in this report.
9
III. Reasons to Examine Water Demand
Background
Water system managers and planners face a myriad of competing issues (AWWA 2013), such as:
Rehabilitating or replacing infrastructure
Lack of public understanding of the value of water
Capital costs and availability
Water supply and scarcity
Aging workforce/talent attraction and retention
Regulation and government oversight
Water security and emergency preparedness
Climate risk and resiliency
Managing finances and optimizing investments are two critical priorities for all water systems.
All water system managers need to balance the short-term delivery needs of their customers with
the long-term planning necessary to build and maintain required infrastructure, including
adequate source waters and treatment and storage capacity to meet increasing future demands.
This section provides some reasoning and justification as to why system managers should be
thoughtful and deliberate in evaluating demand and why more time and resources should be
invested in order to develop more technical capacity for deriving more accurate demand
forecasts. The section also provides an overview of the importance of demand for system
operations and planning and how demand forecasting is used (or should be used) in the strategic
planning of water systems.
The Importance of Water Demand for Operations and Planning
Water demand and resultant water sales represent a water system’s economic engine. Demand
projections form the basis of several complex financial and strategic decisions. Those that link to
capital investments (noting that daily and seasonal projections are needed for water system
operations) are usually prepared for the short term (one to five years) and the long term (15+
10
years), but each projection has a different use and each may be performed by a separate group or
division of water system staff.
For example, water system staff typically develop an
operating budget for the upcoming year or financial planning
period (usually five to ten years), and then this budget is
refined by management and ultimately approved by the water
system’s governing body. Inherent to the development of
these budgets is a projection of water demand that translates
into a projection of gross revenues, and, in conjunction with utility costs, projections of net
revenue requirements. Additionally, if a system has sold bonds to pay for capital improvements,
short-term demand forecasts are important for predicting the bond coverage ratio for future years
for the system. For many water systems, meeting a specific bond coverage ratio is an important
financial and strategic goal.
Meanwhile, water demand forecasts also drive long-term investment and planning strategies,
which may be derived from asset management plans, and are commonly expressed in capital
investment plans, system master plans, and/or urban water management plans. These plans
typically require a longer view on the adequacy and reliability of the water system. These plans
also detail when the next new source of water supply and/or the next increment of treatment
capacity might be needed in the future.
Role of Demand Forecasting in Management and Planning
Demand projections are one of the primary methods by which most water system managers
attempt to align short-term and long-term priorities and objectives. Several demand forecasting
methods are available, which vary in complexity and data requirements. Adopted techniques will
typically differ from utility to utility depending on a host of factors, including the adequacy of
existing supplies, the diversity of the customer base, internal technical capabilities, and
availability of data upon which to build forecast assumptions (Kiefer 2006).
11
Traditionally, water demand forecasts have been prepared using relatively simple methods, such
as taking the simple product of an estimate of the per capita demand and a projection of
population—known as the ―per-capita‖ or ―gpcd‖ method. In some areas, water systems are
required to use this method for demand forecasting
(SFWMD 2012). In South Florida, the average per-
capita daily use is calculated for the last five years or
period of record. This method of calculation is
adequate for gradual decreases in per capita demand but
may not adequately account for more rapid decreases.
Additionally, increasingly complex constraints in
source water availability, financial capacity, and
concerns about climate change and other emerging uncertainties, have led to more emphasis on
evaluating, understanding, and modeling the factors that influence water use over short-term and
long-term horizons. In particular, traditional per-capita approaches to forecasting water demand
neglect—and are incapable of—measuring the effects of factors that can produce variability in
water use, such as weather and climate, the price of water, land use, and several other
socioeconomic variables other than population. In fact, observed reductions in per-capita use—
for example, due to increases in water efficiency, the effects of pricing, and recessionary
pressures—have been seen by many systems over the past 30 years (Rockaway et al. 2011). In
some cases, systems have modified their rate structures to account for declining water use;
however, in some cases, the decline was not anticipated by some systems. Reliance on simple
forecasting methods is partly to blame. Addressing factors that influence demand in the demand
forecasting process permits a means in which to evaluate and consider future demand in the
context of long-term investment and planning strategies.
Water demand is inherently difficult to forecast because water is a complex, multidimensional
commodity that operates in legal, economic, and hydrologic dimensions (Olmstead 2010).
Water is used for a variety of purposes. Some purposes are essential for public health, like
drinking, cooking, and bathing. Other uses include water for cooling, irrigation, production of
goods and services, and aesthetic purposes. Demand patterns vary significantly on a daily,
“Water demand is
inherently difficult to
forecast because water is
a complex,
multidimensional
commodity.”
12
weekly, and seasonal basis. Water treatment and distribution systems must be designed to meet
peak demands during the height of summer irrigation season (for most of the United States),
while having adequate sources to meet average annual demands. Factors that can affect short-
term demands and demand forecasts include:
Weather conditions and extremes
Restrictions on outdoor use due to water shortage
Economic/business cycles (such as recessions)
New connections or the loss of a particular customer (particularly a commercial or
industrial customer that is a large water user, for example, a factory shuts down)
Over longer time horizons, several factors can influence demand, including trends in:
Population
Housing and housing mix (e.g., single-family detached homes versus multifamily
developments)
Density of land use and lot sizes
Employment and mix of industries
Disposable incomes and economic output
Price of water and sewer service
Water efficiency and conservation
programs
Re-use of treated wastewater
Climate change and variability
Climate change represents a relatively new source of uncertainty in the planning process for
many water systems. To date, the understanding of the potential impacts of climate change on
source waters and watersheds, public health, and infrastructure investments is evolving (Means
et al. 2010, AWWA Climate Change Committee 2011). These analyses have generally
considered the impacts of climate change on water supply and show that climate change is
emerging as an additional consideration in the planning and design of water infrastructure.
However, from a planning perspective, water supply and water demand represent the two sides
“Climate change
represents a relatively
new source of uncertainty
in the planning process for
many water systems.”
13
of the water budget and changes in either demand or supply influence the strategic planning for
the other.
One of the difficulties in incorporating climate change into demand forecasts is a clear
understanding of time frames in forecasting and the distinction between weather variability and
climate change. Put succinctly, ―Climate is what you expect, weather is what you get‖ (Miller
and Yates 2006). Water managers have historically dealt with weather variability in short-term
operations through the use of a safety factor or supply buffer to deal with potential drought or
increased demand.
However, over the long-term, excess capacity may not be available to provide sufficient buffers,
or stranded capacity may lead to financial challenges in meeting debt payments and the ability to
maintain high bond ratings. It is no surprise then, that a recent study on the impacts of climate
change on infrastructure planning and design found that ―most [water systems] anticipate
needing to make changes to their demand forecasting modeling‖ (Means et al. 2010). Figure 1
conceptualizes a few of the climate-induced changes in demand along the two dimensions of
time and water system orientation.
Figure 1. Climate-Induced Changes in Demand. Source: AWWA
Short Term Long Term
Inter
(within
watershed)
Intra
(within water
system)
Water Rights and
Demand:
Minimum In-Stream
Flows
Agriculture Industry
Power Generation
Seasonal Peak
(summer irrigation)
Drought Restrictions
Land Use Patterns:
Residential
Industrial
Environmental
Source Water Supply:
Quality
Quantity
End Use Technologies
Demographics/
Population
14
Figure 2, reprinted from the Water Services Association of Australia (WSAA), illustrates several
of the direct and indirect factors that influence water demand and demand forecasting.
Figure 2. Factors Influencing Water Demand. Source: Original Figure from Water Services
Association of Australia Occasional Paper No. 9 – Urban Water Demand Forecasting and
Demand Management.
Implications of Climate-Induced Changes in Demand to Strategic Planning
Climate change will likely exacerbate existing pressures and situations of supply and/or demand
stress (Kiefer et al. 2013 forthcoming). For example, regions that are expected to receive less
precipitation and experience warmer temperatures could see a lengthening of the irrigation
season and higher summer peaking factors. Coupled with population growth, some urban water
systems may experience more frequent regional conflicts involving competing demands from
agriculture, power production, and in-stream uses of water. Anticipating these possible changes
will be important for designing long-term adaptive strategies. Thus, demand forecasts and the
informational characteristics of demand models will become even more important from a
strategic perspective.
The Water Utility Climate Alliance (WUCA), a committee of ten large water systems, published
a white paper in 2010 on Decision Support Planning Methods (DSPMs) for water systems and
how to incorporate climate change uncertainties into long-term planning (WUCA 2010). This
15
white paper presents five distinct DSPMs, with a special
emphasis on the availability and familiarity of traditional
scenario planning. Scenario planning allows a water
system manager to better understand the risk and
exposure of potential investment decisions to different
scenarios based on changes in climate, demand, or
business conditions. By incorporating climate change
into demand projections, and allowing for both micro
and macro dynamic feedback from climate change on
land use, population growth, and supply availability, water managers can more accurately
develop potential scenarios that model the potential variability in demand. For example, one of
the earliest demand forecasts to include both conservation and climate scenarios found that in the
Washington, DC, metro area, future climate-induced demand could likely be offset by
conservation programs including regulatory policies for appliance efficiency and appropriate
pricing signals (Boland 1997). By more explicitly dealing with future uncertainties, this type of
scenario planning will allow water systems to more appropriately justify infrastructure
investments, implement adaptation practices, and generate political support when discussing rate
and cost of service studies.
Summary
Water system managers and staff need to understand both their current demand and their future
demand predictions. All water systems need to balance the short-term delivery needs of their
customers with the long-term planning necessary to build, finance, and maintain the required
infrastructure, ranging from source waters, treatment plants, transmission mains, distribution
system pipes, storage tanks, booster pumping stations, and any other needs. Accurate water
demand forecasts are critical for short-term and long-term service and financial sustainability of
any water system.
“Accurate water demand
forecasts are critical for
short-term and long-term
service and financial
sustainability of any water
system.”
16
IV. Project Approach and Methods
The American Water Works Association (AWWA) was the lead organization for this project.
Established in 1881, AWWA is the oldest and largest nonprofit, scientific, and educational
association dedicated to safe and sustainable water in the world. With more than 50,000
members worldwide and 43 sections in North America, AWWA advances public health, safety,
and welfare by uniting the efforts of the entire water community.
As a member-driven association, AWWA drew on the expertise of its members to provide a
diversity of perspectives on water demand forecasting and the potential implications of climate
change. The sections below describe the project team and the process used to solicit and obtain
input related to water demand and water demand forecasting.
Project Team
A multi-disciplinary team was assembled for this project. Kearns & West (K&W), a firm
specializing in stakeholder engagement, assisted in planning the two project workshops in 2011
by conducting surveys to workshop attendees to develop the priority demand/climate issues.
K&W also facilitated the two project workshops and additional follow-up webinars to continue
solicitation of expert judgment on the project report outline and drafts of the project report.
George Washington University (GWU) conducted a literature search and a review of the existing
research and recent studies on water demand. University of Colorado-Boulder (CU) conducted
two case studies using extreme value analysis on the potential impacts of climate change to water
demand at two water systems (Aurora, Colorado, and Tampa, Florida). These case studies are
intended as examples to guide other utilities in conducting similar extreme value analysis to feed
into their improved water demand forecasts.
Hazen and Sawyer, a multi-disciplinary engineering consultant, provided technical information
on water demand forecasting, and provided additional information during the editing of this
report.
17
Pre-workshop interviews
AWWA engaged a broad range of stakeholders throughout the study. The stakeholder input
helped inform the study of issues to be addressed in the topic of water demand and to develop
recommendations on how to address these issues. AWWA, working with K&W, conducted
interviews with representatives from key stakeholder groups prior to the two workshops in 2011
and designed the workshops based on stakeholder input. Additionally, in order to inform the
discussion at the two workshops, AWWA and K&W conducted an electronic survey of
workshop participants. Below is a summary of the interviews, the survey results, and the
workshops.
K&W interviewed eight experts, representing consultants
(3), academics (4), and utilities (1). The experts were
interviewed on a number of topics including their area(s) of
expertise, key themes related to the current state of
modeling, suggested topics to consider during the
workshops, and recommendations for additional participants.
From the interviews, K&W found that overall, the quality of water demand forecasting models is
highly dependent on funding and data availability. Lack of data in a consistent format from
different sources and different data systems is a significant obstacle to developing a useful model
for a utility, as well as different data formats hindering the development of national-scale
models. Many existing models are not able to adequately define important variables and/or
disaggregate water use spatially or by sector (e.g., by region, family versus multifamily,
commercial versus industrial). Also, some utilities do not use mathematical or statistical models
because they do not see the need, do not have staff trained to use them, and/or data is not readily
available in a format that is easy to model.
In general, the interviews suggest there is a need for standardized data and possibly a central
portal for data access, as well as models that account for climate change (using regional weather
patterns). More modeling expertise and funding is needed, as well as the inclusion of behavioral
variables in demand forecasting, and a better understanding of industrial water use.
18
The interviews indicated that increasingly, larger utilities are seeking to model socioeconomic
and demographic variables as drivers in their demand forecasts. However, the majority of
utilities, especially the smaller utilities, still focus on cost as the main driver underlying the
characteristics of their demand models and demand forecasting approaches.
Surveys and Workshops in Two Regions
K&W assisted AWWA in hosting two workshops, one with an East Coast focus and the other
with a Midwest/Western focus, to help understand respective concerns and recommendations,
and how they differ from each other.
East Coast Focus in Washington, DC
The first workshop was held on March 30, 2011, in Washington, DC. The goal of the workshop
was to initiate an interactive dialogue, and to give presentations on methodologies used in
forecasting water demand and climate change. Prior to the workshop, K&W sent a ten-question
online survey to participants, to generate thinking and help inform discussion at the workshops.
There were 14 respondents to the survey sent in advance of the East Coast meeting. Participants
in the survey and workshop were primarily from the Washington, DC, area, Florida,
Massachusetts, and New York. The following summarizes the results of the East Coast survey,
and then identifies the objectives of the East Coast workshop and some of the main discussion
points and recommendations.
Pre-Workshop Survey
Participants of the East Coast survey indicated that the main reasons people use water demand
forecasts are for water conservation, revenue forecasts, long-term supply planning, regulatory
planning, and infrastructure planning. The climate-related concerns raised were based on
potential changes in water flow, duration, and intensity. The survey results indicated concern
about extreme events, timing of floods and drought, changes in stream flow, sea-level rise, and
changing consumption patterns. Forecasts in the East generally use population demographics
combined with per-capita data, based on billing information. The results from the East Coast
survey indicated that sophisticated models are not widely used, and historical climate data is
19
used minimally by the majority of respondents. The water demand data that people are most
interested in are peak daily demand and total annual demand. The survey found that most people
wanted to attend the workshops to learn new methodologies and best practices for improving
their water demand forecasting capabilities, including how to factor in climate change.
Workshop
The following topics and recommendations were discussed at the March 30, 2011, workshop in
Washington, DC:
1. Review existing research on water demand related to climate change
Presentations were given on related topics
2. Discuss current models’ strengths and weaknesses
It was suggested that models break down data by customer type, and account for
population densities
3. Identify knowledge gaps and list future research topics
Share best practices for data collection and model methodologies; standardize the data
collection process; and develop data templates
Study the effects on water demand of new water efficient fixtures; utility water use
(such as continuously running water through pipes to prevent freezing); reuse of water;
and, green building practices
Predict demographic and behavioral responses to climate change (rising sea levels)
Consider utility zoning
Include population density in models
Look to the financial and insurance industries/communities for lessons learned about
diversification and risk
Partner with energy utilities
4. Develop recommendations for how water utilities can reduce the uncertainties in water
demand forecasting
Better acknowledge and communicate the possibility of error and the confidence
intervals to decision makers
20
Focus on how to incorporate climate change into a forecast; potentially compare
climate change models to both seasonal forecasts and longer term supply forecasts
Midwest/Western Focus in Denver, Colorado
The second workshop, held on July 12, 2011, in Denver, Colorado, was focused around the same
main topics as the workshop held on the East Coast and many of the same issues and
recommendations were discussed.
Pre-Workshop Survey
The Midwest/Western survey sent in advance of the July workshop had nine respondents, who
primarily reside in the intermountain west. Participants stated that their main reasons for using
water demand forecasts were for long-term and short-term planning, which encapsulate many of
the same purposes noted in the East Coast survey. In the West, complex models seem to be used
more frequently, and historical climate data more commonly incorporated, in order to
characterize and model drought cycles and decreasing stream flows. Climate concerns included
extreme events, stormwater runoff, and effects of precipitation on infrastructure. The water
demand data that people are most interested in are total annual demand, followed by peak daily
demand. The other results of the survey were largely the same, including the respondents’ goals
for the workshop.
Workshop
Additional notes from the July 12, 2011, workshop included the following:
1. Climate change models are often too complex to be useful for decision making; there is a
need to develop ―actionable science‖
2. Develop recommendations for utilities based on a ―profile type,‖ depending on their
local/regional issues, their size, funding, etc.
3. Consider alternative water sources
4. Improve tools to analyze data
5. Assess rate structures and their relationships with demand
21
After the two workshops, a series of meetings were held via webinars to review the draft report
sections on the priority research plan and the recommendations for water systems. These
webinars were held in October and December of 2011, and in January, April, and May of 2012.
Through an interactive format, these webinars provided a mechanism for the workshop attendees
to provide additional input on the recommendations for what utilities should be doing now to
improve water demand forecasting and on recommendations for future research. Additional
input was also given through reviews of the draft report.
22
V. Current State of Water Demand Forecasting
Basics of Water Demand Forecasting
There are a number of ways to forecast water demand, which vary in analytical rigor and
requirements for data. In the most general sense, the intent of a demand forecast is to make a
prediction of future water use. However, the actual dimensions of the problem can be numerous
and more complex depending on considerations related to agent or purpose specificity, temporal
scale, and spatial extent (Kiefer et al. 2013). In other words, how water use is defined (e.g., total
use in a service area, water use of particular user types or sectors or geographic areas, annual,
monthly, or seasonal demand) will influence the choice among alternative demand modeling and
forecasting methods.
At a fundamental level, a demand forecast represents a set of calculations, which defines a
formula and embodies a set of assumptions. In short hand, one can generalize the set of
calculations symbolically as a function, Q=f (X), where Q is the measure of water demand to be
forecasted, X represents a set of factors that are part of the calculation and thus influence the
forecast, and the term f(*) defines mathematically how X relates to Q. Therefore, future or
forecasted values of Q are a function and conditioned on future or forecasted values of X.
Unfortunately, and in the case of most situations that involve human preferences and choices,
neither the ―true‖ nature of the dependencies on X are seldom known with certainty nor is the
proper definition of X. Even if one is skilled or lucky enough to have f(*) defined properly, then
one must have confidence in forecasts of X to have confidence in the forecast of Q. Limitations
on available data, less than perfect knowledge of underlying relationships, and inherent
uncertainty about the future make water demand forecasting both an art and a science.
Demand Forecasting Methodologies
Based on reviews of contemporary demand forecasting approaches found in Kiefer (2006) and
Billings and Jones (2008), one may classify demand forecasting into several different categories.
The Aggregate Per Capita Approach is a traditional approach to water demand forecasting that
relies exclusively on population projections. The aggregate per capita approach assumes a fixed
23
Q = f(X)
value of water use per person (per-capita consumption) and multiplies this value by population to
calculate a forecast. (Using the formula previously discussed, the definition of X includes
population, an estimate of water use per person, and f(*) represents a simple multiplication of
these terms.)
Other Fixed Unit Use Coefficient methods define other fixed water use factors and drivers of
demand (other than population) to prepare a forecast. Examples of these methods include the use
of water use per acre coefficients and
projections of future developed acres, water
use per residential housing unit coefficient and
projections of future housing units, and water
use per employee coefficients and
projections of future employment. Many of these types of forecasts rely on disaggregation of
water use into user sectors and seasons, which may improve the informational qualities of a
forecast relative to the per-capita method. However, similar to the per capita method condition,
the demand forecast relies solely on counts of users or related proxies.
Time-Series and Trend-Based Models predict future water demand based on assignment of trend
parameters or statistical (autoregressive) relationships that link past values and systematic
repeating cycles of demand to future values of demand. Using the conceptual example above,
past values of Q are used to predict future values of Q, and the function f(*) defines how past
values relate to current and future values. Time-series models tend to be used to predict demands
over relatively short timeframes when longer-term influences may not be as significant.
Regression and Econometric Models are statistical models that explicitly estimate the parameters
of a function that relates changes in defined explanatory variables (X) to changes in water use.
This class of models uses cause-effect relationships among water use and specified factors that
affect water use to forecast demand. Econometric models can be considered a class of regression
models that specify variables, such as price and income, which according to economic theory,
would be expected to influence consumption.
End-Use Models account for and forecast water used by specific water-using fixtures,
appliances, or for specific purposes. In most cases, end-use models represent an accounting
24
structure that is dependent on assumptions for water-using technologies, market saturation of
various water-using technologies, and behavioral factors, such as frequency of use. Some end-
use models are developed using regression analysis that specifies factors correlated with end-use
consumption (e.g., see Mayer et al. 1999). These types of models are well-suited for examining
the effects of increasing water efficiency through time, which result from plumbing standards
and codes and water utility conservation programs.
There can be considerable overlap among some of the forecasting approaches classified above,
particularly with regard to disaggregation of water use sectors, as well as hybrid models that
blend the features of different techniques. For example, unit use coefficients may be scaled
according to information and parameters obtained from the literature or by means of separate
regression models (sometimes called variable forecast factor approaches). Time series models
may also be blended with regression models to create forecasts based on past values of
consumption and exogenous factors. Furthermore, outputs from end-use models are sometimes
used to adjust the results of forecasts derived from other methods in order to account for
predictions of future water efficiency. Finally, traditionally less conventional methods, such as
artificial neural networks, are being used more often to model and characterize (or learn) patterns
of water consumption, which may hold some promise for demand forecasting.
How Factors that Affect Demand Are Addressed in Models
Some of the key factors that affect water demand were previously described in Section III. By
construction, different models will have different capabilities for addressing these factors or will
address them differently. For example, by relying only on population, the per-capita approach
cannot directly address factors other than population that influence water demand, and cannot
recognize differences in water use patterns across water use sectors. Sector-based fixed
coefficient methods may provide additional information on the structure of underlying demands,
but will generally also lack the ability to test and specify the effects of other factors, especially
economic and climatic factors.
Regression model and econometric approaches are able to incorporate multiple variables to
explain and predict water demand. However, the degree to which the multiple factors that
influence water use are specified depends on a host of modeling considerations, including but not
25
limited to the availability of historical data to estimate numerical relationships and the existence
of projection data to effectively use these factors for the purposes of forecasting. Nevertheless,
predictive model-based approaches to water demand forecasting would seem to be the most ideal
for evaluating the potential effects of climate change. Notwithstanding data constraints, they are
capable of directly incorporating principal indicators of weather and climate, which can be used
to assess alternative scenarios. Disaggregation of data into water use sectors and time periods
further augments the capability to analyze climate change by providing an opportunity to isolate
impacts on the underlying components of water use.
How Uncertainty Is Addressed in Models
Water demand forecasting involves inherent uncertainties. As suggested in earlier sections, there
is always incomplete knowledge and understanding of the determinants of water use and how
they are best related in a mathematical sense to water use. In addition, future values of important
factors are not known with certainty and/or can be highly variable, which makes it virtually
impossible to achieve 100 percent forecast accuracy. In fact, even if one were to know the future
values of key factors with certainty, a demand forecast is likely to be wrong because of practical
and technical shortcomings related to the model being used. For example, if one were to predict
future population with 100 percent accuracy, the per-capita forecasting method may still produce
an inaccurate forecast because water consumption depends on more than just population. This
example rightly implies that the options available to address uncertainty are also affected by
choices about the design of the forecasting model.
In practice, and depending on the characteristics of the forecasting model, forecast uncertainty is
addressed through the use of scenarios, the application of statistical routines for estimating
forecast error, or both. Scenario analysis is very common, such as using high, medium, and low
population growth to create an envelope of future demands. Other types of scenarios such as hot-
dry, cool-wet weather scenarios are also often used, but require a model or other mechanism to
translate weather into predictions of water use. Oftentimes, extreme scenarios are combined in an
attempt to account for most future demand possibilities.
26
Standard formulae exist to
calculate random and sampling
error associated with ordinary
least squares regression
procedures. However,
computational difficulties have
tended to limit applied uncertainty
analysis to evaluation of
conditional forecast error, which
assumes the model is accurate and
accounts only for uncertainty
about the future values of model
variables. Monte Carlo simulation
methods are sometimes used to
simulate potential values of
independent variables, given some
underlying knowledge or
assumptions regarding the type and shape of their respective distributions, which results in a
range of predicted demands.
Current State of Water Demand Forecasting Models
This section summarizes the results of a literature review on water demand forecasting,
conducted by researchers at The George Washington University (GWU) as part of this project.
The objective of the GWU research was to provide a guide to the literature on improving the
practice of demand forecasting for effective decision making.
GWU conducted a search of the water demand forecasting literature published from 2000 to
2010 and developed a bibliography of 79 papers from a cross-section of peer-reviewed journals.
These papers were then categorized into the appropriate type of model (qualitative extrapolative
methods versus nonparametric). The analysis of these models focused on three questions:
How practical are the models?
Figure 3. Forecast uncertainty can be analyzed using a statistical
demand model and assumptions about the distributions of variables that
affect water demand. Source: construct developed by Jack C. Kiefer.
27
Are the forecasts reliable?
What is the best approach?
These papers were then synthesized in order to identify what the main focus of research has been
and to make proposals on how the practice of water demand forecasting can be improved. The
synthesis found that while a wide variety of methods and models have been used and have
attracted attention, applications of these models differ, depending on the forecast variable, its
periodicity, and the forecast horizon (Donkor et al. 2012).
The analysis found that a shift is ongoing from pure conventional methods to a focus on three
approaches:
1. Scenario-based and Decision Support System (DSS) models: approaches that
accommodate some amount of uncertainty in demand forecasting
2. Comparative assessment of performance between neural nets and conventional methods
3. Recognition of the need to improve forecast accuracy by using hybrid models
The results of the literature search and analysis indicated that it is difficult to answer the question
―Which model is best for water demand forecasting?‖ without specifying the periodicity of the
demand variable. The research found that neural networks and hybrid models are more
appropriate for short-term forecasts; but, for extended ones, where incorporating future scenarios
of a variable might be important, scenario-based and DSS models are more suitable. However,
the use of regression in modeling monthly demand follows the generally held view that short-to-
medium-term demand is typically influenced by weather variables while long-term forecasts are
more determined by socioeconomic factors.
Overall, improving forecast accuracy, accounting for uncertainty in long-term forecasts, and
maintaining system reliability now and in the future seem to have provided the impetus for the
current research in urban water demand forecasting.
28
Summary
There are differences in how water system planners model and forecast demand for water. The
forecasting techniques vary in their sophistication and methodology to account for determinants
of water use. The choice of a particular forecasting methodology is affected by the data used to
model relationships among demand determinants and sector water use. The availability and
quality of data to support the development of models typically serves as a practical constraint on
the options that are applicable for forecasting. Furthermore, the specific goals and objectives of
any particular water demand forecasting effort may not immediately require one to enhance the
prediction and informational capabilities associated with more complex methods. However, the
array of uncertainties facing the water utility industry seems to require an emphasis on better
forecasting and more robust modeling capabilities.
29
VI. Risks Associated with Models and Methods
Any forecast has some chance of being incorrect due to the fact that a forecast is an attempt to
predict the future. Water system planners and managers need to understand and mitigate the risks
of being wrong in either direction when predicting future demands. A demand forecast that turns
out to be high can result in stranded capacity and the
water system paying for debt for facilities that are not
producing the predicted revenues. A demand forecast
that turns out to be low can result in lower than
desirable levels of service, i.e., restrictions that might be
placed on water use might be unpopular with the
system’s customers.
The objective in water demand forecasting is to minimize the risk of being incorrect and to
provide for adaptive management so that the water system can accommodate the range of
potential outcomes and their probabilities of occurrence. Water system planners and managers
now need to incorporate the potential changes in demand from climate change, and incorporate
the uncertainties in future weather predictions with all of the other uncertainties previously
discussed, including population and employment predictions and changes in per-capita demand.
Limitations of Existing Models
As previously discussed, models are highly dependent on the quality of the data used to build
and validate the model. Improving modeling at a water system is an investment decision as it
takes additional resources to go beyond what is already being done. In other words, water system
managers need to evaluate whether the limitations in existing demand models are significant
enough to warrant the additional investment in the collection and analysis of existing data, and in
the development of improved models.
In most cases, the additional investment is justified. As previously discussed, for many water
systems, demand forecasting is simply multiplying the gallons per capita per day (gpcd) by the
projected population growth and job growth. However, the traditional per-capita approaches to
forecasting water demand neglect and are incapable of measuring the effects of principal factors
that can produce variability in water use, such as weather and climate, the price of water, land
“Each water system has a
unique set of data; there is
no single model that can
fit all systems.”
30
use, and several other socioeconomic variables other than population. Past observed reductions
in-per capita use—for example, due to increases in water efficiency, the effects of pricing, and
recessionary pressures—have been largely unanticipated by many systems. Therefore, in many
cases, the additional investment in the collection and analysis of existing data, and in the
development of improved models is warranted to overcome the limitations of existing models.
One Potential Approach to Identify Risks—Extreme Value Analysis
Water system managers and planners are particularly interested in the high-impact, low-
probability water demand events that drive infrastructure investment decisions and the need to
fund such investments. Accurate predictions of peak-day and peak-hour demands are necessary
for planning capital improvements such as alternate sources of supply, treatment plant capacity,
transmission mains, storage tanks, and booster pumping stations.
These events, by definition, are ―extreme events,‖ and one approach that this research found to
model these events is the use of extreme value analysis (EVA). Climate change and more
extreme weather events will likely change the above peak demands, and will need to be
appropriately considered by water systems in future planning, design, and operations of their
systems. EVA has been used in a wide variety of disciplines including the financial industry, the
global reinsurance industry, civil engineering, ecology, water quality, and especially climatology
and hydrology. EVA has been used in hydrology to estimate and forecast flood frequency, model
financial loss related to flooding events, and to model extreme hydrological events in various
watershed sizes, but has seen limited use in the water sector, particularly for demand forecasting.
Research conducted by the University of Colorado-Boulder (CU) as part of this project using
EVA show one potential approach to identify and evaluate risks (Haagenson et al. 2013). The
objective of the CU research was to apply EVA techniques to water demand data at two case
study utilities (Aurora, Colorado, and Tampa, Florida) and show the potential impacts from
climate change on the water demand forecasts for these two case studies.
Focusing on Aurora, Colorado, the CU research used an EVA approach to predict the changes in
water demand due to potential climate change scenarios. Daily production data from 1990-2010
showed the critical season of high demand in June-August. Daily weather data were used to
develop weather attributes (hot/dry, wet/cold spells along with average weather) for June-
31
August. A simple bootstrapping method was used to forecast weather trends, and then EVA was
used to generate projections of water demand extremes. This research found that under climate
change scenarios, exceedances increase over time for the warm/wet and the warm/dry cases,
relative to natural variability.
Summary
A forecast for anything has some chance of being incorrect due to the fact that any forecast is an
attempt to predict the future. Realistic forecasts of water demand extremes should be valuable to
water system managers (and their planning staff) during costly infrastructure decisions, as the
cost of being wrong could be significant. Different methods and models can be used for
forecasting, ranging from a simple scenario of 10 percent additional peak-hour and peak-day
demands, to a slightly more complicated scenario of 10 percent additional demand coupled with
a 10 percent decrease in water supply, to more computational-intensive approaches such as EVA.
32
VII. What Utilities Should Be Doing Now
Water system managers are increasingly confronted by a variety of challenges. These challenges
include an increase in drinking water regulations from the Environmental Protection Agency
(EPA), an aging/transitioning workforce, and increased needs for investment in the aging
distribution system in the face of opposition to raising rates, especially in light of current
political and economic conditions (AWWA 2013). Business factors are a significant concern for
water system managers, but climate change introduces a new set of challenges for water system
managers and their planning staff for both long-term planning and for future operations and
maintenance of the water system.
A universal cookie-cutter approach for incorporating
potential impacts from climate change into water
demand forecasting cannot be easily developed.
Situation-specific approaches need to be developed
for demand forecasting that take into account local
considerations in terms of:
Availability of water
Characteristics and patterns of water use and related data
Characteristics of demand (sensitive to climate change) and influential explanatory
factors such as the temporal and spatial characteristics of temperature, precipitation, and
socioeconomic factors
Availability of internal and external modeling expertise
Understanding issues faced by management, the public, and the political leadership
Real or perceived importance of climate change relative to other planning challenges
(e.g., lack of new water sources) and objectives (e.g., reliability of existing supplies).
The last bullet warrants some additional discussion. At any given time, a water system is
presented with a number of risks that vary in terms of immediacy, severity of potential
consequences, and likelihood of occurrence. These risks can include the possibility of supply
loss (due to contamination, regulation, supply seasonality/drought, turbidity, reservoir operations
constraints, etc.), system or component failure, supply contract performance, stranded resources,
“A universal cookie-cutter
approach cannot be easily
developed.”
33
political environments, personnel/labor relations, demand management requirements, and rate
affordability. Managers must be cognizant of the costs associated with eliminating or mitigating
any particular risk, and must justify risk-management efforts accordingly. Realistically,
achieving zero risk is not possible and eliminating certain risks may be cost prohibitive.
Decisions about how to manage the potential demand-side risks related to climate change will
need to be made within the context of the larger
portfolio of risks for each water system, and these risks
typically will be different for each system.
Water systems may face several potential impacts from
climate change on operations and maintenance for both
existing and future facilities. For example, sea-level rise
may result in a system having to relocate facilities
and/or modify operations, and water demand may also
change due to customer relocation and changes in
regional growth patterns. Increased weather variability
resulting in increased incidence of flooding, increasing numbers of other significant weather
events such as hurricanes, ice storms, etc., may also have a more pronounced direct impact on
the water system (i.e., flooding and/or other physical damage to the system). The relative
significance of these impacts will vary from utility to utility.
Water systems need to understand that if ―stationarity is dead,‖ the past may not be the best
predictor of the future, but it is important to understand past dynamics and use that information
to help inform future decision-making (Milly et al. 2008). While all models are built upon past
data, improving the measurement of the impacts and causal relationships between the factors that
are known and have been experienced and a system’s demand is critical. A system’s average
demand could change in the future due to increased market penetration of low-flow plumbing
fixtures. A system’s peak demand could also change due to changes in the weather.
In the future, climate and weather conditions are likely going to change for water systems.
Furthermore, future climate and weather variations may vary geographically. Some examples of
potential changes that may affect operations and planning include:
“…relying solely on the
past to predict future
water demands could be
problematic, especially
without a more in-depth
understanding of what
factors have influenced or
determined past patterns.”
34
Springtime beginning earlier and ending later, which extends the watering season
Changes in total precipitation, or the annual/seasonal distribution of precipitation (e.g.,
fewer but more intense storms)
Warmer temperatures and longer periods of hot and dry spells
The availability of adequate water resources (and/or the lack of new water sources) generally
places water systems into three categories when considering actions to address the potential for
altered conditions and associated risks. The level of effort and expense (time and resources)
allocated toward increasing the understanding of water use patterns and improved demand
forecasting may be characterized as:
1. Wait and See—refers to systems having ample long-term water supplies and adequate
treatment and transmission capacity.
2. Start Thinking About It—refers to systems that are looking for a ―no-regret‖ strategy that
can be adopted now with minimal cost, while learning more about potential impacts of
climate change to their systems. Most systems in this category are also looking for
flexible adaptive management strategies that can be adjusted as more data is available
and translated into actionable information.
3. Should Be Thinking About It—refers to systems already resource constrained (i.e.,
resource shortages already exist) or nearing safe yields and where the possibility of
constraints or demand pressures is likely to worsen.
More research is needed to understand specific potential impacts to water systems so that water
systems can establish their risk tolerance and adaptive management positions. The research
needs are described in greater detail in the next section of this report. In the meantime, system
managers can take measures now to help reduce uncertainty in forecasting water demand. These
recommendations were developed by a broad range of stakeholders, including water system
managers, consulting engineers, and academics during the two workshops discussed in Section
IV and the subsequent webinars.
35
These recommendations fall into six general categories that need further investigation by water
systems to determine the appropriate means of implementation by system managers and
planners. The six categories are discussed in more detail below.
Collect Additional Weather and Demand Data
The past and the present have to be better understood before one can make accurate predictions
about future conditions. Having complete and accurate data lies at the heart of modeling.
Unfortunately, a significant number of utilities collect, maintain, and store only limited data to
support water demand analysis and forecasting. To start the process of reducing uncertainty in
forecasting water demand, systems should improve their data collection processes for historical
water use data, weather information, and related factors that influence water demand.
Water system managers should also be aware of the time and resources necessary for identifying
the appropriate data to collect, how to collect it, and how to analyze it. The objective of any data
collection effort needs to be clear at the outset. Without a clear objective, a water system will
likely struggle not only with turning the data into actionable information for decision-making,
but also with justifying the cost of enhancing data collection efforts.
For weather data, temperature and precipitation are the most commonly collected parameters.
Several potentially important temperature variables will typically be correlated with water use,
for example (but not limited to):
Daily or average high and low temperatures
Frequency of hot days (e.g., number of days in a month with high temperatures exceeding
90 degrees)
Number of consecutive days the temperature is above a certain threshold
Understanding precipitation patterns is also important for analyzing water demand patterns.
Several potentially important precipitation variables will typically be correlated with water use,
for example (but not limited to):
Amount of precipitation (daily, weekly, monthly, annually)
Frequency of precipitation events
36
Intensity of precipitation events (for example, two inches of rain in an hour is quite a
different event than two inches in 24 hours)
Number of consecutive days with or without precipitation
Data on a daily time-scale are typically available for
both of these parameters from the National Climatic
Data Center (NCDC) for several thousand weather
observation stations across the United States (NCDC
2013). Data for multiple timescales are available online
for download and analysis.
It should be noted that missing weather observations
continue to be a problem for data analysis (even from
official data sources) and may worsen due to lack of funding for continuing operations of
weather stations. Despite this problem, several statistical techniques such as imputation, partial
imputation, bootstrapping, partial deletion, and interpolation are used for handling missing
values in time-series data (Honaker and King 2006).
Many water systems may have only one weather station in their service area, especially those
serving a small geographic area. Larger water systems may have multiple available weather
stations, allowing them to decide upon the best data to represent historical conditions or to
weight weather observations from multiple locations to estimate the contours of weather across
their service area. Many water systems use various statistical techniques such as nearest neighbor
or other distance-weighting techniques to incorporate data from multiple weather stations. These
procedures are particularly well-suited for situations where climate and weather conditions can
vary significantly across a water system’s service territory.
Understanding seasonal weather patterns is crucial for evaluating the impact of climate on water
demand. During the summer, most systems experience more outdoor water use (i.e., watering
lawns, gardens, etc.) that can significantly contribute to system peaking patterns. Most (but not
all) systems experience peak-hour and peak-day demands during the summer, with increased
water use for bathing and washing clothes, further driving peak demand.
“Understanding seasonal
weather patterns is crucial
for evaluating the impact
of climate on water
demand.”
37
Both average demand and peak demand (i.e., peak-day and peak-hour) data are important
benchmarks of water demand. Water treatment plants and distribution systems have to be
designed to meet peak demands, and in many cases, those peak factors are based on ―best
engineering judgment‖ and/or design requirements established by the state. Such peak use
factors could be refined and/or modified if sufficient historical data is available and these peak
demands can be correlated with weather conditions. Because of the importance of peak demand
for system design, investment in historical data collection and concentration on peak-use periods
might be easily justified.
Many systems use production data as a surrogate for water demand. This approach is generally
acceptable when it is the only data available, but system managers need to be aware that
production data will include non-revenue water, such as physical losses in the distribution system
and/or fire flows.
Water sales data can provide more disaggregated detail on water usage patterns, though customer
classification schemes can vary substantially from system to system. For example, some systems
have two categories—residential and commercial. Others may classify solely on the basis of
water meter size. More water systems are now classifying customers into multiple categories
such as single-family residential, multifamily residential, commercial, industrial, institutional,
and/or additional categories.
Several other more refined classifications are possible and have been implemented in many
water billing systems. In general, the more categories in which demand data are collected, the
greater the opportunity to better understand water usage patterns.
The emergence of Automated Meter Infrastructure (AMI) technology provides a new means for
collecting very detailed water consumption data. AMI technology can provide instantaneous
demand data, but this requires careful attention to data management considerations. For example,
if a system has 10,000 residential meters and each meter transmitted a flow (demand) reading
every hour, over 87 million data points would be generated every year if the data from every
meter were collected and analyzed. How would all of this data be collected, stored, and
analyzed? What would be the bands of ―normal‖ variability and what would be considered
―outliers‖? Developing the appropriate Quality Assurance/Quality Control (QA/QC) protocols
38
quickly becomes very important, as well as the development of data analysis protocols. High
resolution data from a statistically representative sample of accounts could be used instead of the
data from all accounts, but the sample design would need to be appropriate for the power
(quality) of the data needed. System managers should not underestimate the time and resources
needed to translate this data into actionable information for informed decision-making. However,
as more AMI data becomes available, the benefits and costs for planning and evaluation will
become more transparent.
Analyze the Data and Translate It into Actionable Information
As previously discussed, a data analysis plan is a critical component of any data collection effort.
As part of this effort, a system should have a clear understanding of the data life cycle, which
includes data collection; quality control and assurance; data management; data analysis; long-
term data archival; and, data retirement. Each component of the life cycle is dependent on how
the data will be applied to support utility functions. A water system planner should not
underestimate the time and resources needed to translate data into actionable information for
informed decision-making. The previous example of 87 million data points annually from a
medium-size system with 10,000 meters provides some insight into the criticality of developing a
data analysis plan as part of making the investment in the data collection effort.
The data analysis plan may dictate the collection, processing, and integration of ancillary data
such as the weather and demand data discussed previously. Knowing what questions need to be
answered by the data collection effort will drive the list of critical data elements to be collected,
as well as any metadata that will be required to support the analyses and conclusions reached. A
clear understanding of what is known about the causal relationships between the factors that are
known versus what hypotheses are being tested within a system will help determine what data
should be collected and how it should be stored and managed. Developing a data analysis plan
before data collection begins will improve the efficiency of the process, as well as its
effectiveness.
.
39
Multiple data analyses could potentially be conducted based on the enhanced collection of
weather and demand data previously described. One potential analysis should be developing an
understanding of both the inherent buffer in peak demand (the ability to restrict outdoor demand)
from outdoor use and the potential for ―demand hardening‖ as a result of past outdoor use
restrictions. For example, if outdoor irrigation has been reduced through past conservation
measures, then the potential for additional reductions during a drought has likely been reduced. It
is important for a water system to determine what the ―soft‖ demand actually is, so that it can be
addressed accordingly.
Finally, a system should develop a plan for a regular update of water demand forecasts. These
forecasts should be updated, just as Capital Improvement Plans (CIPs) and cost-of-service
studies. The California Urban Water Conservation Council includes requirements for five-year
demand forecast updates as part of its Memorandum of Understanding (MOU) with its signatory
members (CUWCC 2013). A similar five-year update of demand forecasts (and resource
availability) is performed by the water systems supplying the Washington, DC, metropolitan area
by the Interstate Commission on the Potomac River Basin (ICPRB 2013). Systems should
consider adopting a five-year cycle for a top-to-bottom update of their water supply and demand
forecasts.
Evaluate Potential Changes in Demand
A water system should carefully evaluate potential changes in demand when developing demand
forecasts. Future demands can be impacted by a number of factors:
1. Increased water use efficiency of plumbing devices and appliances (such as dishwashers
and washing machines) and increased installation of such devices
2. Increased water conservation in industrial/commercial applications
3. Socioeconomic factors
a. Density of development
b. Mix and types of businesses
c. Population, employment, and housing
d. Future costs and pricing
e. Relative shift from single-family to multifamily
f. Other
40
4. Attitudes/behaviors
a. Conservation ethic
b. Landscaping
5. Shift towards more water reuse
6. Climate change
The continued penetration into the marketplace of low-flow plumbing fixtures and water-
efficient appliances such as dishwashers and washing machines will likely continue to lower per-
capita demand for most systems until such time as it levels out. The impacts of the Energy Policy
Act of 1992 (EPACT92) on water use has been to reduce water use by 5 percent in the first
decade after EPACT92 (Billings 2008). Furthermore, it has been estimated that additional
reductions in water use over the next 10-20 years could be in the range of 15-25 percent. This is
a significant reduction from past water use patterns. Systems should develop an understanding of
the penetration of low-flow plumbing fixtures in their service area (i.e., the current installations
of such fixtures and appliances, approximately how many are being replaced annually, and the
approximate numbers of older higher-flow plumbing fixtures). Systems should then make
predictions on the future trend of that penetration and its implications on future per capita
demand.
Additionally, ultra low-flow plumbing fixtures that go beyond the regulatory requirements have
entered the marketplace, and their market share is continuing to grow. For example, waterless
urinals are being used in some new buildings and are being retrofitted into some existing
buildings. The use of more water-efficient plumbing fixtures and no water use for outdoor
irrigation in new construction are both part of the LEED scoring system developed by the US
Green Building Council (USGBC 2013).
Other demand-side management programs can also impact future demand forecasts. EPA’s
WaterSense program not only addresses irrigation, but also other residential and commercial
conservation measures (EPA 2013). Between all of these low-flow fixtures and water-efficient
appliances that are driven by the consumer and commercial marketplaces, a huge part of the
forecast (―passive savings‖) is out of the water system’s control. So the system needs to develop
an understanding of these potential impacts on water demand. A water system will likely have to
41
invest time and resources to develop an understanding of all of the installations of low-flow
fixtures and water-efficient appliances in its service area.
Many commercial, industrial, and institutional customers of water systems have also
implemented demand-side management programs. In some areas, these customers have reduced
water use due to water supply constraints (i.e., a drought). Other customers have reduced water
use as part of corporate and institutional environmental stewardship. Within the beverage
industry, the Beverage Industry Environmental Roundtable (BIER) has designated water
stewardship as one of its focus areas (BIER 2013). The roundtable provides a mechanism for the
major beverage manufacturers to share best practices and to benchmark against each other
through the BIER-developed World Class Water Stewardship in the Beverage Industry. Other
commercial, industrial, and institutional customers have reduced their water demands for simple
financial reasons—they want to reduce their operating costs and in some cases, water bills can be
a substantial part of their costs.
Many water systems have already seen decreased water use from their commercial, industrial,
and institutional customers. In some areas, this trend may continue; but in other areas, the
majority of the demand-side reductions have already taken place, and the decreasing trend may
be ―flattening out.‖ Water systems need to have discussions with their major commercial,
industrial, and institutional customers to understand what demand-side management programs
have already been implemented and what programs might be implemented in the future at these
facilities as part of the system demand forecasts.
Water systems need to also consider several socioeconomic factors in their demand forecasts.
The US population is going to undergo several demographic shifts in the next 40-50 years
(Smithsonian 2013). The density of development and the type of development might be different
in the future. While suburban living will still appeal to many, others will gravitate to urban areas.
Similar shifts in the future could hold true for the mix and types of businesses in the service area
of a water system. Beyond demographics, another socioeconomic factor for water systems to
consider will be their customers’ ―willingness to pay‖ as water and sewer rates increase.
Depending on the economic status of the community, future water and sewer rate increases could
decrease demand as consumers become less able to afford to use as much.
42
The attitudes and behaviors of the customers in the service area of a water system will also need
to be factored into water demand forecasts. More and more customers are embracing a
conservation ethic in many facets of their lives, including water use. Water systems will need to
understand the penetration of the water conservation ethic within their customer base when
developing water demand forecasts. For example, the Saving Water Partnership (SWP) is a
collaborative regional conservation program lead by Seattle Public Utilities and includes 18
water utilities purchasing wholesale water from Seattle. SWP has developed nine customer water
conservation use efficiency strategies that focus on education, outreach, and information transfer
(SWP 2013). The SWP goal is to hold total water use below a specified level despite population
growth being forecasted to increase by 3.9 percent between 2013 and 2018.
Changes in landscaping practices can also impact water demand forecasting. In some areas,
increased use of home sprinkler systems has led to increased peak demands in the early morning
hours. In Northern Virginia (typically not considered a water resource-constrained area), one
water system asks its customers to voluntarily implement a two-day watering schedule to reduce
peak summer demands, and it encourages the planting of water-wise/native landscaping, also
known as Xeriscaping (Loudoun Water 2013). In North Carolina, another water system is
providing incentives for customers to purchase and install smart irrigation controllers through a
Smart Irrigation Program (Charlotte-
Mecklenburg 2013). In more traditionally
water resource-constrained areas such as the
West and the Southwest, Xeriscaping is quite
popular (Denver Water 2013). Increased
implementation of Xeriscaping in a water
system’s service area would impact water
demand forecasts. Water systems need to
develop an understanding of changes in their
service areas in landscaping practices for system demand forecasts.
Increased water reuse is another factor to consider in water demand forecasts. Many water
agencies provide both drinking water and wastewater services in their communities. In many
43
areas of the country, water reuse is increasing for primarily two reasons—either to provide an
alternate water resource for non-potable uses to reduce the drinking water demand or to provide
an alternative for wastewater discharge (or in some cases, for both). Reclaimed water can be
used for a variety of purposes, including irrigation, makeup water for cooling towers and/or other
industrial uses, and in some cases, for indirect potable reuse.
For water demand forecasting, the exact end use of the reclaimed water is not critical compared
to the fact that the reclaimed water has taken the place of a traditional drinking water use and the
demand has changed. A 2008 report by the WateReuse Association found 1,221 utilities with a
water reuse facility in their national database, primarily in Florida, Texas, and states in the West
and Southwest (WateReuse 2008). These water reuse facilities produced over 374 billion gallons
of reclaimed water in 2008. Water systems need to understand the use of reclaimed water in their
service areas and understand the projected reclaimed water system growth in their system
demand forecasts.
The potential impacts of climate change are additional factors to consider in demand forecasts.
Increased temperatures and changes in precipitation will change outdoor water use, which in
many areas, is the driver for determining how to meet system demand. Water systems need to
make some predictions on how outdoor watering patterns might change due to future changes in
the climate for their system demand forecasts.
Evaluate Potential Changes in Demographics in the Service Area
As previously discussed, for many systems, water demand forecasting is conducted by simply
multiplying gallons per capita per day (gpcd) times the projected population. Both gpcd and
population have their own underlying uncertainty and multiplying the two magnifies those
uncertainties.
Projections of population and job growth are fraught with uncertainty. The recent recession is
providing an opportunity for many water systems to take a hard look at past population and job
growth projections. The Water Research Foundation has identified water demand as one of its
ten focus areas in its research program and one of the resultant research projects is to develop an
understanding of the recent recession on water use and demand forecasting (WaterRF 2013).
44
Water system managers and planners should actively engage demographers, urban planners and
land use planners to understand their population and jobs projection data and its inherent
assumptions and uncertainties. Water system planners and utility public relations staff should
talk to their customers to learn what influences demand and then apply that knowledge to future
forecasts.
Equivalent accounts are typically the starting point for forecasting water demand for many
systems. Equivalent accounts scale accounts according to the relative water use per account
across sectors. For assuming an average single-family account with 400 gallons per day and an
average industrial account of 2,000 gallons per day, the industrial account is ―equivalent‖ to five
single-family accounts. Systems should be aware of changing demographics and changing size
of families in their service area, and incorporate those changes into future demand forecasts.
Water system managers and planners should seriously consider breaking away from
conventional population and jobs forecasting and invest the time and resources into developing
detailed demand models. Systems need to better understand demand and model what makes
demand vary over both short- and long-term model run periods. Again, a system should not
underestimate the time and resources needed to build a demand model. A system should run the
model over both short- and long-term model run periods, and then translate the model results into
actionable information for informed decision-making.
Understand and Incorporate Uncertainty into Forecasting
Water systems managers and planners only need to look at past forecasts to see their inherent
uncertainty.
45
Figure 4. Source: ICPRB, Demand and Water Resource Availability for 2040.
Figures 4 and 5 depict results from two major metropolitan areas that could be replicated at most
US water systems. Demands forecasted 20 or 40 years ago are not seen today for the many
factors such as conservation and demographics previously discussed.
Figure 5. New York City Water Demand. Source: New York City Department of Environmental
Protection.
What can one learn from these two examples? First, updating water demand forecasts on a
regular basis is prudent, as these examples are typical of many systems that have over-predicted
future water demands in the past. For example, demand in 2013 in the Washington, DC,
metropolitan area is approximately 500-510 million gallons per day (MGD) and past forecasts
had predicted demand in 2013 to be as high as 900 MGD.
Second, presenting future water demand forecasts as a straight line implies a mistaken level of
precision and does not appropriately present the inherent uncertainties in these forecasts.
46
Figure 6. Long-Range Water Demand Forecast for Orange Water. Source: Orange Water and
Sewer Authority Long-Range Water Supply Plan Update.
Figure 6 shows the uncertainty bands for future forecasts for Orange County, North Carolina.
These bands can either be the ―high‖ or ―low‖ forecasts, or a specific confidence interval.
Forecasts of future water demand data are typically shown graphically as a single time-series line
or curve. This graphical single line representation gives a mistaken impression of confidence
surrounding these future predictions. Future demand forecasts should be presented to water
system managers with the appropriate uncertainty bands.
Water system managers and governing board members need to understand these uncertainties
and how these uncertainties might impact investments in future infrastructure. Avoiding stranded
investments (i.e., building infrastructure to meet future needs that turns out not to be needed until
later) is important from both the manager’s and governing board’s perspective. A balance needs
to be struck between water-supply risk and financial risk in all cases. Once the debt is incurred
for infrastructure construction, repayment of the debt is constant and continues whether or not
the demand increases as predicted or lags behind.
47
Plan for Drought So the System Can Cope
A drought plan should address the consequences and operational challenges associated with both
acute and protracted water shortage events, and should also be integrated with a system’s
Emergency Response Plan (ERP) for other emergencies such as hurricanes, tornadoes, flooding,
and power outages. Some water systems are required to develop drought plans but others do not
face such a regulatory requirement.
The typical drought plan involves a series of increasingly stringent conservation and use
measures. One issue facing water systems is that conservation is sometimes regarded as being
equivalent to a source of future supply. This impairs the ability of the water system to deal with
future droughts because there is less excess capacity in the system. While this is a supply issue
(as opposed to demand issues, which are the focus on this project), water systems need to
understand the linkages between supply and demand that result from their conservation efforts.
If a system already has a drought plan, the plan should be revised on a regular basis to take into
account lessons learned from past droughts. A five-year review cycle is what is commonly used
for other Standard Operating Procedures (SOPs) such as Emergency Response Plans (ERPs).
Systems should consider adopting a five-year cycle for a top-to-bottom update of their drought
plans. In situations where the water system has undergone a major expansion or modification, the
EPA recommends revising the ERP after completion of the expansion/modification (EPA 2004).
A drought plan should be part of this revision.
Summary
Water system managers must consider a myriad of challenges when planning for future
operations and maintenance of their water systems. Business factors are a concern; climate
change introduces an additional set of challenges for water system managers and their planning
staff. A single ―one-size-fits-all‖ approach cannot be easily devised for incorporating potential
impacts from climate change into water demand forecasting. More research is needed to
understand specific potential impacts to water systems so that water systems can establish their
risk tolerance and adaptive management positions. However, system managers can take
measures now to help reduce uncertainty in forecasting water demand:
48
1. Collect additional weather and demand data;
2. Analyze the data and translate it into actionable information;
3. Evaluate potential changes in demand;
4. Evaluate potential changes in demographics in the service area;
5. Understand and incorporate uncertainty into forecasting; and
6. Plan for drought so that the system can cope with it.
Before the future effects of climate change are upon us, water system managers and planners
should carefully consider the above recommendations. And, while all system managers can take
them into consideration, the implementation at any one system will be very system-specific
given the time and resources available to the system manager and planning staff as well as the
manager’s knowledge and understanding of the technical and policy issues affecting operations.
49
VIII. Recommendations for Future Research
Water systems can (and should) take steps now to improve their data collection and analysis to
better address the uncertainties surrounding water demand forecasting, particularly in light of
climate change. The direct linkage between water demand and water sales (and system revenues)
requires developing a better understanding of future uncertainties surrounding infrastructure
needs, revenues, and the potential impacts from climate change for risk management. However,
more research is needed to help drinking water systems better understand water demand. This
chapter summarizes some recommendations for future research.
Understanding Baseline Conditions and Potential Changes
Situation-specific (e.g., service-area specific) research on baseline and changing conditions is
recommended for most medium to large water systems, as well as regional and national level
assessments that require aggregation of system data. However, regional and national studies of
baseline and changing conditions will be hampered by lack of uniform data elements and naming
conventions, which must also be addressed.
Water system managers and planners need to better understand both baseline conditions and
potential future changes to those conditions. Questions requiring research include:
What percentage of the service area has installed low-flow plumbing fixtures and what is
the net impact on water demand? How will future per-capita demands change as more
low-flow fixtures are installed and as more water-efficient dishwashers and clothes
washers penetrate the marketplace? How much is indoor residential water use expected
to decrease and when is that decrease expected to flatten out? How might upgrades to
cooling towers on commercial, industrial, and institutional buildings reduce water use?
What percentage of the service area has installed residential lawn sprinklers and what is
the net impact on water demand? How many existing homes are being retrofitted and
how many new homes are being built with lawn sprinkler systems? How many sprinkler
systems have ―smart‖ controllers versus manual controls?
How have changing demographics in the service area impacted water demand? How is
family size changing in the service area? How many households are ―downsizing‖ versus
staying in place? Is the average household smaller in number now than in the past? What
50
do demographers project, and what are the uncertainties, surrounding their future
population, land use, and economic forecasts? How do community sustainability
initiatives impact future forecasts? How do changing economic conditions affect choices
about water use either directly or indirectly? How might climate change impact future
demographic patterns, e.g., people relocating to different parts of the country?
How have shifts in business and industrial customers (e.g., factories closing or relocating)
impacted water demand and what future shifts should be considered?
How might increasing water rates (driven by aging infrastructure, water quality
regulations, and other factors) further impact future water demand? How much will
consumers reduce their water use based on increasing water bills?
How will the increased use of Automated Meter Reading/Automated Metering
Infrastructure (AMR/AMI) help a system develop a better understanding of its non-
revenue water and customer water use characteristics?
How much is water re-use forecasted to replace potable water in the future?
For systems that provide both drinking water and wastewater services, what percentage
of the service area has installed rainwater harvesting and/or using gray water and what
are the impacts on wastewater flows? Are increasing costs and pricing for wastewater
service having an impact on water consumption?
Potential Impacts of Demand on Appropriate System Design
Water systems are typically designed using projections of water demand that are based on
historical trends, as well as a combination of best engineering practices and codes and standards
(such as the Ten-State Standards or state-level design standards). But more research is needed so
that system design can meet customer needs in a changing environment. For example, peak
factors used for peak-day and average-day demand may need revision as lot sizes decrease or the
population ages and ―downsizes,‖ leading to decreased outdoor irrigation demands. However,
increased temperature extremes combined with drought could lead to increased outdoor
irrigation demands. Situation-specific (e.g., service area specific) research on baseline and
changing conditions is needed. Design standards may need to be revised in the future, but data
collection and analysis is needed now to inform any potential revisions.
51
System design questions that require further research include:
Have peaking factors (i.e., peak-hour and peak-day factors) changed over time? Are
these peaking factors projected to change in the future due to increased implementation
of low-flow plumbing fixtures, residential lawn sprinkler systems (with or without
―smart‖ controllers), and due to changing demographics?
Have the requirements for system storage changed over time and are those requirements
projected to change in the future?
For systems that provide both drinking water and wastewater services, will the projected
reduction in returned flows due to low-flow plumbing fixtures impact the design of
wastewater collection and treatment systems? What percentage of the service area has
installed rainwater harvesting and/or using gray water and what are the impacts on the
design of wastewater collection and treatment systems? What are the potential impacts
of reclaimed water used for outdoor irrigation?
System Data
As more water systems implement AMI/AMR, more
and more data is being collected; however, each
system has its own information management
protocols and specific naming conventions for data
elements. For example, researchers at Virginia Tech
found that regarding customer complaint data, one
system had 13 descriptors for water that appeared
―black‖ or contained something black (Whelton
2007). The lack of a common convention for
managing data elements inhibits comparisons
among systems, and makes regional and/or national aggregation of demand data difficult and
time intensive. Development of uniform conventions for managing water demand and
demographic data is necessary to allow for accurate comparison and aggregation of data across
systems.
A minimum set of requirements for collecting water demand and demographic data is needed to
―Development of uniform
conventions for managing
water demand and
demographic data is
necessary to allow for
accurate comparison and
aggregation of data across
systems.”
52
inform demand forecasting and water resource planning, and would also be useful to the drinking
water community across a wide variety of data types and analyses. In general, more research is
needed to get systems ―on the same page,‖ in regard to research and planning.
Systems also need to ensure that their customer data is categorized correctly. Effective
management of customer billing information is critical. At a system level, research may be
needed to ensure that account information is up-to-date (e.g., that the ownership of a building has
not changed, or a building’s uses have not changed), and that single-family, multifamily,
commercial, industrial, and institutional accounts are correctly categorized. The need for uniform
data management conventions is increasingly acute with the volume of data generated by
AMR/AMI systems.
System Revenues
Future water demand forecasts are inextricably linked to forecasts of system revenues. More
detailed information is needed for both water demand and revenues projections. Improved data
will better inform the design of rate structures that strike an appropriate balance between fixed
account charges, tap fees, and commodity sales revenues.
Data and Research Integration
More research is needed on how systems may integrate data from external sources (such as
demographic data from the Bureau of the Census or climate modeling data from the National
Climatic Data Center) with utility demand, production, and planning data. More research is also
needed on the relationships and the linkages between water quantity (water resources) and water
quality and the potential water quality changes resulting from climate change.
Historical Drought/Water Shortage Analyses
Analyses of past droughts, especially droughts resulting in water use restrictions, could provide
useful insights on integrating climate change projections into water quantity and water quality
forecasts. This research would focus on the weather conditions that led to water-use restrictions
and the decision-making process that the water system used to implement those water-use
restrictions (as well as how and when those restrictions were ultimately lifted), yielding
important information on supply reliability. The research should also assess water demand
53
during the period of restricted use versus the average daily demand. The results should show how
effective the water-use restrictions were and whether the demand returned to ―normal‖ pre-
drought levels or if a ―new‖ normal evolved after the restrictions were lifted. System-specific
research would be useful, but research on a regional and national level (requiring aggregation of
data) might inform a broader audience and be more useful from a policy perspective.
Value of Information Studies
Classical decision analysis can be used to estimate the value of new information for decision-
making. Since better information makes for more informed decisions, a value can be placed on
that information. Value of Information (VOI) studies are not typically applied to drinking water
decision-making; however, in a past drinking water context, one researcher used a VOI approach
to estimate the value of arsenic health effects as part of the regulatory development process for
the arsenic regulation (North 1994).
More research is needed to determine the applicability of VOI methods to water demand
forecasting. A VOI assessment could help decision-makers determine how much to invest in data
collection and analysis, and how much these efforts might be worth in evaluating future
investment decisions (i.e., how much should a system spend now to reduce future uncertainty by
a certain amount). VOI studies could also be used as a sensitivity analysis for investment
decisions on whether to spend additional money on new data collection and analysis projects.
However, VOI studies are not simple and require some informed judgment as part of the process.
Social Science Research
Most decision-makers at a water system come from either a technical or financial background;
therefore, limited social science research has been conducted in the drinking water community.
Research is needed on the effectiveness of ongoing conservation efforts, turf replacement
programs, customer education initiatives, and on the tolerance/acceptance of level of service
(e.g., frequency of water-use restrictions being imposed).
Tools for Investment Decisions
While water systems are a fundamental societal need, they are capital-intensive, requiring
significant investments to develop sources, to build and operate treatment plants, and to build
54
and operate distribution systems. Significant expenditures are also needed to continually repair,
rehabilitate, or replace aging components of a water system (AWWA 2012). AWWA’s Buried
No Longer report estimated that the cost to restore existing infrastructure and to build new
infrastructure to serve a growing population will total at least $1 trillion between 2012 and 2037.
Therefore, water systems need tools that effectively optimize their investment decisions and
generate accurate water demand forecasts. Issues to address include:
Relationships between past and future water demand, water sales, demographics,
weather, economic conditions, and investment decisions
Relationships between the past and future combinations of tap fees and commodity sales
for water system revenues
Uncertainties in variables important for each system (i.e., whether demographic issues are
more important than weather/climate or economic issues)
Translating the uncertainties in predicting future water demands, risks, and potential
impacts on investment decisions
Steps required by water system staff and governing boards to develop a better
understanding of the linkages between future water demands and investment decisions
Summary
More research is needed to help water system decision-makers better understand their current
water demand and the improvements needed for enhanced water demand forecasting. The above
recommendations are a starting point for identifying specific areas of future research, but by no
means an exhaustive list. More work is needed to develop a water demand research roadmap.
What is known now is that better tools are needed to help systems implement enhanced water
demand forecasting, especially as it relates to future investment decisions. Given the direct
linkage between demand and water sales and system revenues and their impacts on a
community’s viability, developing a better understanding of future uncertainties surrounding
infrastructure needs, revenues, and the potential impacts from climate change is prudent risk
management for water systems and the communities they serve. As part of their fiduciary
responsibilities, governing boards and elected officials also need to understand these linkages
and the potential impacts of climate change.
55
IX. Summary and Conclusions
Operating and managing a water system involves juggling many competing priorities, such as:
Rehabilitating or replacing infrastructure
Lack of public understanding of the value of water
Capital costs and availability
Water supply and scarcity
Aging workforce/talent attraction and retention
Regulation and government oversight
Water security and emergency preparedness
Climate risk and resiliency
As shown above, matching supply and demand is just one set of competing priorities for water
system managers. Optimizing new and/or expanded sources of supply (new and/or expanded raw
water sources as well as new and/or expanded treatment plants) with new and/or expanded
transmission and distribution facilities to match increasing demands is challenging for water
utility managers and planners. Climate change now adds another level of complexity for water
demand forecasting. In order to incorporate climate change in demand forecasting, existing
forecasting needs to be improved before climate change is added to the mix.
Water system managers and planners do not have to wait until all of the uncertainties
surrounding climate change and weather extremes are resolved to improve demand forecasting.
Steps can be taken now to reduce the risk of ―being wrong‖ in their water demand forecasts.
The recommendations for water systems that resulted from this research fall into six general
categories. Water system managers and their planning staff will need to determine how to
appropriately implement these recommendations at their systems, noting that many system-
specific factors would impact potential implementation:
1. Collect additional weather and demand data.
2. Analyze the data and turn into actionable information.
3. Evaluate potential changes in demand.
4. Evaluate potential changes in demographics in the service area.
56
5. Understand and incorporate uncertainty into future forecasts.
6. Plan for drought so the system is able to cope with it.
Every water system does not have the resources and/or the expertise to implement all six of the
above recommendations. However, appropriately managing the system finances and optimizing
future capital investments are two critical priorities for all water systems, and the above
recommendations will help improve the decision-making for future capital investments. These
recommendations offer a starting point for considering the investment of time and resources
necessary to improve and optimize a system’s long-term water demand forecast that drive many
capital investments.
This project also developed several future research recommendations. The water sector will need
to determine how to get this research funded and implemented. Ideally, this research would be
conducted by a blend of government agencies (such as the EPA and the Army Corps of
Engineers Institute for Water Resources), universities, and the research organizations affiliated
with the water sector (such as the Water Research Foundation). The Water Research Foundation
allocates 60 percent of its annual research budget into ten focus areas and water demand is one of
those focus areas (WaterRF 2013). The goal of this focus area is to increase the effectiveness of
water demand forecasting and the incorporating of their uncertainty into water systems
infrastructure and financial planning. But a sustained effort will be needed by a variety of
research organizations in order to address the research topics discussed in this report.
Water demand forecasting is a critical component of water system planning, and having accurate
demand forecasts will ensure that system demand does not exceed the system capacity during
peak demands, as well as minimize the possibility of ―stranded assets‖ due to building new
facilities too far ahead of future demand. It cannot be stressed enough that water system
managers need to make accurate water demand forecasts a priority when juggling all of the
issues inherent in operating and managing a water system.
57
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