RMRS-GTR-295
Vulnerability of U.S. Water Supply to Shortage: A Technical Document Supporting the
Forest Service 2010 RPA Assessment
Appendices
Online: http://www.fs.fed.us/rm/pubs/rmrs_gtr295.html
RMRS-GTR-295: Vulnerability of U.S. water supply to shortage: a technical document supporting the Forest Service 2010 RPA: Appendices
AppendixA:DetailedMethodsforProjectingWaterUse
Future consumptive water use (C) was projected by estimating future withdrawal (W),
multiplying that withdrawal by a consumptive use rate (γ), and then adding the change in
consumptive use attributable to future changes not captured by extrapolating past trends in
withdrawals (ΔC):
C = W ⋅ γ + ΔC
(A1)
Withdrawal is computed as the product of a water demand unit (also called a water use driver) (U)
and an estimate of withdrawal per demand unit (Φ). For example, a water use driver could be a
person for domestic use or an irrigated acre for agricultural use; corresponding withdrawal rates
would be withdrawal per person and withdrawal per acre, respectively.
Estimates of withdrawal rates (Φ) are developed by extrapolation of past trends. Past trends
in the rates of water withdrawal (Φ) in most cases have been nonlinear, with the rate of change
gradually diminishing. Extrapolation of past trends in Φ was accomplished by applying an annual
growth rate (g) based on data from recent years and a corresponding decay in that growth rate (d).
The decay rate was chosen to attenuate the trend, leading gradually toward a hypothesized
equilibrium level. Given a five-year time step for projecting withdrawals, the extrapolation procedure
for a given year (Y) and Water Resource Region (WRR) is as follows:
(
Φ WRR ,Y = ΦWRR ,Y −5 1 + g DIV (1 + d DIV )
Y − LDY
)
5
(A2)
where LDY is the last year for which withdrawal data were available (typically 2005), and DIV is a
major division of the United States, either the eastern or western portion. g and d typically were
computed for major divisions rather than for each WRR because past trends for individual WRRs, as
estimated using withdrawal data from the USGS circulars, were somewhat erratic. The annual growth
factor (g) was computed from all or part of the record from 1985 to 2005.
Estimates of water withdrawal and consumptive use are computed for all ASRs, but the
factors used to produce those estimates (e.g., Φ or γ) are typically estimated at a larger spatial scale
because the data for Assessment Sub-regions (ASRs) are sometimes erratic—perhaps because of
annual weather fluctuations or errors in estimation—so that they do not appear to support
estimation at the smaller scale. Factors Φ and γ are estimated by WRR and applied to ASRs within the
WRR. Similarly, g and d are estimated for eastern (specified as WRRs 1-9) and western (WRRs 10-18)
divisions of the United States and typically applied to all ASRs within the division.
Domestic and public withdrawals and, to some extent, industrial and commercial withdrawals
are distributed spatially with population, allowing use of census tract data for carefully apportioning
county data to ASRs. Withdrawals in the thermoelectric, agricultural irrigation, and livestock and
aquaculture sectors, however, are not distributed spatially with population, thus requiring a different
procedure for allocating county-level withdrawal estimates to ASRs. Lacking data on the actual
locations of thermoelectric plants, irrigated fields, or livestock and aquaculture operations, county
data were apportioned to ASRs by assignment of whole counties to ASRs based on which ASR
contained a greater amount of the county.
DomesticandPublic(DP)WaterUse
Consumptive water use (C) in the DP sector (CDP) in gallon-days (gallons per day for a year) in
future year Y was computed by ASR as:
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RMRS-GTR-295: Vulnerability of U.S. water supply to shortage: a technical document supporting the Forest Service 2010 RPA: Appendices
DP
DP
DP
DP
C ASR
,Y = ρ ASR ,Y ⋅ΦWRR ,Y ⋅ γ WRR ,1990 − 95 + ΔC ASR ,Y
(A3)
DP
where ρ ASR ,Y is population (as computed in equation 5.5), ΦWRR
is withdrawal in gallons per capita
DP
per day for DP uses in the WRR to which the ASR belongs, γ WRR
is a consumptive use proportion of
withdrawal for DP uses in the corresponding WRR based on an average of the proportions for 1990
and 1995 (the last two years for which the USGS provided estimates), and ΔCDP is the change in
consumptive use attributable to climate change.
ΦDP for future years was computed by extrapolation of past trends as follows (see equation
A2, which is identical to equation 5.3):
(
DP
DP
ΦWRR
,Y = Φ WRR ,Y − 5 1 + g DIV (1 + d DIV )
Y − LDY
)
5
(A4)
where LDY is 2005 and g and d are as listed in Table 5.8. ΦDP for past years, providing the basis for
computation of g, was computed as:
DP
WRR ,Y
Φ
=
DP
WWRR
,Y
(A5)
ρWRR,Y
where WDP is withdrawal for DP uses.
The change in consumptive use attributable to climate change was computed as:
DP ,CO2
DP
DP , P '
DP , ETp
ΔC ASR
,Y = ΔC ASR ,Y + ΔC ASR ,Y + ΔC ASR ,Y
(A6)
where the three terms are as specified in equations 5.15, 5.17, and 5.18.
IndustrialandCommercial(IC)WaterUse
as:
Consumptive use in the IC sector (CIC) in gallon-days for future year Y was computed by ASR
IC
IC
IC
IC
C ASR
,Y = I ASR ,Y ⋅ΦWRR ,Y ⋅ γ WRR ,1990 − 95 + ΔC ASR ,Y / 365
(A7)
where I is total annual personal income in 2006 dollars, ΦIC is withdrawal in gallons per day for IC
IC
is a consumptive use
uses per $1000 of annual personal income in the corresponding WRR, γ WRR
proportion of withdrawal for IC uses in the corresponding WRR based on an average of the
proportions for 1990 and 1995, and ΔCIC is the change in consumptive use attributable to meeting the
renewable fuel standard goals (from equation 5.7).
Future personal income by ASR was estimated using county-level population data to spatially
distribute nationwide estimates of future income. Future county-level estimates of I were computed
from national estimates of future I for the three scenarios, while accounting for projected changes in
relative population among counties, as follows:
I j ,Y = ρ j ,Y ⋅ IUS ,Y
I ρj ,2005
  P
j ,Y
j
⋅ I ρj ,2005 
(A8)
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where j indicates a county and Iρ is income per person. These estimates, as well as county-level I for
2000 and 2005 from BEA, were then allocated to ASRs using the 2000 census tract-based allocation
of county population to ASRs as follows:
I ASR,Y =   I j ,Y ⋅ z ASR , j ,2000 
(A9)
j
where zASR,j,2000 is the proportion of a county’s population in the ASR in year 2000.
ΦIC was computed for future years by extrapolation of past trends following the procedure of
equation A2 where LDY is 2005 and g and d are as listed in Table 5.8. ΦIC for past years was
computed as:
IC
WRR ,Y
Φ
=
IC
WWRR
,Y
IWRR ,Y
(A10)
where WIC is withdrawal for IC uses.
ThermoelectricFresh(TF)WaterUse
Computation of future water use at fresh water thermoelectric plants (CTF) must acknowledge
that electric energy consumed in one ASR may be produced in another ASR, and that electric energy
is produced at not only fresh water thermoelectric plants but also at salt water thermoelectric,
hydroelectric, solar, wind, and other types of plants. In light of these facts, the basic approach adopted
here can be characterized as requiring the following four steps:
(1) estimate total energy produced in the United States,
(2) allocate the national electricity production to the ASRs,
(3) determine the proportion of that energy that is produced at fresh water thermoelectric
plants in the ASR, and
(4) apply an estimate of withdrawal per unit of electricity produced to the estimate of energy
produced.
Step 1 was accomplished by multiplying population by Energy Information Administration
(EIA) estimates of future electric energy consumed per person in the United States, under that
assumption that production equals consumption at the national scale. Step 2 was based on the
location of past production capability, on the assumption that future thermoelectric production
capacity would be located proportional to recent past capacity. Step 3 was accomplished by first
subtracting non-thermoelectric electricity production from total production and then apportioning
thermoelectric production among fresh and salt water plants assuming that the recent ratio of fresh
water thermoelectric production to total thermoelectric production remains constant into the future
(i.e., that growth in production occurs at fresh and salt water plants in proportion to recent past
allocation between the two types of thermal plants). Information from the EIA was used to estimate
future non-thermoelectric energy production. Step 4, regarding water use at thermoelectric plants,
was accomplished using USGS water use data. Because USGS thermoelectric energy production data
did not distinguish between these two kinds of thermoelectric plants, we assumed that electricity
produced per gallon of water withdrawn at salt water plants is identical to production per gallon at
fresh water plants.
Water withdrawal and consumptive use at fresh water thermoelectric plants was estimated at
the WRR level because ASRs were considered to be too small for modeling future electricity
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RMRS-GTR-295: Vulnerability of U.S. water supply to shortage: a technical document supporting the Forest Service 2010 RPA: Appendices
production (as production is not so spatially connected to demand that future changes in demand are
all met by changes in production within the same ASR). The water use in an ASR was then estimated
as a proportion of the WRR water use based on the proportion of the withdrawal of the WRR that
occurred in the ASR in 1995, the last year for which we have accurate water basin data from the
USGS.
In more detail (and ordering the procedural steps differently for the purpose of presentation),
consumptive water use at fresh water thermoelectric plants (CTF) for future year Y in gallon-days is
computed by WRR as:
TF
TF
TF
TF
TF
CWRR
,Y = ( EWRR ,Y ⋅ΦWRR ,Y ⋅ γ WRR ,1990 −95 + ΔC ASR ,Y ) / 365
(A11)
where ETF is electric energy produced as fresh water thermoelectric plants (measured in kilowatt
TF
is withdrawal per kWh at fresh water thermoelectric plants in the corresponding
hours, kWh), ΦWRR
TF
WRR based on an average of the 1990 and 1995 proportions, γ WRR
,Y is a consumptive use proportion
of withdrawal for IC uses in the corresponding WRR based on an average of the proportions for 1990
and 1995, and ΔCTF is the change in consumptive use attributable to climate change (see equation
5.22).
TF
EWRR
of equation A11 (thus, for future years) was estimated as a proportion of total thermal
T
production EWRR
based on USGS data for 2005 on the breakdown between salt and fresh water
production:
TF
T
EWRR
,Y = EWRR ,Y ⋅
TF
EWRR
,2005
T
EWRR
,2005
(A12)
T
EWRR
was estimated by subtracting non-thermoelectric energy from total electric energy
production as follows:
T
H
A
EWRR
,Y = EWRR ,Y − EWRR ,Y − EWRR ,Y
(A13)
H
A
is hydroelectric energy production, and EWRR
is
where EWRR is total electricity production, EWRR
other (alternative source) electricity production.1 EWRR of equation A13 was estimated from
population and energy production per capita as:
ρ
EWRR ,Y = ρWRR ,Y ⋅ EWRR
,Y
(A14)
where ρ is population and Eρ is electric energy production per capita, which was assumed to remain
constant at the year 2005 level taken from Kenny and others (2009). The assumption of a constant
level of per-capita electricity use was based on the EIA projections, which show little change in perH
capita use through year 2035 (EIA 2010: Tables 8 and 20). EWRR
of equation A13 was estimated for
years 2010 to 2035 by apportioning the EIA U.S. total hydroelectric energy projections (EIA 2010:
Table 16) to ASRs based on the 1995 apportionment (the USGS circular did not report hydroelectric
energy production after 1995) as follows:
We took electric energy production and water use data from the USGS, rather than going directly to the EIA
from which the USGS obtained much of its data, because the USGS data include some small plants not included
in the EIA data and because we could not account for differences between the two databases that could not be
explained by the inclusion of small plants.
1
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H
WRR ,Y
H
US ,Y
=E
E
⋅
H
E ASR
,1995
H
EUS
,1995
(A15)
H
A
was assumed to remain at the 2035 level.2 EWRR
of equation A13
For years beyond 2035, EWRR
was estimated for years 2010 to 2035 by apportioning the EIA U.S. total for electricity projection
A
) (EIA 2010:
from wind, solar, geothermal, municipal waste, wood and other biomass sources ( EUS
Table 16) to ASRs based on the 1995 apportionment of total thermal generation, as follows:
T
EASR
,1995
A
A
EWRR
,Y = EUS ,Y
T
EUS
,1995
(A14)
A
was computed by extrapolation of past trends using equation A2
For years beyond 2035, EWRR
with LDY equal to 2035 based on the projected growth from 2015 to 2035.3
TF
ΦWRR
of equation A11 was computed by extrapolation of past trends as in equation A2 with
TF
for past years (1985-2005)4 was computed as:
LDY equal to 2005. To allow this extrapolation, ΦWRR
TF
WRR ,Y
Φ
=
TF
WWRR
,Y
TF
EWRR
,Y
(A17)
where WTF is fresh water withdrawal from the USGS circulars and ETF was computed as a portion of ET
based on USGS withdrawal data as follows:5
TF
WRR ,Y
E
T
WRR ,Y
=E
⋅
TF
WWRR
,Y
T
WWRR
,Y
(A18)
Finally, as previously mentioned, the WRR values are apportioned to their respective ASRs as
follows:
C
TF
ASR ,Y
TF
WRR ,Y
=C
⋅
TF
WASR
,1995
TF
WWRR
,1995
(A19)
These estimates ignore the possibility of developing hydrokinetic energy (wave, tidal, ocean current, and river
kinetic energy). Current projections of hydrokinetic energy production would be very uncertain. Much of the
potential for hydrokinetic energy is located in Alaska and thus is outside of our study area (Bedard and others
2009). Assuming, as did Bedard and others, that 15% of the available kinetic energy can be developed and
assuming a 90% efficiency rate, we estimate that the potential is about 84,000 GWh, or about 2% of projected
electricity consumption in 2030. Apparently, including this source of electricity in our estimates would have
only a small impact on water use.
3 There is thought to be tremendous potential for developing offshore wind energy. Musial (2008) estimated
that potential to be 3948 TWh per year, which is about equal to the total current electricity use of the United
States. Much of this potential is in Alaska, and there are other issues of transmission from the coasts, but the
potential in the lower 48 states remains large. Very little of this potential—only 0.34% of projected total wind
energy generation in 2035—is included in the EIA projections incorporated here.
4 Excepting year 2000 when the USGS did not provide estimates of electricity production for its corresponding
estimates of thermoelectric withdrawal. Linear interpolation was used to fill in this missing year based on the
values for 1995 and 2005.
5 WTF includes both self-supplied withdrawals and deliveries from public supplies.
2
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Irrigation(IR)
Consumptive water use in the IR sector (CIR) for future year Y in gallon-days was computed by
ASR as:
IR

 IR
ΔC ASR
,Y
IR
IR
γ
C ASR
A
=
Φ
⋅
+

,Y
ASR ,Y 
 gross , ASR ,Y WRR ,1990−95
β



  α
 
(A20)
IR
where A is irrigated acres, Φ IR
gross is irrigation withdrawal in feet of depth, γ WRR is a consumptive use
proportion of withdrawal for IR uses in the corresponding WRR based on an average of the
proportions for 1990 and 1995, ΔCIR is the change in consumptive use attributable to climate change
(from equation 5.11), β is 30.5 (the number of centimeters per foot), and α is 893 (for converting
from acre-feet to gallon-days). AASR of equation A21 is computed by allocating acres within the WRR
as follows:
AASR ,Y = AWRR ,Y ⋅
AASR ,1995
AWRR ,1995
RFS
+ AASR
,Y
(A21)
where AWRR,Y for future years is computed by extrapolation of past trends using equation A2 where
RFS
LDY is 2005 (for 2010, the mean of 2000 and 2005 was used as the base year), and AASR
is the
increase in acres attributable to meeting the renewable fuel standard goals (see equation 5.8). AWRR,Y
for past years was taken from USGS water use circulars. The year 1995 was used for the
apportionment to ASRs because it is the last year the USGS provided estimates by basin.6
Φ IR
gross , ASR , withdrawal for IR uses per irrigated acre in equation 5.10, was computed for future
years by extrapolation of past trends at the WRR level using equation A2 where LDY is 2005 and g
and d are as listed in Table 5.8, as follows:
IR
Φ IR
gross , ASR ,Y = Φ gross ,WRR ,Y
(A22)
Φ IR
gross ,WRR for past years was computed as:
Φ IR
gross ,WRR ,Y =
IR
WWRR
,Y
AWRR ,Y
(A23)
where WIR is withdrawal for IR uses. 7
Livestock(LS)andAquaculture(AQ)WaterUse
The USGS’ estimates of livestock (farm animals and feedlots) water use go back to 1960.
Aquaculture was added to the livestock category in 1985. Beginning in 1990, the fast-growing
The USGS acreage from the 2005 circular could have been used by allocating county acreage data to ASRs. The
loss of accuracy in that allocation was considered a greater problem than using the earlier (1995) data.
7 The USGS estimates of irrigated acreage account for double cropping by counting a double-cropped acre twice
(the same approach is used for triple cropping). By using the extrapolation approach described here, we are
assuming that the proportion of acres that are double cropped in a given WRR will not change in the future,
which may be unrealistic if temperature increases with climate change make double cropping more prevalent
in some ASRs.
6
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aquaculture category was reported separately from livestock. We report results for only the 1990 to
2005 period.
Two facts complicate the projection of livestock and aquaculture withdrawals. First, these
categories consist of water use by several different animal species and kinds of aquaculture
operations, and site-specific data on numbers of animals or acres of fish farms are not easily obtained.
Without accounting for numbers of livestock animal units by species (on the range and then in
feedlots and other confined areas) and acres of fish farm, there is no obvious summary unit of
production. Second, livestock products are often not consumed where they are produced, so future
changes in ASR livestock withdrawals will not be proportional to changes in basin population.
For projecting future water withdrawals, the first fact (lack of data on demand units) was
accommodated by basing future withdrawals not on numbers of animals or acres of fish ponds but
rather on human population, as humans are the consumers of livestock products. The second fact
(spatial mismatch between production and consumption) was accommodated by computing total
livestock withdrawal for the WRR and then apportioning that total to individual ASRs based on the
location of past 2005 withdrawals at the county level, under the assumption that the future annual
rate of growth in withdrawal will be the same in all counties.
The approach followed to project future livestock withdrawals, then, was to estimate
withdrawals for the United States as a whole and then apportion those withdrawals to counties (and
then to ASRs) based on county withdrawals for years 1985-2000. Water use for livestock (CLS) for
future year Y is computed by ASR as:
LS
LS
LS
LS
C ASR
,Y = ρWRR ,Y ⋅ΦWRR ,Y ⋅τ ASR ,2005 ⋅ γ WRR ,1995
(A24)
LS
LS
where C ASR
,Y is consumptive use for livestock purposes in year Y, ΦWRR ,Y is withdrawal per person in
LS
the WRR to which the ASR belongs, τ ASR
,Y is the proportion of the withdrawal of the WRR in 2005 that
LS
occurred in the ASR, and γ WRR
,1995 is the consumptive use proportion of the WRR in 1995. τ was
computed from county withdrawal data for 2005, which each county assigned to the area-dominant
ASR of the county. ΦLS was computed for future years by extrapolation of past trends following the
procedure of equation A2 with LDY = 2005 and g and d as listed in Table 5.8, with the base for
computing 2010 withdrawal a weighted average of the 2000 and 2005 withdrawals, with weights of
0.25 and 0.75, respectively. ΦLS for past years was computed as:
LS
ΦWRR
,Y =
LS
WWRR
,Y
ρWRR,Y
where WLS is withdrawal for livestock purposes.
For aquaculture, equations A24 and A25 also apply, replacing LS with AQ.
(A25)
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RMRS-GTR-295: Vulnerability of U.S. water supply to shortage: a technical document supporting the Forest Service 2010 RPA: Appendices
AppendixB:MonthlyBreakdownofAnnualPublicSuppliedWaterDeliveries
Comprehensive water use data that would be used to estimate water demand, such as the
water use data provided by the USGS circulars, typically have been compiled at the annual time step.
However, levels of some water uses, such as irrigation, vary temporarily during the year. The monthly
variation in annual public supply deliveries was needed to help estimate the effect of future climate
change on domestic and public water use.
Lacking a centralized database of monthly water use, we contacted individual cities (or water
companies serving cities, which are considered “cities” herein for the sake of brevity) across the
United States and requested monthly water delivery data for recent years (we accepted monthly
withdrawal data if delivery data were not available). We attempted to obtain data from at least one
city in each of the 204 four-digit basins in the contiguous 48 states (the four-digit basins are a
standard hydrologic division supported by the USGS). Cities of at least 10,000 persons were favored,
but in less populated regions, we contacted cities with at least 2500 people. Nearly all of the monthly
water delivery data are for one or more years during the period 2000 to 2006, but a few are for years
during the 1990s. The process of obtaining municipal data in many cases required repeated efforts to
gain the assistance of city officials familiar with their water delivery data. In all, we obtained data on
10,433 months of water use from 238 cities, for an average of 44 months of data per city.
City data were averaged for ASRs and WRRs. The procedure we used for summarizing the city
data is as follows:
(1) Remove any city without at least one estimate for each month (this step resulted in loss of
two cities).
(2) For each remaining city, use all available data to compute monthly proportions by first
computing average delivery for each month and then computing proportions from the
monthly averages.
(3) Remove a city if the monthly proportions appear to be in error (this step resulted in loss of
four cities).
(4) Compute simple average proportions for all remaining cities within a given ASR or WRR.
(5) Use cluster analysis with the ASR averages to define a small set of clusters of monthly
proportions (cluster analyses were performed using the K-means cluster analysis routine of
SPSS, which allows the user to specify the number of clusters desired). We limited the number
of clusters by restricting cluster size to five or more ASRs.
After removing six cities from the database for reasons explained above, 232 useable cities
remained, representing 91 ASRs.
Table B1 lists the monthly percentages by ASR and the number of four-digit basins and cities
representing each of the ASRs. The average and median numbers of cities per ASR are 2.5 and 2.
Table B2 lists the monthly percentages for four clusters of ASRs, and Figure B1 shows the
locations of the clusters. Cluster 3 contains 45 ASRs, represented by 106 cities, located largely in the
eastern half of the country. Cluster 2 contains 16 ASRs, represented by 41 cities, covering roughly the
northern two-thirds of the West, except the West Coast. Cluster 1 contains 25 ASRs, represented by
72 cities, located in the Great Plains, Southwest, and along the West Coast. Finally, cluster 4 contains 5
basins, represented by 13 cities, 2 in the upper Great Basin, 1 in northern California, and 2 in the
headwaters of the Missouri Basin.
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The monthly percentages of the clusters of Table B2 are plotted in Figure B2. The eastern
cluster (number 3) shows the least temporal variation. The next most evenly distributed cluster (1)
represents a mixture of climates. Here, the weather is either generally drier than in the East,
requiring more landscape irrigation whenever the temperatures warrant it (the Great Plains and
Southwest) or rainfall is less evenly distributed over the year then the East (as along the upper West
Coast). The two clusters showing the highest temporal variation are located in the more northern dry
parts of the West, one (cluster 2) with a maximum use in July and the other smaller group (cluster 4)
with a maximum use in August.
Table B3 lists the monthly percentages of annual water use for the 18 WRRs and the number
of four-digit basins and cities representing each of the WRRs. The WRRs are made up of from 3 (WRR
9) to 26 (WRR 10) four-digit basins. The overall difference in monthly distribution between the
eastern and western portions of the country is seen by comparing Figures B3 and B4.
References
Bedard, Roger J.; Previsic, Mirko; Polagye, Brian L. 2009. Marine energy: how much development
potential is there? Hydro Review 28(4).
EIA. 2010. Annual energy outlook 2010. DOE/EIA-0383(2010). Washington, DC: Energy Information
Administration, U.S. Department of Energy.
Kenny, Joan F.; Barber, Nancy L.; Hutson, Susan S.; Linsey, Kristin S.; Lovelace, John K.; Maupin, Molly
A. 2009. Estimated use of water in the United States in 2005. Circular 1344. Reston, VA: U.S.
Geological Survey.
Musial, W. 2008. Status of wave and tidal power technologies for the United States. Technical Report
NREL/TP-500-43240: National Renewable Energy Lab.
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RMRS-GTR-295: Vulnerability of U.S. water supply to shortage: a technical document supporting the Forest Service 2010 RPA: Appendices
Figure B1. Location of four clusters of ASRs for Table B2.
4 clusters of ASRs
16
14
Percent
12
10
C1
C2
C3
C4
8
6
4
2
Jan Feb Mar Apr May Jun
Jul
Aug Sep Oct
Nov Dec
Figure B2. Monthly distribution of annual municipal water delivery for four clusters of
ASRs (see Table B2).
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East (WRRs 1-9)
16
1
14
2
Percent
12
3
10
4
8
5
6
6
7
8
4
9
2
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Figure B3. Monthly distribution of annual municipal water delivery for eastern WRRs (see Table B3).
West (WRRs 10-18)
16
10
11
14
Percent
12
12
10
13
8
14
15
6
16
4
17
18
2
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Figure B4. Monthly distribution of annual municipal water delivery for western WRRs (see Table B3).
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12
Table B1. Monthly distribution of annual municipal water delivery for ASRs.
ASR
101
102
103
104
105
106
201
202
203
204
205
206
207
301
302
303
304
305
306
307
308
309
401
402
404
405
406
407
408
501
502
503
504
505
506
507
601
602
701
702
703
704
705
801
802
803
901
1001
1002
1003
1004
No. of
basins
4
2
1
1
1
0
1
0
1
1
2
1
1
3
2
2
3
1
2
2
1
2
2
1
2
2
2
1
2
0
3
3
1
3
1
1
2
2
3
3
3
3
0
2
3
2
3
1
2
0
4
No.
of
cities
4
2
1
1
1
0
1
0
1
1
5
1
1
3
2
3
3
1
3
3
1
2
3
2
3
2
2
1
3
0
4
3
1
4
1
1
2
2
3
4
4
6
0
2
3
3
3
1
3
0
4
Percent
Jun
Jul
Jan
Feb
Mar
Apr
May
Aug
Sep
Oct
Nov
Dec
8.7
7.2
7.5
7.8
8.0
7.8
6.9
7.6
7.1
7.2
8.3
7.2
7.5
7.4
8.1
8.0
7.5
7.6
7.3
7.9
8.2
8.0
7.7
8.5
8.4
8.5
9.7
9.4
9.2
8.7
8.9
10.5
9.9
10.5
10.1
9.4
10.8
10.6
10.2
10.4
8.2
9.7
9.9
8.9
8.4
8.2
8.1
7.8
8.2
8.0
7.8
7.3
7.4
7.4
7.4
8.0
7.2
7.3
7.5
7.4
8.5
8.4
8.9
8.0
8.3
8.5
8.3
9.0
7.9
8.1
8.0
8.1
9.9
8.1
7.9
8.4
7.6
7.9
7.6
6.7
7.4
7.7
7.6
7.5
7.5
7.7
7.9
7.8
7.2
8.1
7.2
8.3
8.3
8.1
8.8
7.2
8.0
6.8
7.1
6.8
6.0
7.7
7.8
7.0
7.6
6.7
6.9
7.5
7.1
7.2
7.4
6.8
7.7
7.1
7.9
7.6
7.9
8.3
8.5
7.9
7.8
6.3
8.4
8.3
8.1
6.7
7.3
6.4
8.0
7.8
7.6
8.1
7.0
8.2
7.9
7.6
8.3
7.8
8.2
6.6
7.8
8.2
7.8
8.8
9.0
8.8
7.8
7.8
7.3
7.9
7.6
7.1
7.9
7.2
7.8
8.0
7.7
8.0
9.0
8.6
6.6
8.5
9.4
9.5
9.5
9.2
9.0
8.3
8.5
7.5
7.8
8.2
7.9
8.4
7.7
8.2
7.5
7.9
8.4
9.0
8.6
8.8
8.7
9.4
9.8
9.2
8.5
9.6
9.6
10.0
10.4
8.6
9.1
8.1
8.9
9.3
9.8
9.9
9.4
9.0
9.0
8.8
7.9
9.2
9.6
10.4
8.4
8.5
9.0
8.7
10.3
9.6
10.0
11.1
9.5
10.2
9.9
9.6
9.6
8.2
8.9
9.7
8.9
9.4
9.6
9.5
9.8
8.5
8.5
8.6
8.8
9.6
9.7
9.8
8.9
11.1
9.7
10.3
9.7
9.0
8.1
9.1
8.4
8.4
10.9
9.1
8.6
9.7
8.7
7.9
8.3
9.8
8.5
9.7
8.8
8.4
9.8
8.5
10.9
8.2
8.5
9.1
8.1
8.3
8.2
8.4
8.7
8.5
8.6
7.7
8.4
8.6
9.0
8.3
8.4
8.3
8.3
8.9
7.8
8.6
7.6
8.6
8.1
7.7
7.9
7.4
8.8
7.7
7.5
8.2
7.9
8.3
7.9
9.0
7.6
8.6
7.5
7.5
8.3
7.4
7.6
7.2
7.6
8.0
8.0
7.8
8.1
9.7
7.8
7.1
7.0
7.8
7.9
7.6
7.3
7.9
7.9
7.9
8.1
7.4
7.7
7.5
7.8
8.1
8.3
7.9
8.3
7.7
7.8
7.7
7.8
7.7
6.4
7.1
7.3
7.7
7.9
7.2
8.2
7.0
7.4
7.6
7.7
7.6
6.4
6.6
6.9
6.6
8.3
7.9
8.5
7.9
8.9
7.6
7.3
7.6
6.5
7.1
7.4
7.6
8.0
7.8
8.6
7.6
8.3
7.9
7.8
7.7
6.5
7.7
7.6
7.0
8.3
8.5
8.5
8.5
8.6
8.3
8.2
8.6
8.3
7.9
8.4
8.2
8.7
9.6
8.3
9.3
9.2
9.3
8.8
9.0
8.9
9.7
10.3
9.4
9.1
9.3
8.1
9.4
9.2
10.1
9.3
9.2
11.7
10.9
10.5
10.6
9.3
9.8
8.0
10.0
9.4
9.0
9.0
9.4
13.1
10.6
9.7
10.1
8.3
8.5
8.9
8.4
8.4
8.6
8.9
8.8
10.5
9.6
8.7
9.8
8.1
8.1
8.5
8.2
8.4
8.4
8.6
8.6
7.7
8.3
8.3
8.0
7.8
7.4
8.4
8.2
6.8
7.7
8.2
7.8
7.1
7.1
7.4
7.2
8.1
7.8
7.8
7.9
7.6
7.9
8.2
8.0
6.9
7.2
7.5
7.8
7.5
7.4
7.1
7.4
5.5
4.6
7.0
7.4
6.6
7.0
5.0
4.6
7.2
7.0
7.7
7.3
5.7
4.4
7.4
7.1
8.3
7.2
6.9
4.5
8.6
7.9
8.8
7.9
9.2
6.1
10.1
8.8
10.8
9.1
9.5
8.7
9.8
8.9
9.3
10.2
17.1
14.4
10.1
10.0
8.4
11.0
15.1
15.0
9.4
10.1
8.9
9.8
9.3
15.9
8.5
9.3
8.8
8.3
6.0
11.2
6.9
8.6
7.7
7.3
5.4
5.8
7.4
7.6
7.7
7.4
5.4
4.8
5.7
4.3
4.8
5.1
7.3
10.7
13.8
15.0
14.2
8.5
6.0
4.6
RMRS-GTR-295: Vulnerability of U.S. water supply to shortage: a technical document supporting the Forest Service 2010 RPA: Appendices
1005
1006
1007
1008
1009
1010
1011
1101
1102
1103
1104
1105
1106
1107
1201
1202
1203
1204
1205
1302
1303
1304
1305
1401
1402
1403
1501
1502
1503
1601
1602
1603
1604
1701
1702
1703
1704
1705
1706
1707
1801
1802
1803
1804
1805
1806
1807
Sum
4
1
2
4
2
3
3
1
1
3
2
3
2
1
1
2
3
1
2
3
2
0
1
3
3
2
1
2
5
2
1
2
1
1
3
2
1
3
1
0
1
2
2
1
1
2
1
181
5
1
4
7
2
3
5
1
1
3
2
4
3
1
2
7
6
3
6
3
4
0
1
4
3
2
1
2
5
2
1
2
1
1
3
2
1
3
1
0
1
3
3
1
2
3
1
232
13
6.0
6.5
5.4
5.4
6.9
6.0
6.7
7.2
4.3
5.6
7.2
6.2
7.4
7.0
7.6
7.2
6.6
6.1
7.4
5.7
7.0
5.1
5.9
5.0
5.2
6.3
5.8
6.3
6.6
3.7
5.2
6.5
5.4
6.3
5.7
6.3
6.0
6.3
5.5
6.8
5.7
6.4
5.2
6.6
5.5
5.5
6.7
6.3
6.4
7.3
4.2
6.0
7.2
6.4
6.8
6.4
7.0
6.8
6.5
7.0
7.5
6.3
6.3
6.2
7.0
6.0
6.1
7.3
7.3
8.0
7.5
6.6
8.8
7.7
7.6
7.6
7.0
7.8
7.4
7.7
8.0
7.7
5.9
8.5
7.4
8.4
9.0
9.5
8.2
8.8
8.6
8.4
9.6
8.8
8.7
9.8
9.0
8.5
8.7
8.8
8.7
9.0
8.5
9.5
8.8
9.9
9.7
12.3
11.5
10.7
11.7
10.3
8.9
13.8
10.9
9.3
11.1
11.6
10.2
9.8
9.6
10.4
9.9
9.0
12.4
10.4
15.5
13.6
15.1
14.8
11.9
12.0
10.8
10.4
14.2
10.7
10.9
12.8
11.5
12.1
10.5
11.0
11.3
11.8
10.0
12.6
10.4
16.5
12.5
13.7
12.9
11.2
11.3
11.3
11.0
13.5
12.1
10.3
11.6
11.2
12.0
11.2
10.9
11.0
11.8
10.3
11.0
8.6
10.7
9.1
10.7
9.8
9.6
9.7
9.5
9.6
11.7
10.7
9.5
9.2
7.9
8.9
9.3
9.0
9.5
9.5
8.9
11.1
9.1
6.7
7.6
6.9
7.9
8.2
8.3
8.8
8.8
8.7
9.2
8.4
7.5
8.0
8.7
8.3
9.1
8.3
8.5
8.6
7.9
8.1
5.6
6.4
5.1
6.0
6.4
6.6
6.6
7.2
5.3
6.4
7.1
6.4
6.8
7.2
6.9
7.8
6.9
6.2
7.7
6.3
9.5
5.3
6.6
5.3
5.5
6.6
6.2
6.6
7.1
4.4
5.5
7.2
6.1
6.0
6.5
6.7
6.6
6.7
6.6
7.6
5.6
7.0
6.8
5.4
4.0
4.7
6.1
6.3
5.7
4.9
4.4
4.2
4.1
4.7
4.6
5.7
3.8
6.9
7.5
6.6
5.3
4.3
4.3
6.1
5.9
5.0
4.3
3.4
4.7
3.7
4.7
4.5
5.2
3.9
6.5
7.3
7.6
5.9
4.7
5.2
6.0
6.7
5.5
4.7
3.9
4.8
4.2
4.9
6.0
6.1
4.8
7.2
7.4
7.8
7.0
7.4
7.2
8.1
7.2
6.7
3.4
5.0
5.1
5.4
6.2
7.2
6.3
5.3
7.4
7.4
9.4
10.2
11.7
9.6
10.5
9.4
9.8
4.3
8.3
9.9
8.2
9.4
8.8
8.9
9.3
7.9
7.2
9.7
14.1
13.8
12.0
13.3
10.4
12.2
10.6
13.7
12.1
12.3
11.9
11.4
11.5
11.7
9.1
8.0
11.1
14.6
13.7
14.2
10.2
11.3
12.5
13.3
18.2
13.1
14.5
15.9
14.4
14.4
18.0
11.4
8.8
10.6
12.2
13.2
12.2
8.2
11.1
10.6
17.7
14.2
13.5
14.2
15.7
14.2
13.0
17.0
12.1
12.4
8.7
8.3
9.5
11.2
11.5
9.9
10.0
15.5
13.7
12.3
13.8
10.7
11.1
9.7
12.3
9.8
11.1
8.3
5.9
7.9
8.5
7.7
8.3
8.6
10.0
7.4
8.6
10.3
6.3
7.9
8.0
6.0
7.8
9.5
7.0
5.4
5.3
5.7
6.3
7.0
7.0
5.8
4.1
6.0
5.5
4.7
5.2
5.7
3.9
7.1
6.8
6.5
5.6
4.5
5.1
6.0
6.5
6.5
5.5
3.7
5.4
3.9
4.9
4.7
5.5
3.9
6.8
6.8
5.2
3.3
4.3
6.2
6.0
5.5
4.4
4.9
3.4
4.3
6.3
5.8
4.6
4.2
6.4
3.8
5.2
6.0
6.1
5.0
4.9
6.4
5.4
6.5
7.3
6.7
6.0
6.8
8.6
7.6
9.5
7.6
7.4
9.6
11.0
11.8
11.0
11.6
10.2
9.2
10.5
11.5
12.2
13.4
13.1
10.5
11.4
12.8
13.9
12.7
14.6
13.1
11.0
10.3
12.1
13.1
11.3
12.9
11.1
10.6
10.4
11.8
11.2
9.0
10.3
9.5
9.9
10.2
8.0
8.4
5.9
7.7
6.7
7.3
8.6
6.9
5.3
5.5
6.6
5.2
7.1
7.9
7.2
5.3
RMRS-GTR-295: Vulnerability of U.S. water supply to shortage: a technical document supporting the Forest Service 2010 RPA: Appendices
14
Table B2. Monthly distribution of annual municipal water delivery for clusters of ASRs.
Cluster
1
2
3
4
Sum
No.
of
ASRs
25
16
45
5
91
No.
of
cities
72
41
106
13
232
Jan
Feb
Mar
Apr
May
6.4
4.8
7.8
4.5
5.9
4.5
7.3
4.1
6.5
5.2
7.7
4.4
7.3
6.4
7.8
4.8
8.8
9.5
8.3
6.7
Percent
Jun Jul
Aug
Sep
Oct
Nov
Dec
10.5
12.0
9.2
10.6
11.2
14.0
9.6
15.3
9.9
10.8
9.0
14.5
8.4
7.6
8.4
10.0
6.9
5.3
7.7
6.2
6.5
5.0
7.7
5.1
11.6
15.0
9.5
13.9
Table B3. Monthly distribution of annual municipal water delivery for WRRs.
WRR
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
Sum
No. of
basin
s
9
7
18
12
12
4
12
7
3
26
13
9
6
8
8
6
11
10
181
No. of
cities
9
10
21
16
14
4
17
8
3
35
15
24
8
9
8
6
11
14
232
Jan
Feb
Mar
8.0
8.3
7.5
7.8
7.9
7.7
7.1
7.3
7.4
5.8
6.4
6.9
6.3
4.7
5.9
4.4
5.6
4.8
7.4
7.8
7.1
7.2
7.4
7.7
6.6
7.0
7.0
5.3
5.6
6.2
6.1
4.7
5.4
4.2
5.4
4.6
7.9
8.2
7.5
7.8
8.1
7.5
7.2
7.3
7.3
5.6
6.4
6.9
6.5
5.3
5.9
4.5
6.3
5.1
Ap
r
7.7
7.8
8.1
7.6
7.9
7.8
7.2
7.5
7.2
6.3
7.7
7.7
7.1
7.2
7.0
4.6
6.8
6.3
May
Percent
Jun
Jul
Aug
Sep
Oct
Nov
Dec
8.2
8.2
8.8
7.9
8.4
8.4
8.2
8.4
7.9
8.2
9.1
8.7
9.2
10.6
9.8
7.5
8.5
8.8
9.0
8.6
9.4
9.0
9.1
8.9
9.6
9.7
9.1
10.7
10.8
9.8
11.3
13.4
11.9
11.9
10.5
10.9
10.0
9.1
9.2
9.9
9.5
9.2
10.9
9.6
11.0
13.6
11.6
10.9
10.1
12.6
10.4
15.2
13.6
12.6
8.8
8.7
9.0
9.1
8.5
8.8
9.7
9.5
9.8
11.0
9.6
9.2
10.0
9.5
10.2
13.8
10.6
11.5
8.1
8.3
8.5
8.4
8.2
8.6
8.1
8.9
8.3
8.1
8.4
8.6
8.0
7.3
8.4
9.2
7.7
9.3
7.6
8.0
8.1
7.6
7.7
8.0
7.2
7.8
7.3
6.0
6.6
7.2
7.5
5.4
6.9
5.5
5.8
7.0
7.6
8.2
7.6
7.8
7.9
8.1
7.4
7.6
7.4
5.6
6.1
6.9
6.2
5.0
6.4
4.9
5.5
6.4
9.7
8.8
9.2
9.9
9.2
9.3
10.9
9.3
10.2
13.8
11.7
10.9
11.6
14.2
11.9
14.3
13.6
12.7
RMRS-GTR-295: Vulnerability of U.S. water supply to shortage: a technical document supporting the Forest Service 2010 RPA: Appendices
AppendixC:ProjectedPrecipitationandPotential
Evapotranspiration
In the following maps of precipitation the scale is truncated at +10 cm and -10 cm.
(A)
(B)
(C)
(D)
Figure C1. Change from current conditions in mean precipitation (cm/yr) with the A1B-CGCM future for:
(A) 2020; (B) 2040; (C) 2060; (D) 2080.
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RMRS-GTR-295: Vulnerability of U.S. water supply to shortage: a technical document supporting the Forest Service 2010 RPA: Appendices
(A)
(B)
(C)
(D)
Figure C2. Change from current conditions in mean precipitation (cm/yr) with the A2-CGCM future for:
(A) 2020; (B) 2040; (C) 2060; (D) 2080.
(A)
(B)
(C)
(D)
Figure C3. Change from current conditions in mean precipitation (cm/yr) with the B2-CGCM future for:
(A) 2020; (B) 2040; (C) 2060; (D) 2080.
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RMRS-GTR-295: Vulnerability of U.S. water supply to shortage: a technical document supporting the Forest Service 2010 RPA: Appendices
(A)
(B)
(C)
(D)
Figure C4. Change from current conditions in mean precipitation (cm/yr) with the A1B-CSIRO future for:
(A) 2020; (B) 2040; (C) 2060; (D) 2080.
(A)
(B)
(C)
(D)
Figure C5. Change from current conditions in mean precipitation (cm/yr) with the A2-CSIRO future for:
(A) 2020; (B) 2040; (C) 2060; (D) 2080.
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RMRS-GTR-295: Vulnerability of U.S. water supply to shortage: a technical document supporting the Forest Service 2010 RPA: Appendices
(A)
(B)
(C)
(D)
Figure C6. Change from current conditions in mean precipitation (cm/yr) with the B2-CSIRO future for:
(A) 2020; (B) 2040; (C) 2060; (D) 2080.
(A)
(B)
(C)
(D)
Figure C7. Change from current conditions in mean precipitation (cm/yr) with the A1B-MIROC future
for: (A) 2020; (B) 2040; (C) 2060; (D) 2080.
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RMRS-GTR-295: Vulnerability of U.S. water supply to shortage: a technical document supporting the Forest Service 2010 RPA: Appendices
(A)
(B)
(C)
(D)
Figure C8. Change from current conditions in mean precipitation (cm/yr) with the A2-MIROC future for:
(A) 2020; (B) 2040; (C) 2060; (D) 2080.
(A)
(B)
(C)
(D)
Figure C9. Change from current conditions in mean precipitation (cm/yr) with the B2-HADN future for:
(A) 2020; (B) 2040; (C) 2060; (D) 2080.
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RMRS-GTR-295: Vulnerability of U.S. water supply to shortage: a technical document supporting the Forest Service 2010 RPA: Appendices
In the following maps of potential evapotranspiration the scale is truncated at +1 mm and -1 mm.
(A)
(B)
(C)
(D)
Figure C10. Change from current conditions in mean potential evapotranspiration (mm/day) with the
A1B-CGCM future for: (A) 2020; (B) 2040; (C) 2060; (D) 2080.
(A)
(B)
(C)
(D)
Figure C11. Change from current conditions in mean potential evapotranspiration (mm/day) with the
A2-CGCM future for: (A) 2020; (B) 2040; (C) 2060; (D) 2080.
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RMRS-GTR-295: Vulnerability of U.S. water supply to shortage: a technical document supporting the Forest Service 2010 RPA: Appendices
(A)
(B)
(C)
(D)
Figure C12. Change from current conditions in mean potential evapotranspiration (mm/day) with the
B2-CGCM future for: (A) 2020; (B) 2040; (C) 2060; (D) 2080.
(A)
(B)
(C)
(D)
Figure C13. Change from current conditions in mean potential evapotranspiration (mm/day) with the
A1B-CSIRO future for: (A) 2020; (B) 2040; (C) 2060; (D) 2080.
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RMRS-GTR-295: Vulnerability of U.S. water supply to shortage: a technical document supporting the Forest Service 2010 RPA: Appendices
(A)
(B)
(C)
(D)
Figure C14. Change from current conditions in mean potential evapotranspiration (mm/day) with the
A2-CSIRO future for: (A) 2020; (B) 2040; (C) 2060; (D) 2080.
(A)
(B)
(C)
(D)
Figure C15. Change from current conditions in mean potential evapotranspiration (mm/day) with the
B2-CSIRO future for: (A) 2020; (B) 2040; (C) 2060; (D) 2080.
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RMRS-GTR-295: Vulnerability of U.S. water supply to shortage: a technical document supporting the Forest Service 2010 RPA: Appendices
(A)
(B)
(C)
(D)
Figure C16. Change from current conditions in mean potential evapotranspiration (mm/day) with the
A1B-MIROC future for: (A) 2020; (B) 2040; (C) 2060; (D) 2080.
(A)
(B)
(C)
(D)
Figure C17. Change from current conditions in mean potential evapotranspiration (mm/day) with the
A2-MIROC future for: (A) 2020; (B) 2040; (C) 2060; (D) 2080.
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RMRS-GTR-295: Vulnerability of U.S. water supply to shortage: a technical document supporting the Forest Service 2010 RPA: Appendices
(A)
(B)
(C)
(D)
Figure C18. Change from current conditions in mean potential evapotranspiration (mm/day) with the
B2-HADN future for: (A) 2020; (B) 2040; (C) 2060; (D) 2080.
24