1. Survey of Data Related to Municipal Water Systems in Utah
Paul Harms
CEE 6440: GIS in Water Resources
Utah State University
Fall 2002

2. Relating ancillary data to water use data:
May help explain why water systems use different amounts of water.
May improve estimates of existing water demands.
May allow categorization for better use of random sampling.
May improve predictions of future water demands, using projections of the related data.

3. Correlations with residential water use:
IWR-MAIN
Household income
Persons per household
Housing density
Average maximum temperature
Rainfall
Price of water
Wasatch Front Model
Persons per household
Lot size
Assessed value
Soil type
Season
Dual system

10. Town Size: Total GPCPD Domestic GPCPD Total Use number mean stan.dev.
Total GPCPD
Domestic GPCPD
Total Use
number
mean
stan.dev.
coef. var.
Population
(ac-ft/yr)
small 38
289.13
133.58
0.46
235.86
126.58
0.54
64660
21180
mid 9
275.49
114.67
0.42
191.44
76.34
0.40
114863
38533
metro 13
188.21
81.74
0.43
131.68
47.69
0.36
748129
181726
total 60
265.22
0.48
206.62
114.56
0.55
927652
241438

11. Domestic Fraction: Dual Fraction: r n sig.?
GPCPD/Domestic Fraction Correlation Coef. =
0.0207 60 no
Metro GPCPD/Domestic Fraction Correlation Coef. = 13
yes
Dual Fraction:
GPCPD/Dual Fraction Correlation Coef. =
Metro GPCPD/Dual Fraction Correlation Coef. =

16. >90% Surface Water Source:
Well Fraction: r n
sig.?
GPCPD/Well Fraction Correlation Coef. = 60 no
Metro GPCPD/Well Fraction Correlation Coef. =
0.4122 13
>90% Surface Water Source:
Average GPCPD
# yes: 4
169.70
# no: 56
272.04

19. Average Maximum Temperature:
Latitude: r n
sig.?
GPCPD/Latitude Correlation Coef. = 60 no
Average Maximum Temperature:
GPCPD/Temperature Correlation Coef. =
Metro GPCPD/Temperature Correlation Coef. =
0.5516 13

20. Average Annual Precipitation:
sig.?
GPCPD/Ann. Precip. Correlation Coef. = 60 no
Metro GPCPD/Ann. Precip. Correlation Coef. = 13
yes
Average JJAS Precipitation:
GPCPD/JJAS Precip. Correlation Coef. =
0.0367
Metro GPCPD/JJAS Precip. Correlation Coef. =

22. Water pricing structures
Flat charge
Uniform rate
Increasing block rate
Decreasing block rate

23. Example: Escalante Culinary Water
Unchanging $17 for the first 15,000 gallons used each month.
$1.50 per 1000 gallons for the next 15,000 gallons.
$2 per 1000 gallons after 30,000 gallons.

24. Rate Structure: # no data: 18 # flat charge: Average GPCPD
Average GPCPD
# uniform rate: 25
uniform
275.31
# increasing block: 15
increasing
287.86
# decreasing block: 2
decreasing
297.56
# metro uniform: 6
metro uniform
230.09
# metro increasing: 3
metro increasing
90.01

25. Linear Regression Metro GPCPD = 248.966 – 161.851*(dual fraction).
se = R2adj =
Metro Total Use = *(pop) – *(dual) *(temp) *(annPrecip) – *(sumPrecip).
se = R2adj =

26. Problems Obtaining trustworthy data Sample not random
Different data types sometimes cover different years
Some correlations may exist but be obscured by stronger correlations

27. Conclusions Correlations significantly different from zero:
more population – more total use
more dual – less GPCPD
more metro annual precipitation – less GPCPD
lower metro domestic fraction – more GPCPD

28. Conclusions Other trends: smaller water system – more variable GPCPD
higher latitude – less GPCPD
higher metro and mid temperature – more GPCPD
more metro summer precipitation – less GPCPD
metro increasing block rates – less GPCPD
higher base rate limit – less GPCPD (counter-intuitive)
higher rate per 1000 gallons – less GPCPD
more surface water dependent – less GPCPD

29. Conclusions
Grouping by town size appeared appropriate, and may be useful for random sampling.
Large water systems appeared predictable.
Small water systems appeared unpredictable.
Averages over many small systems may be useful.
Estimating or predicting water use at individual small systems with this ancillary data would not be valid.