RESOURCES and the in ARIZONA SOUTHWEST

RESOURCES and the in ARIZONA SOUTHWEST
':.
r-;
Volume 15
HYDROLOGY
and WATER
RESOURCES
in ARIZONA
and the
SOUTHWEST
PROCEEDINGS OF THE 1985 MEETINGS
OF THE
ARIZONA SECTION
AMERICAN WATER RESOURCES ASSOCIATION
AND THE
HYDROLOGY SECTION
ARIZONA- NEVADA ACADEMY OF SCIENCE
APRIL 27, 1985, LAS VEGAS, NEVADA
Volume 15
Hydrology and Water Resources in Arizona and the Southwest
Proceedings of the 1985 Meetings
of the
Arizona Section -
American Water Resources Association
and the
Hydrology Section Arizona- Nevada Academy of Science
April 27, 1985, Las Vegas, Nevada
TABLE OF CONTENTS
Page
Introduction
v
Ordering Information for AWRA Publications
vii
Snowpack Density: An Index of Snowpack Condition
Peter F. Ffolliott
1
Problems of Simplification in Hydrologic Modeling
H.B. Osborn, C.L. Unkrich and L. Frykman
7
Evaluation of Hydrologic and Hydraulic Procedures for
Small Urban Watersheds in the Southwest
L.J. Lane, T.J. - Ward and J.J. Stone
21
Storm Runoff and Sediment Production After Wildfire
in Chaparral
Alden R. Hibbert
31
Estimation of Soil Texture and Plant Available Water
by Correlation with the Laser Light Scattering Method
R.L. Haverland, D.F. Post, L.R. Cooper and E.D. Shirley
43
Casa del Agua: Residential Water Conservation Retrofit
Richard Brittain, K. James DeCook and Kennith E.
Foster
59
Arizona Water Information Center. Foundation and
Activities
Kennith E. Foster and L.G. Wilson
65
An Empirical Evaluation of the Costs of Groundwater
Overdraft
David B. Bush and William E. Martin
71
Economic Feasibility of Artificial Recharge and
Recovery of Imported Water in Butler Valley, Arizona
J.M. Abe and B.C. Saliba
87
Evaluating the Effectiveness of Curtain Wells Against
Subsurface Flooding in Yuma, Arizona
Don W. Young and Earl E. Burnett
99
iii
INTRODIICTION
On April 27, 1985, the Arizona Section of the American Water Resources
Association and the Hydrology Section of the Arizona -Nevada Academy of Science
met in Las Vegas, Nevada, to discuss and disseminate information on water issues
and current research results. This document, a proceedings of the 15th annual joint
meeting of these two organizations, was compiled by Anna Elias -Cesnik, editor,
Office of Arid Lands Studies (OALS), University of Arizona. Margo Gumina,
secretary, OALS, assisted with the word processing.
v
ORDERING INFORMATION FOR AWRA PUBLICATIONS
Copies of the following documents can be ordered from Arizona Section, American
Water Resources Association, 845 North Park Avenue, Tucson, Arizona 85719, c/o
Dale Wright.
Hyrdrology and Water Resources in Arizona and the Southwest, Volumes 7 through
10 (Proceedings of the 1977 -1980 meetings) $12 per copy. Volumes 11 through 15
(proceedings of the 1981 -1985 meetings) $14 per copy.
Ground Water Quality Management (October 29, 1982, symposium) $10 per copy.
Urban Water Management:
Augmentation and Conservation (October 21, 1983,
symposium) $10 per copy.
Water Quality and Environmental Health (November 9, 1984, symposium) $10 per
copy.
vii
SNOWPACK DENSITY: AN INDEX OF SNOWPACK CONDITION
by
Peter F. Ffolliott
School of Renewable Natural Resources
85721
Tucson, Arizona
Introduction
Snowpack density is often a useful index to the stage of "ripening" in
a snowpack. In general, freshly fallen snow has a density of 0.10 gm cm -3.
A density of 0.35 to 0.50 gm em -3 is characteristic of a snowpack undergoing
In most instances, additional inputs of energy
metamorphosis and ripening.
will cause the snowpack to melt and runoff to occur.
To evaluate the usefulness of snowpack density as an index of snowpack
condition in the forest types of Arizona, a number of studies have been
conducted, as described below.
Description of the Studies
Over an eight -year period, a series of studies were conducted to obtain
baseline data on the spatial and temporal variations in snowpack densities
in the mixed conifer forests, the aspen forests, and the ponderosa pine
forests of Arizona. Collectively, these studies were designed to:
1.
describe the variations in snowpack densities;
2.
relate these variations to hydrographie characteristics, where
possible; and
3.
determine the effects of differences in forest density,
and potential solar radiation on snowpack densities.
elevation,
Study Areas
The studies were carried out on several locations that were
representative of the forest types of Arizona. In particular, measurements
in the mixed conifer forests were obtained on experimental watersheds on the
Thomas Creek drainage in east -central Arizona (Rich and Thompson 1970.
Seven coniferous and two deciduous tree species (Douglas -fir, white fir,
white
pine,
corkbark fir, Engelmann spruce, ponderosa pine, southwestern
1
quaking aspen, and Gambel oak) were found on these watersheds. Topography
varied, with the lower and middle slopes of the watersheds quite steep.
Soils were derived from basaltic parent materials.
Elevations varied from
8,400 to 9,200 feet.
Annual precipitation averaged 28 inches, approximately
one -third of which occurred during the snowfall season of November through
April.
Quaking aspen does not occur as extensive, continuous forests in
Arizona, rather it is found as isolated stands varying from less than 10 to
over 100 acres in size.
Two stands, on the lower slopes of the San
Francisco Mountains near Flagstaff, comprised the study areas for
measurements in the aspen forest (Timmer et al. 1984). Average slope was
less than 5 percent.
Soils were basaltic. Elevations ranged from 7,900 to
8,100 feet.
Annual precipitation was approximately 26 inches.
Measurements in the ponderosa pine forests were collected on
experimental watersheds on the Beaver Creek drainage in north -central
Arizona (Brown et al. 1974).
Cutover ponderosa pine comprised over 75
percent of the forest overstory on these watersheds, with Gambel oak and
alligator juniper as intermingling species.
Few slopes exceeded 15 percent,
and the general aspect was southwest.
Soils, derived from volcanics, were
primarily basaltic.
The elevation ranged from 6,500 to 7,800 feet. Annual
precipitation averaged 24 inches, with nearly one -half occurring in the
winter months.
Collection of Source Data
Estimates of snowpack density were obtained from total snow depth and
corresponding water equivalent measurements taken with a federal snow
sampler and scale at randomly located sample points throughout the winter
periods of the year of study. Different years were measured in each of the
forest types studied, although the range of conditions sampled within the
forest types was similar.
In general, the measurements were taken prior to
peak seasonal snowpack accumulation, at the time of peak seasonal snowpack
accumulation, and during the snowmelt- runoff period.
On the experimental watersheds that were studied, streamflow was
measured at a water -stage gaging station.
Daily streamflow values,
expressed in terms of area -inches, were computed from a streamflow
discharge -water stage rating curve.
Data required to develop expressions of forest density, elevation, and
potential solar radiation were also obtained at the sample points.
Forest
density, in square feet of basal area per acre, was estimated by point
sampling techniques (Avery and Burkhart 1983). The elevation of each
sample point was estimated from 7 1/2- minute USGS topographic maps.
Potential solar radiation, in gm calories cm2, received
on selected index
dates was determined from slope and aspect measurements (Frank and Lee
1966).
2
Results and Discussion
Regardless of the forest type, the snowpack density values appeared to
Furthermore, the coefficients of variation
be normally distributed.
remained statistically constant throughtout a major portion of the runoff
Ripening of the snowpacks was rapid, once snowpack metamorphosis
Empirical relationships between snowpack density and
was initiated.
associated inventory -prediction variables, while statistically weak,
periods.
reflected some qualitative characteristics of Arizona's snowpack as affected
by these variables.
Other, more specific results are described in the
following paragraphs.
Mixed Conifer Forests
The snowpack densities observed on Thomas Creek ranged from less than
An average snowpack density of between 0.35 and
0.25 to over 0.55 gm cm -3.
0.40 gm cm -3 represented ripe conditions, as the snowpacks generally
remained in this density range for most of the runoff periods. Similar
results were obtained from an earlier study in the mixed conifer forest
(Ffolliott and Thompson 1977). Occasionally, snowpack densities exceeded
this range near the end of runoff, when only residual patches of snow
remained on the experimental watersheds.
Comparable snowpack densities have been reported in mixed conifer
forests elsewhere in the western United States.
Work (1948) presented data
for Crater Lake, Oregon, which indicated that snowpack melting does not
According
occur until a density between 0.40 and 0.50 gm cm -3 is attained.
to Kittredge (1953), snowpack densities between 0.40 and 0.50 gm cm -3 are
required before water will drain from snowpacks under ponderosa -sugar pine -
fir forests in California.
Gary and CQltharp (1967) reported maximum
snowpack densities of 0.35 to 0.40 gm cm
cover types in New Mexico.
in Douglas -fir, aspen, and grass
Forest density, elevation, and potential solar radiation were subjected
to correlation analyses to determine the magnitude of their individual
associations with snowpack densities at peak seasonal accumulation. In
general, higher snowpack densities were observed under sparsely stocked,
rather than densely stocked, mixed conifer stands. Quite possibly, this
pattern was the result of a greater proportion of "old" snow in the samples
taken on sites with low forest density; more snow accumulates on these
sites, therefore, the snow persists longer.
No significant correlations existed between snowpack densities and
elevation, nor between snowpack densities and potential solar radiation,
possibly due to the relatively limited range of values for these inventory prediction variables on Thomas Creek.
3
Aspen Forests
Snowpack density values in the aspen forests were similar to those
observed in the mixed conifer forests, ranging from nearly 0.20 to over 0.45
gm cm -3.
This finding was not surprising, however, as aspen stands in
Arizona are often found intermingled with the mixed conifer forests.
Snowpack densities between 0.35 and 0.45 gm cm -3 apparently represented ripe
conditions.
Once this range was attained, snowpack density values did not
drastically change, although water equivalents decreased as the snowpacks
melted.
Empirical relationships describing snowpack densities as functions of
forest density, elevation, and solar radiation were either statistically
Again, the
nonsignificant or, if significant, of little predictive value.
relatively narrow range of values for the inventory- prediction variables
within the aspen stands on the study areas undoubtedly
weak relationships.
contributed to these
Ponderosa Pine Forests
The snowpack densities in the ponderosa pine forests on Beaver Creek
In general, these
ranged, for the most part, between 0.20 and 0.50 gm cm -3.
values are comparable to an earlier, exploratory study (Ffolliott and Thoru4
1969).
An average snowpack density between 0.35 and
0.40
gm cm
represented ripe conditions, because the snowpacks remained in this density
range for most of the runoff periods. Only residual snow patches with
densities of about 0.50 gm cm -3 remained on the watersheds at the end of
Lejcher (1969) observed that a snowpack in a ponderosa pine stand
runoff.
near Flagstaff was ripe and had begun to yield melt water at a similar
density level.
Higher snowpack densities were found under relatively sparsely stocked
Also,
higher snowpack
ponderosa pine stands at peak seasonal accumulation.
densities were observed at higher, rather than lower, elevations. The
occurrence of these high snowpack densities was probably the result of a
greater proportion of "old" snow in the integrated samples obtained on
these sites at peak seasonal accumulation.
No significant correlations existed between snowpack densities and
to the relatively limited
potential solar radiation due, it was believed,
range of potential solar radiation values.
Conclusions
From the above studies of snowpack densities in the forest types of
Arizona, the following conclusions can be made:
1.
snowpack densities are normally distributed;
4
2. coefficients of variation for snowpack density remain relatively
constant throughout a major portion of the runoff period;
3. an average snowpack density of between 0.35 and 0.40 gm cm -3
represents ripe snowpack conditions;
4.
higher snowpack densities occur under relatively sparsely stocked
mixed conifer stands and ponderosa pine stands; and
5. other relationships between snowpack densities and inventory prediction variables are either statistically nonsignificant
or, if significant, of little predictive value.
5
References Cited
Avery, Thomas Eugene, and Harold E. Burkhart.
McGraw -Hill Book Co., New York, 331 p.
1983.
Forest measurements.
Brown, Harry E., Malehus B. Baker, Jr., James J. Rogers, Warren P. Clary, J.
L. Kovner, Frederic R. Larson, Charles C. Avery, and Ralph E. Campbell.
1974.
Opportunities for increasing water yields and other multiple use
values on ponderosa pine forest lands. USDA Forest Service, Research
Paper RM -129, 36 p.
Ffolliott, Peter F., and J. R. Thompson.
Snowpack density on an
1977.
Arizona mixed conifer forest watershed.
in Arizona and the Southwest 7:227 -233.
Hydrology and Water Resources
1969. Snowpack density, water
Western Snow
content and runoff on a small Arizona watershed.
Ffolliott, Peter F., and David B. Thorud.
Conference 37:12 -18.
Frank, Ernest C., and Richard Lee. 1966. Potential solar irradiation on
USDA Forest Service, Research
slopes: tables for 30° to 50° latitude.
Paper RM -18, 116 p.
1967.
Gary, H. L., and G. B. Colthorp.
Snow accumulation and disappearance
by
aspect and vegetation type in the Santa Fe Basin, New Mexico.
USDA Forest Service, Research Note RM -93,
11
p.
1953.
Kittredge, Joseph.
Influences of forests on snow in the ponderosa Hilgardia 22 :1 -96.
sugar pine -fir zone of the Central Sierra Nevada.
1969.
R.
Snow accumulation and melt under various densities of
ponderosa pine in Arizona. Master's Thesis, University of Arizona,
Lejcher, T.
Tucson, Arizona, 70 p.
1974.
Watershed management in
Rich, Lowell R., and J. R. Thompson.
Arizona's mixed conifer forests: The status of our knowledge. USDA
Forest Service, Research Paper RM -130, 15 p.
1984.
Timmer, Michael J., Peter F.Ffolliott, and Malchus B. Baker, Jr.
Snowpack dynamics in aspen stands near the San Francisco Mountains,
Arizona.
Hydrology and Water Resources in Arizona and the Southwest
14:51 -55.
1948.
Work, R. A.
Snow -layer density changes.
Transaction 29:525 -545.
6
American Geophysical Union
PROBLEMS OF SIMPLIFICATION IN HYDROLOGIC MODELING
H. B. Osborn, C. L. Unkrich, and L.
Frykman
USDA -ARS Southwest Rangeland Watershed Research Center,
Rd., Tucson, AZ 85719.
2000 E. Allen
INTRODUCTION
Thunderstorm rainfall dominates small watershed runoff in the
Thunderstorm rainfall is highly variable,
southwestern United States.
both in time and space, and must be simplified for use in rainfall -runOften, models are used that are more sophisticated than is
off models.
justified by the available data. Conclusions, based on sophisticated
models with overly simplified watershed characteristics and /or rainfall
There is a tendency to claim
input, may be incorrect or misleading.
better results from more complex models without considering that the
quality of the output is dependent upon the quality of the input. Also,
significant changes in runoff characteristics may be hidden because of
There is a need for hydrologists and
oversimplification in the model.
others working in water yield and water use investigations to quantify
information on the possible errors resulting from simplification in
In
this paper, records
watershed characteristics and rainfall input.
along with a
southeastern
Arizona,
in
from a dense raingage network
were
used
to
investigate
the
kinematic cascade rainfall- runoff model,
problems of spatial representation in hydrologic modeling.
RAINFALL -RUNOFF MODELING
Many different mathematical models have been used to estimate runoff peaks and volumes from small watersheds, but few models are sensitive enough to separate the influences of rainfall variability and
In many cases, particuwatershed characteristics in estimating runoff.
larly for very small watersheds (about 100 acres and less), such sensitivity is not needed, and simple models, such as the Rational Formula,
However, to delineate hydrologic response to
may be satisfactory.
changes in rangeland condition when the input is thunderstorm rainfall
Such a model must represent both thunrequires a more complex model.
derstorm rainfall input and watershed characteristics such as infiltraFor this study, a kinetion, cover and slope, and channel geometry.
matic cascade model (KINEROS) (Kibler and Woolhiser, 1970; Rovey et al.,
1977; Lane and Woolhiser, 1977; Smith, 1981) was chosen as being versatile and sensitive to both rainfall and watershed characteristics
(Osborn, 1983).
KINEROS is a well- tested, nonlinear, deterministic, distributed1977)
Inputs are: (1) the rainfall, (2)
parameter model (Rovey et al .
,
.
7
the watershed surface geometry, roughnesss, and infiltration characteristics, and (3) the channel network, including slope, cross -sectional
area, shape, hydraulic roughness, and abstraction. For a more detailed
description of the model, see Smith (1981).
EXPERIMENTAL WATERSHED
Subwatershed 63.011 (2000 acres) is located on the upper end of the
Walnut Culch Experimental Watershed (Fig. 1).
It has a combined grass/
brush cover, and has been grazed for about 100 years.
Subwatershed
63.011 is drained by three principal channels referred to as the north,
central, and south branches (Fig. 2, 3)
Runoff from the central branch
is largely contained by two stock ponds, so the central branch was not
included in the mathematical model.
The north branch is characterized
by an incised sand - bottom channel extending to within 400 yds of the
head of the drainage.
The south branch is dominated by an incised channel on the lower half of the drainage. An active headcut is moving up
the south branch, cutting into a broad swale.
.
There are 10 weighing -type recording raingages on, or immediately
adjacent to, the 2000 -acre subwatershed (Fig. 2).
Runoff is estimated
from water -level recorders located at Walnut Gulch runoff -measuring
flume -weirs (Smith et al., 1982).
WATERSHED SIMULATION AND SIMPLIFICATION
In the first part of this study, the sensitivity of KINEROS to the
degree of topographic detail was investigated.
In the model, topography
is a faceted surface of sloping planes and channel segments. Water is
routed over planes and through channels using the kinematic approximation to the equations of unsteady, gradually- varied flow.
Therefore,
the number of elements used to define the watershed surface determines
the detail expressed by the input parameters.
For this study, watershed 63.011 was subdivided into planes and
channels representing three different levels of detail. The 3 data sets
contained large -sized planes (13 planes and 5 channel segments) mediunsized planes (20 planes and 9 channel segments (Fig. 3) ), and smallsized planes (40 planes and 18 channel segments)
A representative
plane for the median-sized model is shown in Fig. 4.
Surface geometries
were determined separately for each plane and channel reach. Obviously,
there must be considerable simplification for rolling rangeland watersheds such as Walnut Gulch.
,
.
Input to the model consisted of measurable quantities and estimated
parameters. Areas and lengths were measured directly from maps.
Slopes
were estimated by inspecting profiles drawn from topographic maps.
Roughness and infiltration parameters were treated as lumped parameters
8
Yoe
ó.
7UCSON
GILA
N,
PHOENIX
ARIZONA
2
3
4
.v MAIN CHANNELS
WATERSHED BOUNDARIES
SCALE IN MILES
3
LOCATION OF
-WALNUT GULCH
WATERSHED
//'
lC
.
.;
f
c
-
1.9.10.4 SWOON
;
.,... POND
AA<.A.
..o0oawnw.
__
`e
Figure 1. Location and map of Walnut
Gulch watershed and subwatershed 63.011.
Figure 2. Map of Walnut Gulch 63.011.
u
o
o
TOMBSTONE
CITY OF
ig
IS
1
1
25
24
9
1
12
0.4
F m1 . 0 33
ROC
SIII.O.S
510..1.0.9
A30
11.InI 0.40
RIn1O.OG
S0.09
PLANE
II
PARAMETERS
0.015
Rlnl 0.05
S
L5500
FT.
CHANNEL ISWALEI
22
21
2000 1000
IODO
Sco10 Io Fool
500
PLANE
CHANNEL SEGMENT
STOCK PONO
1000
%
19
19
CONTOUR INTERVAL - 20FT.
SP
S
i
0
îQ
Figure 4. Representation of
plane for watershed 63.011.
Figure 3. Schematic representation of watershed
63.011 for KINEROS.
26
API
27
"
W
d
1500
17I
optimized by trial and error, using actual hydrographs against simulaThese 'best fit' estitions generated by the medium representation.
mates were obtained prior to this study, and remained fixed throughout,
except for the initial soil moisture, which varied between storms.
Eight actual storm events on 63.011 were selected to compare runoff
peaks, volumes, and time to peak for the 3 different spatial representaThese events provided a wide range of rainfall inputs and out tions.
fall hydrographs.
RESULTS OF WATERSHED SIMULATION AND SIMPLIFICATION
Since the model parameters were calibrated using the version with
medium -sized planes, this version could not be used to make meaningful
However, the outfall hydrographs of the small- and large comparisons.
plane models showed consistent differences which could relate differences in total channel length to spatial distribution of the rainfall.
Differences in runoff volume and time to peak could be placed in three
categories corresponding to three general spatial conditions in the
rainfall input: (1) greater volume and shorter peak time for runoff from
to a storm center
small planes relative to large which corresponded
lying on the upper third of the watershed, (2) little difference in runoff with storms centered on the middle third of the watershed, and (3)
greater volumes with shorter peak times for large planes, with storms
If the most intense rain centered on the lower third of the watershed.
fall occurred well into the interior of the watershed, then the efficiency of channelled relative to overland flow becomes dominant, and the
If the
more detailed representation yields greater peaks and volumes.
rainfall is centered near the outlet, then the association of a greater
area with high intensities (coarser raingage -plane associations) favors
a less detailed version having larger peaks and volumes.
RAINFALL SIMULATION AND SIMPLIFICATION
The emphasis in the second part of the study was to determine the
sensitivity of runoff to rainfall simplification via simplification of
simulated events for selected durations and recurrence intervals, and
The model for medium temporal simplification of actual storm rainfall.
sized planes was used throughout this part of the study. For simplification of simulated events, maximum storm point rainfall was simulated
for 30- and 6O-min durations for 5 -, 10- and 100 -yr recurrence intervals
(Osborn and Lane, 1981), and the areal distribution and storm shape were
based on areal relationships reported by Osborn and Laursen (1973) and
Model storms were centered on the long axis of
Osborn et al. (1980)
Point rainfall amounts were distributed in the
the watershed (Fig. 5)
same way for each period within the 30- and 60-min durations, respecRainfall intensity distributions were determined for
tively (Table 1).
all 10 gages for each simulated storm (Table 2) . Then simulated storm
.
.
10
rainfall for each event was simplified by averaging over the watershed
while retaining the distribution of rainfall intensity (Table 3).
Finally, the simulated storm events were averaged both in time and space
(Table 4).
Table 1. -- Distribution of 30-min and 60-min rainfall intensities
30 -min duration
(minutes)
Factor*
.17
.13
12
9
6
3
0
.13
.17
18
15
.08
.11
.13
24
21
.01
.03
.04
30
27
*Multiply by 20 P (P is total 30-min storm rainfall) for intensities in
in/hr for each 3-min duration.
60 -min duration
(minutes)
0
Factor*
.20
*Multiply by 10 P
18
12
6
.20
.28
(P
24
.08
.14
42
36
30
.04
.02
48
.02
60
54
.01
.01
is total 60-min storm rainfall) for intensities in
in /hr for each 6-min duration.
Table 2.-- Simulated intensities (in /hr) for 5 -yr, 30-min centered storms
with simultaneous start times.
Time (min)
Raingage
0
18
15
12
9
6
3
27
24
21
30
.92
.46
.28
.41
1.62
1.25
.62
.38
.19
1.88
1.62
1.25
.62
.38
.19
2.49
1.92
.96
.58
.29
44
1.38
1.92
1.92
1.38
1.38
1.19
51
1.88
2.62
2.62
1.88
1.88
89
1.88
2.62
2.62
1.88
2.88
4.02
4.02
2.88
2.88
52
2.12
2.98
2.98
2.12
2.12
1.84
1.42
.71
.42
.21
88
2.88
4.02
4.02
2.88
2.88
2.49
1.92
.96
.58
.29
54
2.38
3.32
3.32
2.38
2.38
2.06
1.58
.79
.48
.24
56
2.38
3.32
3.32
2.38
2.38
2.06
1.58
.79
.48
.24
.38
.19
.30
.15
90
91
1.88
2.62
2.62
1.88
1.88
1.62
1.25
.62
55
1.50
2.10
2.10
1.50
1.50
1.30
1.00
.50
11
Table 3.-- Simplified, simulated averaged breakpoint rainfall (inches /hr)
for selected storms on Walnut Gulch 63.011
Storm
5-yr,
30-min
30-m n
100-yr,
30-min
Rainfall
Max.
Avg.
(in)
(in)
Time (min)
0
6
9
12
15
18
24
21
2.38 3.32 3.32 2.38 2.38 2.06 1.58
27
30
.48
.24
1.22
3.27 4.25 4.25 3.27 3.27 2.60 2.00 1.00 .65
.33
1.80
4.70 6.26 6.26 4.70 4.70 3.91 3.13 1.57
.47
1.2
.95
1.5
2.3
0
5 -yr,
3
6
12
18
24
.94
48
42
36
30
.79
54
1.5
1.2
2.40 3.36 2.40 1.60
.80
.48
.24
.24 .16 .16
10-yr,
60-min
1.9
1.5
3.16 4.11 3.16 2.05 1.03
.63
.32
.32
.16 .16
100-yr,
60-min
2.9
2.3
4.76 6.34 4.76 3.17 1.59
.95
.48
.24
.24 .24
60-min
60
Table 4.-- Simplified, simulated rainfall averaged in time and space for
selected storms on Walnut Gulch, 63.011
Rainfall intensity
Storm
(in/hr)
5-yr, 30-min
10-yr, 30-min
100-yr, 30-min
1.90
2.44
3.60
5-yr, 60-min
10-yr, 60-min
100-yr, 60-min
1.20
1.50
2.30
For temporal simplification of storms, 12 actual events were used
to investigate the effect of rainfall simplifications based on the temporal pattern of the recording raingage which received the most rainThe temporal rainfall pattern of each gage was constructed by
fall.
multiplying the intensities of the maximum gage by a constant equal
to the ratio of their total inputs (Thus, the input to each gage is a
Also,
scaled -down copy of the intensity pattern at the maximum gage.).
as a check of the model sensitivity, more information was added to the
The duration, and then the start times,
input data for three events.
were changed to coincide with the actual data at the other 9 gages. As
12
more information was incorporated into the input for the rainfall- runoff
model, the peak and volume estimates were expected to improve.
RESULTS OF SIMPLIFICATION OF SIMULATED EVENTS
In
general, simplifying break -point rainfall input by averaging
the input over the watershed and retaining the intensity distribution
However,
resulted in smaller peak discharges (Table 5, Fig. 6 and 7)
the differences were small, just 100 cfs (Table 5, Fig. 6 and 7). These
differences were insignificant for the larger events, considering the
One might add about 40
uncertainties in other simulation parameters.
cfs /mil to simplified simulated peaks to make the averages, with and
There was eswithout spatial simplification, come out about the same.
sentially no difference in runoff volumes between the break -point rainfall simulation and the simulation without spatial variability (Fig. 8
.
and 9).
Table 5 -- Effect of spatial and temporal simplification of simulated
rainfall on runoff peaks and volumes, Walnut Gulch, 63.011
Avg.
rai n
fall
Varied in time and
space
( cfs)
"wet"
"dry'
"wet"
Peak
Uniform in space,
time varied
Vol
(i n)
Peak Vol Peak
(cfs) (i n) (cfs)
"dry'
Uniform in space
and time
"wet"
"dry'
Vol Peak Vol Peak
Vol Peak Vol
(i n) (cfs) (i n) (cfs)
(i n) (cfs) (i n)
5-yr,
30-min
.95
1111
.36
135 .04
996
.36
10-yr,
30-min
1.22
1868
.60
749 .21 1751
.63
100-yr,
30-min
1.83
3773 1.16
5-yr,
60-min
1.20
1336
.48
304 .09 1207
.46
172 .07
697
.31
10-yr,
60-min
1.51
2110
.75
902 .29 1978
.74
792 .29 1248
.58
100-yr,
60-min
2.30
4190 1.45
878
.32
3 .00
611 .21 1578
.56
350 .13
52 .02
2581 .76 3618 1.16 2410 .76 3270 1.10 1898 .60
0
0
219 .09
3017 .99 4080 1.46 2895 .99 2728 1.35 1770 .72
There were significant differences in peak discharge between spatially uniform simulations and spatially /temporally uniform simulations
The differences were particularly apparent
(Table 5, Fig. 6 and 7).
with the 60-min storms, since the rainfall intensity is lower over a
For the 30-min storm, the differences ranged
longer period (Fig. 7)
from about 120 cfs for the 5 -yr event to about 340 cfs for the 100 -yr
.
13
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event, or about 12% to
10 %.
A linear correction of about 10% would
probably be safe for the 30-min storms.
Table 6.--Actual
and simulated peak discharge for selected events for
best fit simulation and simulations based on gage recording
maximum storm rainfall, Walnut Gulch 63.011
Simu1at ed Peaks
Actual
peak
Date
disch
Maximum
gage
Best
fit
cfs)
Max gage,
Max gage,
varied
Maximum gage, varied
intensity,
varied
intensity
duration,
intensity
and
and start
duration
times
(cfs)
30
5/6
10
5
18
22
22
Jul
Aug
Sep
Aug
Aug
Aug
Jun
31 Jul
1
15
27
11
Sep
Jul
Aug
Sep
66
66
67
68
71
75
77
956
319
1706
876
434
780
343
206
988
340
3400
655
77
77
81
82
82
Average
Standard deviation
Coeffient of
variation
55
91
52
90
91
938
291
1711
668
298
711
319
202
1015
316
3260
622
2006
1076
262
957
283
273
861
417
2990
468
917
890
880
886
859
833
0.97
0.97
0.95
88
44
54
90
91
56
56
878
418
1842
1653
458
499
577
651
(Best Fit) QPA = 1.03 QpS(r2
= 1.00)
(Maximum Gage) QPA = 8 + 1.03 Qp5(r2
= .94)
The differences for 60-min storms ranged from about 500 cfs (40 %)
to about 1350 cfs (33 %) for the 5 -yr and 100 -yr events, respectively. A
safe correction might be to add about 30 %, but the differences are large
enough to indicate that drastic simplification of rainfall is probably
unacceptable for sophisticated models such as KINEROS, and might lead
to serious underprediction of peak discharge with most rainfall-runoff
models.
The "wet" and "dry' antecedent conditions for the simulated events
were considered near extremes for the Walnut Gulch watershed, so the
15
differences in runoff for the same event were considerable (Table
5,
In fact, uncertainty in estimating antecedent watershed condition could mask significant differences caused by simplifications in
rainfall input.
The differences between wet and dry antecedent conditions for watershed -centered rainfall were about 1200 cfs for peak discharge and 0.4 in. for runoff.
Fig. 6 -9).
Table 7.-- Actual and simulated runoff for selected events based on best
peak fit simulation and simulations based on gage recording
maximum storm rainfall, Walnut Gulch 63.011
Simulated Runoff (inches)
Actual
Date
30 Jul
5/6 Aug
10 Sep
5 Aug
18 Aug
22 Aug
22 Jun
31 Jul
1 Sep
15 Jul
27 Aug
11 Sep
Average
runoff
(in)
.353
66
66
67
68
71
75
77
77
77
81
82
82
Maximum
gage
From
best
peak
fit
,
Max gage,
Maximum gage, varied
varied
intensity
intensity
and
duration
.756
55
91
52
.301
.114
.651
.230
.128
.746
.170
90
.258
.129
91
.151
.154
88
44
54
90
.242
.253
.077
.284
.142
.123
.046
.441
.098
.452
.125
.080
.118
.377
.152
1.024
1.016
.302
.214
.306
.317
.306
Standard
deviation
.288
.277
.288
Coeffient of
variation
,941
.874
.941
.087
.970
.306
91
56
56
.090
Max gage,
varied
intensity,
duration,
and start
times
.698
.708
.130
.134
.289
.295
(From Best Fit) QA = -.017 + 102 QS(r2 = .96)
(Maximum Gage) QA = .012 + 0.96 QS(r2 = .93)
RESULTS OF SIMPLIFICATION OF ACTUAL STORM RAINFALL
A general model
intensities in space
temporal correlation
the model as well.
of thunderstorm rainfall would simulate rainfall
and time, maintaining the appropriate spatial and
Storm movement would be included in
structure.
Such models have been developed for more general,
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frontal -type rainfall (Bras and Rodriguez -Iturbe, 1976), but would be
An alternative would be
exceedingly complex for thunderstorm rainfall.
to use a simpler depth -area model of thunderstorm rainfall such as that
presented by Osborn et al. (1980), which describes the distribution of
total storm depths over the watershed, but does not include spatial and
The relationship
temporal correlation structure or storm movement.
between rainfall amount at the storm center and storm duration could be
simulated using the joint distribution of depth and duration presented
and the intensity pattern at the center
by Woolhiser et al. (In Press)
could be simulated using the point disaggregation model of Woolhiser and
Osborn (1985). Finally, the intensity patterns at other points could be
scaled from that at the center.
,
Obviously, several simplifications are involved in this approach.
The data available from the Walnut Gulch Watershed enables us to examine
The criteria
the relative importance of some of these simplifications.
(1) good records
used to select the 12 events used for this study were:
must be available from all 10 gages and the runoff -measuring structure,
(2) only storms with peaks of 200 cfs or greater were considered (this
guaranteed that a significant portion of the watershed received runoffproducing rainfall), and (3) the two largest events during the period of
record (1966 - 1982) would be included (10 Sep 1967 and 27 Aug 1982) .
The other 10 events were chosen randomly from the storm sample.
First, runoff was simulated for all storms using the known intensiThen, runoff hydrographs were simulated
ty -time patterns at each gage.
for each storm using the scaled- intensity pattern at the maximum gage
Rainfall was assured to start
and the duration at the maximum gage.
simultaneously at all gages, so there was no storm movement.
Peaks estimated from the maximum gage were not as well correlated
with the actual peaks as were those estimated from the breakpoint data
However, there was no indicabased on all 10 gages (Table 6, Fig. 10)
tion of a meaningful bias in the estimates, which indicated that maximum
point rainfall could be a useful tool for prediction if enough was known
about the spatial and temporal distribution of rainfall around the storm
By chance, the average storm run -off was the same for the actucenter.
al storms and the storms generated from the maximum gage. However, run.
off from the maximum gage simulations were more scattered than those
based on best fit (Table 7, Fig. 11)
Three storms with well- fitted actual and simulated hydrographs were
chosen to test the model's sensitivity to storm duration and movement
In the successive simulations, more information was used
(Fig. 12 -14).
-- first, the storm durations at each gage, and second, the start times
As was hoped, the improved simulations also improved the
for each gage.
accuracy of the peak and volume estimates (Table 6 and 7,
18
Fig. 10 -17).
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CONCLUSIONS
This study suggests that for rainfall -runoff models incorporating
channel and overland flow elements, the results can be strongly biased
simply by how many elements are used in the simplification. Also, representation of the channel network is an important consideration in the
model.
In
this case, a difference in output corresponding to greater
model detail was explained solely by the greater extent of channelled
versus overland flow.
For spatial representation of rainfall, as long as itensity distribution is incorporated into the rainfall input, several simplifications
can be used effectively.
Simplifications in actual and simulated rainfall gave the best results when storms were centered on the watershed.
References Cited
Bras, R. L., and I. Ro gri guez -It urbe.
1976.
Rainfall generation:
a
nonstationary time -varying multidimensional model.
Water Resour.
Res. (12)3:450 -456.
D.
F., and D. A. Woolhiser.
1970.
The kinematic cascade as a
hydrologic model.
Colo. State Univ. Hydrology Paper 39, 38 p.
Kibler,
J., and D. A. Woolhiser.
1977.
Simplifications of watershed
J.
Hydrology,
geometry affecting simulation of surface runoff.
Lane, L.
Vol. 35, pp. 173 -190.
Osborn, H. B.
1983.
small watersheds.
Storm -cell
properties influencing runoff from
Trans. Res. Board, NAS, TRR 922, pp. 24 -32.
Osborn, H. B., and L. J. Lane.
1981.
Point -area -frequency conversions
for sunnier rainfall in southeastern Arizona.
Hydrology and Water
Resource of Arizona and the Southwest, Univ. of Arizona 11:39 -42.
Osborn, H. B., and E. M. Laursen.
1973.
Thunderstorm runoff in southeastern Arizona.
J. Hydraulics Division, ASCE 99(HY7) :1129 -1145.
Osborn, H. B., L.
J.
Lane, and V. A. Myers.
1980.
Two useful rainfall/
for southwestern thunderstorms.
Trans.
watershed relationships
ASAE 23(1) :82 -87.
Rovey, E. W., D. A. Woolhiser, and R. E. Smith.
1977.
A distributed
kinematic model of upland watersheds. Colo. State Univ., Hydr. 93.
Smith, R. E.
1981.
A kinematic model for surface mine sediment yield.
Trans. ASAE 24(6) :1508 -1519.
Smith, R. E., D. L. Chery, K. G. Renard, and W. R. Gwinn.
1982.
Supercritical flow flumes for measuring sediment -laden flow.
USDA -ARS
Tech. Bull.
No. 1655, 70 p.
Woolhiser, D. A., and H. B. Osborn.
sionless thunderstorm rainfall.
1985.
A stochastic model of dimen-
Water Resour. Res. 21(4) :511 -522.
Woolhiser, D. A., H. B. Osborn, and J. Hershenhorn.
(In Press)
Disaggregation of daily rainfall.
In:
Proc, National Resources Modeling Workshop, Pingree Park, CO, USDA -ARS, Tucson, AZ.
20
EVALUATION OF HYDROLOGIC AND HYDRAULIC PROCEDURES FOR SMALL URBAN
WATERSHEDS IN THE SOUTHWEST
L.
J.
J. Ward, and J.
Lane, T.
J.
Stone
USDA, ARS Southwest Rangeland Watershed Research Center, 2000 E. Allen
Rd., Tucson, AZ
85719; New Mexico State University, Las Cruces, NM
88003; and USDA, ARS Southwest Rangeland Watershed Research Center, 2000
E. Allen Rd., Tucson, AZ 85719.
Abstract
Hydrologic and hydraulic design procedures developed by the Pima
County Department of Transportation and Flood Control District were proposed for use by the Department of Transportation, Engineering Division
These procedures were evaluated with
of the City of Tucson, Arizona.
respect to their reasonableness of approach and in comparison with other
methods and existing data used to derive similar methodology and standThe proposed criteria/methodology were found to be consistent
ards.
with procedures used in other cities in the Southwest, and with the current state of the art in urban hydrology and hydraulic engineering pracHowever, locally derived rainfall intensity -duratices and procedures.
tion relationships were found to be superior to regionally based relationships, and minor modifications were suggested for channel design
procedures.
INTRODUCTIO N
The primary objective of the evaluation was to examine the validity
and reasonableness of the City of Tucson's hydrologic and hydraulic design procedures, methodology, and criteria. This objective required that
we consider the City of Tucson procedures in comparison with accepted
theory and standard engineering practices and in relation to accepted
regional procedures and federal standards.
During the middle to latter part of the 1970's, an effort was made
to develop improved procedures to predict peak rates of discharge from
These efforts resulted in
small watersheds in Pima County, Arizona.
publication of a hydrology manual for the Pima County Department of
Transportation and Flood Control District (Zeller, 1979), and Drainage
and Channel Design Standards for Local Drainage for Flood Plain ManageAs a result of
ment within Pima County, Arizona (Pima County, 1984).
these efforts to improve procedures for Pima County, the City of Tucson
is adopting similar standards and procedures based almost entirely on
the Pima County standards and procedures.
However,
some
of
these
procedures
21
were
questioned
by
local
consulting engineers, builders, developers, and their clients.
In
an
attempt to develop the best possible standards and methodology, the City
of Tucson sought independent evaluations of their proposed methodology
and standards for hydrologic and hydraulic designs. This paper summarizes findings from a longer report (Lane, Ward, and Stone, 1984) descri-
bing the results of an independent evaluation of the City of Tucson's
hydrologic and hydraulic design procedures and methodology.
RESULTS AND DISCUSSION
To accomplish the evaluation objectives, we performed the following
tasks and analyses:
1.
Analyses of rainfall intensity- duration relationships.
2.
Regional flood frequency analyses.
3.
4.
Detailed hydrologic analyses using a distributed hy-
drologic simulation model
Review and evaluation of hydraulic and drainage standards.
Analyses of Rainfall Intensity- Duration Relationships
Rainfall data from the Walnut Gulch Experimental Watershed (Osborn,
1983) and from the National Weather Service station at the Tucson International Airport (Reich, 1978) were used to derive intensity- duration
estimates for durations of 5 to 60 minutes, and for return periods of 2
to 100 years. These estimates were then compared with corresponding
estimates from NOAA Atlas 2 (Miller et al., 1973).
Analyses of the estimates for Walnut Gulch suggested that NOAA
Atlas 2 procedures underestimated the 100 -year 60- minute point rainfall
depths. Analyses of the estimates for the Tucson International Airport
suggested that NOAA Atlas 2 procedures underestimate the 60- minute point
rainfall depths for return periods longer than 10 years. There was no
suggestion in the data or our analyses that NOAA Atlas 2 procedures
overestimate 60- minute point rainfall depths for return periods longer
than 10 years.
Comparisons of 60- minute point rainfall depths for Tucson, Arizona
and Walnut Gulch are summarized in Table 1. Comparisons of 5- to 60-
minute point rainfall intensities are summarized in Table 2.
Based on
these data and our analyses, we concluded that the City of Tucson (1977)
point rainfall estimates for 5- to 60- minutes, and for return periods of
10 years and longer, are appropriate for use in hydrologic and hydraulic
analyses in the vicinity of the Tucson International Airport.
22
Finally, we concluded that the City of Tucson rainfall intensity duration relationships are superior to the older relationships based
on NOAA Atlas 2.
We recommended that the City of Tucson request the
National Weather Service to revise, as appropriate, the rainfall intensity- duration estimation procedures described in NOAA Atlas 2.
Table 1.-- Comparison of 60- minute point rainfall depths at Tucson, Arizona from NOAA Atlas 2, National Weather Service, Tucson Int'l
Airport (TIA) Data and from the City of Tucson (1977), with
data from Walnut Gulch (Osborn, 1983, Fig. 36) and NOM Atlas
2.
Data are from Lane et al. (1984), and rainfall depths are
in inches.
Return
Period
(years)
NOAA1
Atlas 2
Tucson
TIA
5
1.28
1.58
0.95
1.43
10
1.77
1.80
25
2.01
2.24
2.48
2.27
2.63
3.01
2
50
100
Reich (1978)
City of Tucson
NWS2
(1977)
1.06
1.52
1.96
2.38
2.65
3.12
Walnut Gulch3
Osborn (1983) NOAA Atlas 2
1.20
1.45
1.61
1.93
2.18
2.40
1.15
1.55
1.85
2.25
2.56
2.85
NOTES:
'NOAA Atlas 2 estimates for Tucson from Miller et al. (1973), as tabill ated by Zeller (1979, p. 51).
2NWS Tucson Int'l Airport estimates represent the results of an independent frequency analysis of the National Weather Service, Tucson Int'l
Airport data from 1943 -1974, using a log -normal probability distribution and the Weibull plotting position formula (m /(N+1)).
3Walnut Gulch data represent point frequency estimates from a log- normal
probability distribution and from NOAA Atlas 2.
Table. 2-- Comparison of 5- to 60- minute rainfall intensities at Tucson
Int'l Airport (TIA) from analysis of National Weather Service
data using a log -normal probability distribution with City of
Tucson (COT) estimates (Reich, 1978, City of Tucson, 1977).
Data are from Lane et al. (1984), and rainfall intensities are
in inches per hour.
Return
Period
(years)
2
5
10
25
50
100
5 min
TIA
COT
3.67
5.08
6.02
7.26
8.16
9.10
4.02
5.25
6.50
7.51
8.25
8.84
10 min
TIA
COT
2.82
4.08
4.93
6.12
6.96
7.80
3.25
4.30
5.38
6.56
7.42
8.13
20 min
TIA
COT
2.13
3.21
3.99
5.10
5.91
6.78
23
2.30
3.21
4.00
5.02
5.83
6.45
30 min
TIA
COT
TIA
COT
1.78
2.53
3.20
3.96
4.48
5.10
.95
1.43
1.80
2.27
2.63
3.01
1.06
1.52
1.96
2.38
2.65
3.12
1.65
2.54
3.16
4.00
4.68
5.38
60 min
Regional Flood Frequency Analyses
Runoff data from selected watersheds in southeastern Arizona were
analyzed (using the log - normal probability distribution) to estimate
flood peaks for return periods of 2 to 100 years. These flood peaks, in
turn, were used to establish an approximate regional relationship beThis regional relationship can
tween drainage area and peak discharge.
then be used as a benchmark to judge the reasonableness of subsequent
flood peak estimates.
The 100 -year flood peak estimates are summarized in Table 3. Notice
that the data from High School Wash represent 8 years of record, and may
result in low estimates for the 100 -year flood peak in relation to the
Analyses of the Santa Cruz River data suggest that
other watersheds.
the data are nonstationary, and annual peak discharges are increasing
The 100 -year flood peak esti1984)
(e.g., Reich, 1984; Lane et al .
mates on the Santa Cruz River at Tucson range from 30,000 cfs to over
90,000 cfs, with most estimates in excess of 50,000 cfs. Therefore, the
flood peak estimates shown in Table 3 are only approximate, and subject
to uncertainty.
.
,
Table. 3- -Flood peaks estimated from flood frequency analysis of runoff
data from selected watersheds in southeastern Arizona.
Drainage Period
Watershed
area
of
record
(sq mi)
0.9
High School Wash, Tucson, AZ'
2.75
Big (Enchanted Hills) Wash, Tucson, AZ
3.18
63.011, near Tucson, AZ
58.
63.001, near Tombstone, AZ
1220.
San Pedro River at Charleston, AZ
2220.
Santa Cruz River at Tucson, AZ2
1968
1965
1963
1956
1916
1916
100 -yr flood
peak
(cfs) (cfs /mie )
1690.
1520.
-1975
6820.
-1975
-1975
8000.
-1980 25000.
-1980 35000.
-1983 40000.
2480.
2520.
430.
30.
20.
NOTES:
1High School Wash data represent only 8 years of record.
2Santa Cruz River data are from a nonstationary series,
and 100 -year
flood peak estimates range from 30000 to over 90000 cfs.
Based on these analyses, we concluded that the historical flood
series on the Santa Cruz River, at Tucson, represented a nonstationary
flood series, and thus, we recommend additional analyses to develop
improved estimates of the 100 -year flood peak on the Santa Cruz River.
We also concluded that the City of Tucson Flood Peak Estimator (modified
Pima County Method) usually produced flood peak estimates consistent
with flood peak estimates from regional analyses (Lane et al., 1984).
24
Detailed Hydrologic Analyses
The Pima County Method (Zeller, 1979) and a simplified version of
it, called the City of Tucson Flood Peak Estimator (City of Tucson,
1982), and a distributed hydrologic model (Lane, 1982) were applied to a
number of small watersheds in the Tucson area to provide a basis of comparison and evaluation of the Pima County Method, and thus, the City of
Tucson Flood Peak Estimator.
The Pima County Method is a synthesis of the Soil Conservation Service (SCS) runoff equation, the kinematic wave equations for time of
concentration, the National Weather Service NOAA Atlas 2 and the rationAs such, it incorporates the soil -cover complexes of the
al formula.
SCS equation, the time of concentration -intensity relationship from the
kinematic equations, and thus, the peak discharge -area- intensity relaThe Pima County method was develtionship from the rational formula.
oped to reflect the hydrologic impacts of urbanization through factors
known to affect the volume of runoff and peak discharge. These factors
include the amount of impervious area, the degree of changes in flow
length, hydraulic roughness and changes in drainage patterns.
The distributed model (Lane, 1982), used for comparison and evaluapurposes with the Pima County Method, uses the SCS equation to
tion
compute runoff delivered to the stream channel network. Flow routing in
the channels is based on transmission loss equations and a double- triangle hydrograph approximation to relate runoff volume and peak discharge.
The distributed model method was derived for natural (nonurban) watersheds, and uses rainfall data distributed over the entire watershed
general, the distributed model can be used on larger waterIn
area.
sheds, and emphasizes channel processes more than the Pima County
Method.
A brief comparison of the two methods is summarized in Table 4. The
Pima County Method is a handbook method designed for repeatable applications in flood peak estimation on small urban and suburban watersheds.
The distributed model method was developed for watershed research, and
is not supported by a user's manual or handbook. Moreover, the distributed model requires more judgment and experience for applications than
Although the procedures are quite differdoes the Pima County Method.
ent, they have enough in common with respect to prediction of peak discharge, that the distributed model can be used to judge the accuracy and
applicability of the Pima County Method.
Computed values of the 100 -year flood peak, from both methods for
Notice that the estimated
six small watersheds, are shown in Table 5.
flood peaks are quite comparable from both methods, and differ by less
Also, the Carmack Wash and Finger Rock Wash
than 15% in all cases.
watersheds represent small watersheds with much of their drainage areas
Examples 1 and 2 represent "footin the Santa Catalina Mountains.
Example 6, Big
hills" watersheds in natural and suburban development.
25
(Enchanted Hills) Wash, represents a natural watershed in the Tucson
Example 7, High School Wash,
Mountains area with desert brush cover.
represents a moderately urbanized watershed with about 29% of the drainThus, these six watersheds represent
age area as impervious areas.
natural and urban areas, mountains and foothills areas, and an urbanized
watershed on the Tucson valley floor.
Table. 4-- Comparison of the Pima County Method (Zeller, 1979) and the
distributed model method (Lane, 1982).
Pima County
Item
method (PC)
ap-
pegioniof
Pima Countyl
Distributed Model
method (DM)
Seimarid rangelands
SCS Equation and
transmission losses
SCS Equation
Peak rate
Rainfall depth
and intensity.
Watershed features.
Emphasi s
Flood peaks on
natural and urbanized waterHandsheds.
book approach.
Runoff volume and
peak rates on natural watersheds.
Transmission losses.
Research model.
Handbook,
Zeller (1979).
Journal article,
Lane (1982)
tion /application
Both emphasize
thunderstorm
rainfall.
Runoff volume
Documenta-
Comments
Rainfall depth and
an assumed hydro -
graph shape. Watershed features.
DM incorporates
transmission
losses.
PC has variable
Tc,2 and DM has
constant Tc.
DM designed to
allow distributed
raingage data as
input and simulate partial area
runoff.
PC method more
repeatable, and
requires less
judgment.
1The Pima County Method was developed specifically for Pima County, but
is applicable to much wider geographical areas where the rainfall -runoff model (SCS Equation) is appropriate.
Both methods compute Tc as a function of
2Tc = time of concentration.
watershed characteristics, and the Pima County Method computes Tc as a
a function of rainfall intensity as well.
The correspondence of 100 -year flood peak estimates from the two
methods suggest that the Pima County Method is in close agreement
with the distributed model method, and in approximate agreement with
available measured data (Table 3 herein, and Appendix D in Lane et al.,
26
1984).
From these and similar analyses, we concluded that the Pima
County Method, the City of Tucson Flood Peak Estimator, and the distributed model all produce similar flood peak estimates, given the same
Morebasic input data on soils, topography, and rainfall intensity.
over, all three methods produced flood peak estimates in agreement with
observed flood data on small watersheds (less than 10 mil) in southeastern Arizona.
Table. 5-- Comparison of 100 -year flood peak estimates for selected small
watersheds in the Tucson area using the Pima County Method
(PC) and the distributed model (DM) method with rainfall data
Al 1 watersheds are
from NOAA Atlas 2 (Miller et al. , 1973)
in Pima County, and are in or near Tucson, Arizona.
.
Watershed
location
Drainage
area
100 -year flood peak
Method with
(PC)
(DM)
1 argest peak
(sq mi)
(cfs)
( %)
Carmack Wash at Hardy Rd
3.0
5570.
5130.
PC( +7.9)
Finger Rock at Skyline Dr
4.4
5550.
6230.
DM( -12.3)
Zeller (1979) Example 1,
Natural foothills
1.8
1824.
1940.
DM( -6.4)
1.8
2404.
2290.
PC( +4.7)
2.75
4064.
4340.
DM( -6.8)
0.9
1777.
1520.
PC ( +14.5 )
Zeller (1979) Example 2,
Foothills CR -1
Zeller (1979), Example 6,
Big (Enchanted Hills) Wash
near Mission Road
Zeller (1979), Example 7,
High School Wash at Cherry
Avenue
Values in parentheses in the last column are a percent difference
term calculated as ((QPC- QDM) /(QPC)) times 100 where QPC is the peak
discharge using the PC method, and QDM is the peak discharge using the
All differences in computed peak discharge are less than
DM method.
NOTE:
15 %.
Finally, Lane et al. (1984) examined procedures used to estimate
peak discharge in a number of other southwestern cities. The Pima County
Method was found to be comparable to, or better than, methods used in
Albuquerque, NM; Denver, CO; El Paso, TX; Los Angeles, CA; Phoenix, AZ;
and Ventura County, CA in most respects except rainfall frequency data.
We recommended that City of Tucson rainfall intensity- duration data be
With this modification, the Pima
used in place of NOAA Atlas 2 data.
County Method and (under appropriate conditions, including watershed
27
size limits, i.e., 2.0 sq mi or less)
Estimator will be considerably enhanced.
the City of Tucson Flood Peak
Review of Hydraulic and Drainage Standards
The City of Tucson proposes to adopt drainage and channel design
standards developed by Pima County (Pima County, 1984)
The Pima County
standards adequately cover most of the major design problems with the
exception of storm sewer design and detailed roadway drainage.
The following paragraphs review specific factors in the Pima County guidelines.
.
Freeboard.
The freeboard equation presented is appropirate in
terms of mean flow velocity.
However, we suggested modifying the freeboard equation to produce 2.0 ft, rather than 1.0 ft, as the minimum
freeboard in design of conveyance channels.
Setbacks. The setbacks are 50 to 300 feet, depending upon the size
of the channel.
We concluded that these setback limits appear to be
reasonable, but in view of the 1983 floods in Pima County, perhaps could
be modified if analysis of channel erosion -meandering patterns suggest
larger setbacks are appropriate.
We suggested this as an appropriate
area for further study using state -of- the -art channel dynamics- morphology models.
Encroachment.
The 0.1 ft change in water surface elevation is conservative (in that modifications should not cause a larger change)
However, it is not a practical limit in that water surfaces in general
cannot be determined with this precision.
Therefore, we would suggest
that the standards require no change in water surface elevation as a
result of encroachment.
.
Criteria and standards for right -of -ways, channel side slopes, bend
radius, transitions, and confluences appear reasonable if the upper limit of 30 degrees for entrance transition angle is lowered to about 11
degrees (1:5 flare)
Design criteria for channel stabilization should
be based on the 100 -year flow depth, except that the equilibrium slope
may be based on the 2 -year flow.
Finally, we recommended a detailed
review and appropriate modification of the roadway drainage guidelines.
Additional details are given in Appendix F of Lane et al. (1984).
.
SUMMARY AND COMMENTS
Summary
The Pima County Method and the City of Tucson Flood Peak Estimator
appear to be reasonable and state -of- the -art procedures with respect
to current engineering practices used in several cities in the Southwest.
The Pima County hydraulic and drainage procedures are also reasonable with respect to scientific, regional, and acceptable engineering
28
practices criteria.
However, we recommended using the City of Tucson
rainfall intensity- duration relationship, as it is based upon a longer
period of record, and is superior to the older procedures described in
NOAA At l as 2.
We also recommended that the City of Tucson adopt the
Pima County Drainage and Channel Design Standards for Local Drainage
after they are reviewed and modifications (as a result of our review and
the proposed review) completed.
Comments
Pima County, through its development of the Hydrology Manual for
Engineering Design and Flood Plain Management within Pima County, Arizona (Zeller, 1979) and the Pima County Drainage and Channel Design Standards for Local Drainage (Pima County, 1984) has improved hydrologic
and hydraulic methodology applicable in, and available to, the City of
Tucson.
The City of Tucson has adopted, or is in the process of adopting,
slightly modified versons of the Pima County hydrologic and hydraulic
methodology and standards. However, as a result of questions raised by
consulting engineers, builders, developers, and their clients, the City
of Tucson sought an independent evaluation of their existing and proposed standards.
The floods of October, 1983, subsequent flood frequency
analyses suggesting increasing peak discharges on the Santa Cruz River,
and the independent evaluations reported by Lane et al. (1984) and summarized herein, suggest that the present and proposed standards are
acceptable, but need to be strengthened in certain areas. Specifically,
the City of Tucson rainfall intensity- duration relationship (resulting
in higher intensities and larger flood peaks) is superior to the relationship described in NOAA Atlas 2.
Recent flood peaks on the Santa
Cruz River are increasing, and estimates of the 100 -year flood are being
revised upward.
Therefore, we conclude that the City of Tucson and Pima County have
made significant progress in standardizing procedures, but should continue to develop improved methodology and standards for hydrologic and
hydraulic designs.
ACKNOWLEDGEMENTS
In making the evaluations, the authors recognized a dual responsibility to protect the public with regard to loss of life or injury and
property damage, while avoiding unreasonable criteria which penalize
individual property owners and the public through unnecessary exclusions
and higher costs.
Our analyses and results are dependent upon data collected by public agencies and their employees.
These include the federal agencies through the National Weather Service, the U.S. Geological
Survey, and the Agricultural Research Service.
Pima County, through its
29
development of the Hydrology Manual
and Drainage and Channel
Standards, has improved hydrologic methodology and hydraulic
standards applicable on small watersheds in the Southwest.
Design
design
REFERENCES CITED
Dept.
Preciptation characteristics at Tucson.
1977.
City of Tucson.
of Transportation, Engineering Div. Rainfall Intensity Data, Fig.
2 -1, and Table 2 -1, Oct., 1977.
Flood peak estimator.
City of Tucson.
1982.
Engineering Div., Apr., 1982, 4 p.
Dept. of Transportation,
Distributed model for small semiarid watersheds.
Lane, L. J.
1982.
Hyd. Div., Proc. ASCE 108(HY10) :1114 -1131.
Lane, L.
J., T.
J. Ward, and J.
J.
Stone.
1984.
J.
Evaluation of hydrolo-
gic methodology and hydraulic standards for the City of Tucson,
Independent consultants' report prepared for the City of
Arizona.
Tucson, City Engineers Office under P.O. NO. 91990, Aug. 7, 1984,
11 p. plus 6 appendices consisting of 57 p.
1973.
PrecipitationF., R. J. Frederick, and R. J. Tracey.
frequency atlas of the western United States, VIII: AZ, NOAA Atlas
2, National Weather Service, NOAA, Silver Spring, MD.
Miller, J.
Precipitation characteristics affecting hydrologic
B.
1983.
USDA -ARS, Agricultural Reresponse of Southwestern rangelands.
Osborn, H.
views and Manuals, ARM -W -34, Oakland, CA.
Drainage and channel design standards for local
Pima County.
1984.
drainage for flood pl ain management within Pima County, Arizona.
Pima County Dept. of Transportation and Flood Control District,
Adopted by the Board of Supervisors, May 15,
Tucson, Arizona.
1984, effective June 1, 1984.
Rainfall intensity- duration -frequency curves develReich, B. M.
1978.
oped from (not by) computer output. Transportation Research Record
685, Nat'l Acad. Sci., Washington, DC, p 35 -43.
Recent changes in a flood series. Paper presented
Reich, B. M.
1984.
1984 Meetings of the Arizona -Nevada Acad. Sci.,
at the April,
Tucson, AZ, 8 p.
Hydrology manual for engineering design and flood
Zeller, M. E.
1979.
Pima County Dept. of
plain management within Pima County, Arizona.
Transportation and Flood Control District, Tucson, Arizona, 137 p.
30
STORM RUNOFF AND SEDIMENT PRODUCTION
AFTER WILDFIRE IN CHAPARRAL
Alden R. Hibbert
Research Hydrologist, USDA Forest Service
Rocky Mountain Forest and Range Experiment Station
Forestry Sciences Lab., ASU Campus
Tempe, Arizona 85287
Abstract
Stormflow and sediment production increased greatly after a
wildfire on three small cha3parrlal watersheds in .entral Arizona.
Peaks frequently exceeded 5 m s
kml
(450 cfg mi ) when 15- minute
rainfall intensity exceeded 50 mm hr
(2 in hr ) on catchments that,
before burning, responded little to intense rainfall.
Source water
for the flashy spates and heavy erosion was surface runoff
on the
severely burned, unprotected, water -repellent soils.
For a few years
after the fire, intense summer rains produced a disproportionate
amount of the runoff and sediment.
Early postfire recovery was rapid;
severe flooding and erosion were over in 3 years, and within 5 to 10
years stormflows and peaks declined to near prefire levels.
Postfire
conversion to grass on one watershed did not appreciably -change the
rate of recovery.
Introduction
For many years it has been recognized
that floods
follow
wildfires in chaparral.
Perceived causes of the often -dramatic
changes in surface runoff and erosion after
fire are
loss
of
protective vegetation and surface organic matter, and the heat -induced
formation of water repellency a few centimeters below the soil surface
(Rice, 1974; DeBano, 1981).
Experience worldwide in chaparral and
related ecosystems suggests that the fire effect is largely gone
within 5 to 10 years (Rowe et al., 1954; Brown, 1972).
This paper
describes postfire runoff from 3 small chaparral catchments in central
Arizona where, unlike most Mediterranean -type climates, intense summer
rains make up about one -fourth of the annual precipitation.
31
Study Area and Methods
A severe wildfire in June 1959 burned the dense chaparralcover on
three small experimental watersheds near Lake Roosevelt in central
Arizona.
The watersheds had been instrumented with rain gages and
streamflow gages 3 years earlier by the USDA Forest Service in
cooperation with the Salt River Valley Water Users Association and the
Arizona Game and Fish Department to study the effects of chaparral
Sediment
conversion on water yield, erosion, and game populations.
catchments were hastily bulldozed downstream from the streamflow gages
in anticipation of heavy sediment production from summer storms.
l
Three
Bar
o+
Phoenix
e
',zoo
N
THREE BAR WATERSHEDS
IMF N
FEET
0
1000
1. --The Three Bar experimental watersheds are located near Lake
Figure
Hatching
100 km northeast of Phoenix, Arizona.
Roosevelt about
denotes areas treated by 1965. The remainder of Watershed B was
treated in 1972.
32
The Three Bar watersheds (Figure 1) receive from 620 to 750 mm
mean annual precipitation depending on elevation, which varies between
1,000 and 1,500 m.
Seasonal distribution of precipitation is 74%
winter (October -April) and 26% summer (May -September).
Slopes are
steep, exceeding 60% in some places.
Soils are poorly developed from
the deeply weathered (6 -12 m) granitz regolith.
Detention storage in
the regolith is more than 500 mm (Ingebo, 1969).
Dominante shrubs are
shrub live oak (Quercus turbinella) and birchleaf mountainmahogany
(Cercocarpus betuloides).
Shrub crown cover averaged 70 -75% before
the wildfire top -kille& all shrubs and destroyed the surface organic
matter on all watersheds.
The shrubs, which sprout readily from root crowns, regained 44%
of their prefire crown cover in 4 years, and more than 90% by the end
of the study period in 1979.
Watershed C (38.6 ha) was converted to
grass by killing shrubs with herbicides over a 6 -year period beginning
the first year after the fire (Hibbert et al., 1974).
Watershed B
(18.8 ha) was allowed to recover naturally for 6 years until 1965,
when shrubs on northeast -facing slopes (40% of the watershed) were
controlled with a one -time application of soil -applied herbicide. The
rest of the watershed was treated the same way in 1972. Watershed D
(33
ha), which functioned as a control, was allowed to recover
uninterrupted. There has been no livestock grazing on the watersheds
since the early 1940's.
Streamflow was measured at 90° V -notch sharp- crested weirs in
concrete dams anchored on bedrock.
Stormflows were separated by
computer from the3 strgamflo2w hydlrograph by projecting a straight line
km
(slope 0.00055 m
s
hr
)
from the rain -induced rise in the
The volume of flow
hydrograph to the recession limb (Figure 2).
(depth in mm over the watershed area) above the separation line is
Peak flow
defined As Atormflow, that below the line as base flow.
km ) were obtained by subtracting the base flow rate at
rates (m s
the beginning of hydrograph rise from the maximum flow rate during the
stormflow period.
This simple procedure separates stormflow "pulses"
from nonstorm flows in a uniform manner;
it works equally well on
large or small storms.
Rainfall was measured at 9 rain gages distributed over the
Rainfall intensity was
(numbered circles in Figure 1).
sampled by a recording rain gage centrally located near the stream
Only storms of 20 mm or more of rainfall were
gage on Watershed C.
included in the analysis; some storms on dry soil failed to produce
Summer and winter storms were evaluated separately because of
runoff.
seasonal differences in stormflow response, both before and after the
wildfire.
Summer storms averaged 35 mm, and had 15, and 60- minute
rainfall intensities that averaged 49 and 24 mm hr
respectively.
Winter storms were larger (55 mm) but less intense (18 and 11 mm
watersheds
,
hr
).
33
0.5
Figure 2.-- Stormflows are
separated from base
flows by a straight line procedure that
works well for both
Summer
winter and summer
storms.
y
Winter
I
1 Stormflow
3'km2hr 1
-
0.00055 m
-
4
3
2
1
Days
Response to Prefire Storms
Summer Storms
Summer thunderstorms were noted for lack of runoff before the
wildfire (Table 1).
Of 5 storms in 3 years that were 20 mm or larger,
only one (8/6/58) (30 mm of rain in 35 minutes) caused any flow, and
Summer stormflow response to storms 320 mm averaged by 3 -year periods, except that first
Table 1.
3 postfire years are individual summer averages.
Years
After
Fire
PERIOD
STORM RAINFALL
No. of No. of
Years Storms Amt.
Intensity
I15
mm
I60
STORMFLOWS AND PEAKS
Watershed D
Peak
Amount
mm hr
Depth
mm
28
0.05
7.
Watershed C
of
m3 921
Depth
Amount
Peak
Amount
km
rain
Watershed B
7. of
rain
m3 s1
km
-2
Depth
mm
Peak
X of
rain
m3 s_2
km
PREFIRE
-
3
5
28
78
0.008
0.1
0
0
0
0
0
0
POSTFIRE
1
1
3
41
74
28
10.40
2
1
3
26
43
21
1.03
3
1
2
32
70
28
7.61
4-6
3
7
37
56
26
.76
7-9
3
4
45
59
30
.30
10-12
3
3
28
63
31
.07
13-15
3
8
40
48
26
.23
16-18
3
4
28
34
17
.05
19-20
2
5
28
36
15
.02
48.0
4.500
9.70
25.0
6.200
20.00
3.5
.520
2.92
11.4
1.600
1.43
5.4
.860
19.4
3.170
2.28
7.0
1.980
3.68
11.2
1.360
2.0
.264
.81
2.2
.178
.81
2.5
.492
.6
.101
.36
.8
.081
.55
1.3
.217
.2
.027
.12
.4
.045
.02
.1
.024
.5
.063
.40
1.0
.086
.03
.1
.032
.2
.017
.08
.3
.024
.01
<.1
.002
<.1
.010
.09
.3
.021
.01
<.1
.001
29.0
12.600
* Storm rainfall amount (depth) is weighted average of 4 rain gages on Watershed C; intensity is
from weighing type recorder at stream gaging site ou Watershed C.
34
The maximum 15- minute intensity of 112 mm
they only on Watershed D.
was the highest ever recorded on the Three Bar watersheds.
Perhaps more important to the generation of runoff, however, was the
occurrence 2 days earlier (8/4/58) of 44 mm of rain in 63 minutes.
This first rain set the stage for runoff by prewetting the shallow
the runoff was
the watershed where
soil in the lower part of
Even then, the volume of stcrmf]low wats only 0.2 mm, (< 1%
generated.
Both volume and
km
s
of the rain), and the peak only 0.04 m
peak rate of runoff per unit area would be higher if prorated to the
portion of the watershed that actually produced the runoff. However,
since these areas cannot be known exactly, volumes and rates of flow
are reported as if they were generated uniformly over the watershed.
hr
.
Winter Storms
Virtually all streamflow (storm flow and base flow) in the
prefire period was derived from winter (October- March) precipitation
Except for Watershed B, which yielded no water the entire
(Table 2).
3 years, some streamflow occurred each winter, with flow continuing
into spring or summer, depending on the amount of winter recharge.
Streamflow was continuous at both D and C stream gages for _151 days in
the first water year (July 1956 -June 1957) as a result of 380 mm of
Second -year flow durations were similar, but in
rain in January 1957.
Tabla 2. Winter stormflow response to storms 120 mm averaged by. 3 -year periods except that first
3 postfire years are individual winter averages.
*
Years
After
Fire
PERIOD
No. of
Years
STORMFLOWS AND PEAKS
STORM RAINFALL
No. of
Storms
Amt.
115
160
Depth
mm
mm hr
Peak
Amount
mm
Z of
rain
Watershed B
Watershed C
Watershed D
Intensity
Amount
s1
-2
Depth
mm
% of
rain
m3 s21
km
0.035
0.76
1.8
0.035
0
in
km
Peak
Amount
Peak
Depth
mm
Z of
rain
m3 s-1
km
PREFIRE
0
0
3
21
41
16
9
0.75
1.6
1
1
5
97
27
16
22.50
22.1
2.180
22.60
23.0
1.970
10.10
11.2
1.190
2
1
4
42
28
17
.94
2.0
.235
1.41
3.4
.260
.90
2.6
.315
1
6
57
10
8
1.98
3.1
.084
3.01
5.2
.077
.32
.6
.023
20
46
15
10
1.09
2.1
.042
2.00
4.4
.060
.07
.2
.026
3.5
.072
7.61
12.9
.138
1.26
2.4
.056
POSTFIRE
3
4-6
3
7-9
3
17
59
17
11
2.11
10-12
3
15
40
15
8
.37
.8
.030
1.03
2.6
.042
.01
<.1
.001
13-15
3
21
54
23
12
1.64
2.8
.070
4.28
7.9
.117
.03
.1
.005
16-18
3
15
51
18
11
.14
.3
.055
2.64
5.2
.081
.08
.2
.006
20
78
17
10
5.34
6.4
.101
19.78
25.4
.218
2.32
3.3
.038
19-20
2
* Storm rainfall amount (depth) is weighted average of 4 rain gages on Watershed C; intensity is
from weighing type recorder at stream gaging site on Watershed C.
35
the third water year, a dry one ending the month of the wildfire,
streamflow persisted for only 6 days in Watershed C and 26 days in
Watershed D.
The failure of Watershed B to yield water in the prefire years is
less
attributed primarily to a more deeply weathered regolith,
precipitation, and a warmer climate (lower elevation) relative to the
other watersheds. Precipitation on Watershed B averaged 10% less than
Chaparral shrub roots, which penetrate
on C and 227 less than on D.
these soils 7 m or more (Davis and Pase, 1977), can withdraw all or
most of the winter recharge during the hot, dry months that follow.
Because rains normally soak into these soils without running off the
surface, there can be little yield until soil water deficits are
satisfied.
While it is possible that some deep seepage or underflow may have
escaped the catchment undetected, the plausible cause for no yield on
B
was insufficient precipitation to overcome the large deficits
The 23 -year (1957 -1979) mean
created each year by evapotranspiration.
Only the second prefire year was near
.precipitation on B was 609 mm.
average; first -year precipitation was 89% of the mean, and the third
This conclusion is supported by the postfire water
year was only 60%.
Streamflow was intermittent for 6
yield behavior on Watershed B.
years (1960- 1965), flowing only during wet periods and briefly from
However, flow became continuous after treatment of
summer storms.
Obviously, if there was any
only 40% of the catchment in 1965.
leakage from Watershed B it was less than the treatment -induced
reduction in evapotranspiration.
Response to Postfire Storms
Summer Storms
In the first summer after the wildfire, nearly -every storm, large
Stormflows repeatedly overflowed the weir
or small, caused runoff.
dams on all watersheds the first summer and filled the weir ponds with
None
sediment, making reliable measurement of streamflow difficult.
of the three storms in the first postfire summer that exceeded 20 mm
(average values are shown in Table 1) was as intense as the two August
Nevertheless, each of the 3 storms,
storms of the previous summer.
and 7 smaller ones between 6 and 19 mm (not included in Table 1),
caused runoff on one or more of the watersheds in thi first summer.
and
km
High flow marks indicated peaks were as high as 23 m s
stormflow volumes as much as 50% or more of storm rainfall.
,
Stormflows and peaks were much reduced in the second summer
response
intense storms, although the
less
because of smaller,
remained above that from higher intensity storms before the wildfire.
and
Rainfall intensities again were high in the third summer,
stormflows and peaks were midway between those of the first and second
36
o
z
W
2
u.
O
100
- -- -- -- - - --- MEAN
z
W
U
¢
W
a
./11
200
,"
N
0;
to
10
i
..
ï
o
LL.
2W
2a
O
5
.5
ry
î
W
a.
0
PRE-
1
FIRE
2
3
4 -6
7-9 10-12 13-15 16-1819-20
0
YEARS AFTER WILDFIRE
Figure 3.-- Summer stormflow response on Watershed D,
the wildfire. Data are from Table 1.
before and after
summers.
In the fourth and subsequent summers, stormflow volumes and
peaks dropped off markedly, despite the occurrence of several large,
intense storms.
The tendency for fire effects to decline with time is apparent
for all watersheds.
Table 1 shows the average response each summer
for the first 3 postfire years and for 3 -year periods thereafter.
Watershed D results are plotted as bar graphs in Figure 3 to better
illustrate the relationships between storm size, intensity, and time
after the wildfire.
By displaying the amount and intensity of storm
rainfall as percent of long -term means, their relative magnitudes are
more easily related to runoff behavior throughout the study period.
Within 2Q years, stormflow response dropped to a level similar to that
before the fire.
Winter Storms
Rains in the first postfire summer did not recharge the soil
mantle, even though the July- September rainfall (244 mm on C) was 50%
37
above average.
Most of the summer moisture either evaporated or ran
off the surface. Therefore, channels were dry at the end of September
except for a small seep in the lower channel on Watershed D. In the
first postfire winter (October 1959 -March 1960) there were 5 storms
larger than 20 mm and several smaller ones for a total of 636 mm,
about one -third wetter than average.
Two of the storms were unusually
large and intense; as such they require special consideration in the
evaluation of the effects of the fire.
There was no response from the first winter storm on
10/1/59
(28 mm of rain at near -average intensity) except for a small flow
The next event was an
(0.03 mm or 0.1% of rain) on Watershed D.
unusually strong frontal system on 10/29/59 that produced 176 mm of
rain in 26 hours.
Thg maximum 15- and 60- minute rainfall intensities
were 42 and 24 mm hr
more than double the average rates for winter
storms.
03ertopping flows occurred on all 33watershecs. Peaks ranged
on Watershed D,
from 1.2 m s
km
on Watershed B to 7.9 m s
km
somewhat lower than from storms earlier in the summer.
Stormflow
volumes, on the other hand, were several times larger (13 to 42% of
Base flow was continuous after
rainfall) than from summer storms.
this storm on Watersheds D and C, but not on B, where flow stopped 3
days after the storm, and became continuous only after an additional
200 mm of rain fell over the next 2 months.
,
During
contributed
watersheds.
November
December,
small
storms
and
early
several
mm of rain, which was 99% absorbed by all the
Another major storm on 12/24/59 dropped 160 mm of rain in
90
Rainfall intensities were even higher than in the big
hours.
October storm, although resulting stormflow volumes and peaks were
The final storm of the first postfire
slightly lower on the average.
At 93 mm, the storm
winter occurred 18 days later in January 1960.
was well above average, although rainfall intensities were less than
35
half the ave3ragel
low (0.1 in
s
intensity
rain.
Peak flow rates from the storm were relatively
km
or less), as might be expected from the low
were
volumes,
hand,
on
the
other
Stormflow
substantial at 7 -9% of rainfall, except on Watershed B, which absorbed
more than 997 of this low- intensity storm.
Stormflow response was much lower, on the average, in the second
than in the first winter, even though average intensity of storms in
What is not apparent
the second winter was slightly higher (Table 2).
in the averaged values in Table 2, however, is that even the most
intense of the storms in the second winter was only 77% as intense and
40% as large as the 2 large storms in the first winter.
These 2
storms completely dominated the first winter runoff by contributing
93%
of
70%
of
storm runoff.
The
storm rainfall and causing
relationships between storm size, intensity, and time after the
wildfire are displayed for Watershed D in Figure 4.
The dashed lines
in the bar graphs of the first postfire year show the portions
contributed by the 2 large storms. Without these large storms, first
year stormflows were much closer to expected values for the size of
38
0
z_
cc
z
z_
- - MEAN
î
0
1
PREFIRE
4 -6
7 -9 10 -12 13 -15 16 -18 19 -20
YEARS AFTER WILDFIRE
Figure 4. -- Winter stormflow response on Watershed D, before and after
the wildfire. Data are from Table 2.
Dashed portions of the bar
graphs for the first postfire year were
contributed
by 2
large
storms.
storm, even though they remained substantially higher than the prefire
Peaks, on the other hand, averaged about the same as before the
mean.
fire.
Despite the variation in winter stormflows and peaks, caused
largely by storm size and wetness of season, their magnitude declined
gradually with time after wildfire (Figure 4).
However, this trend
was much less pronounced for winter storms than for summer storms
(Figure 3).
Sediment
Prior to the wildfire, measurable bedload sediment occurred only
once when rains tota.ing -?12 mm in 4 days in January 1957 produced the
equivalent of 7.7 m km
on Watershed C only. At the beginning of
the study, sediment was caught and measured in the weir ponding
basins.
After the wildfire, basins were scooped by bulldozers from
the channels below the weirs to catch the overflow. The small amount
39
Figure 5.-- Cumulative plots
of summer (S) and winter
and
(W) storm rainfall,
STORM RAINFALL
stormflow and sediment
production on Watersheds
B, C, and D combined for
years before and 7
3
years
after the fire.
STORMFLOW
2
3 .,
ONO
0
J
a
J
SEDIMENT
SW
SW
-3
-2
SWI SW
-1
1
I
SW
2
SWSW
3
4
SW
SW
SW
5
8
7
YEARS AFTER WILDFIRE
PREFIRE
of fine silts and clays that failed to settle as the stormflows passed
through the basins could not be included in the sediment production
figures.
Sediment production in the first 3 years after the wildfire
contrasts sharply with yields before and after this period (Figure 5).
While some of the accelerated production was eroded from the channels
where sediments had been accumulating for years from soil creep, dry
ravel, and occasionally overland flow, most of it came directly off
The
the slopes as evidenced by severe rilling in the catchments.
total yield3for_phe first 3 postfire years on all watersheds combined
This total is more than 25 times the total measured
years shown in Figure 5, including the 4 years
following this period and the 3 years before the fire (Hibbert et al.,
was 7,580 in km
in the other
.
7
As with stormflows, most of the sediment was produced in the
first postfire year, slightly more than half of it from a few summer
Also, sediment yields declined rapidly after the first year,
storms.
commensurate with the decline in stormflows, particularly those in
1974).
summer.
Discussion and Conclusions
The abrupt increases in stormflows and peaks after wildfire in
small chaparral watersheds is consistent with experience in
California (Hoyt and Troxell, 1934; Rowe et al., 1954; Sinclair and
Similar stormflow behavior was also reported by
Hamilton, 1955).
Brown (1972) after a brush fire in the Snowy Mountains region of New
The abrupt change in storm hydrology was
South Wales, Australia.
attributed by these investigators to an increase in surface runoff as
In this study, the flashy, highly peaked flows
a result of fire.
immediately after the fire compared to almost no runoff from intense
summer rains before the fire, plus the observational evidence of
wildfire
rilling and hillslope erosion, led to the same conclusion:
these
40
altered watershed conditions which caused large,
though temporary,
increases in surface runoff and erosion.
Failure of rainfall to infiltrate the burned soils was attributed
to the loss of protective cover and to fire -induced water repellency.
Scholl (1975) found that unburned soils on the experimental watersheds
showed some water repellency, but that fire greatly increased it. The
time required for these soils to lose their water repellency is not
known, but after 3 years, the watersheds again consistently absorbed
over 97% of intense summer storms with the exception of one intense
Likewise after 3 years, summer peaks dropped to
storm on wet soil.
less than 10% of immediate postfire highs.
An important finding was that seasonal stormflow response shifted
temporarily from a prefire pattern in which almost all stormflows
occurred in winter, to a postfire pattern in which summer storms
After a few years, the pattern
contributed heavily to surface runoff.
to winter- dominated responses (Figures 3 and 4).
gradually returned
Postfire seasonal sediment production was correlated with this shift
Three intense August rains in the first
in stormflow responses.
summer produced 53% of the massive first -year sediment yield, most of
which came from active hillslope erosion. Annual sediment production
dropped off rapidly after the first year, but the proportion from
summer storms actually increased to 88% by the fifth year before
declining to almost none in the seventh and later years. Very little
The
surface runoff or rilling were observed after the sixth summer.
time
was
relatively small amount of sediments produced after this
eroded from channel deposits by heavy subsurface stormflows in wet
winters.
The postfire control of sprouting brush and the establishment of
grass on Watershed C reduced evapotranspiration and caused streamflow
(presumably both base flows and stormflows) to remain higher than if
the brush had been allowed to recover (Hibbert, 1971). Brush control
on Watershed B, starting 6 years after the.fire, also increased both
Any
types of flows, although not as much as on Watershed C.
by
the
deleterious impact of higher stormflows appeared to be offset
stabilizing effect of herbaceous growth along stream channels as a
However, the
result of perennial flow brought on by brush conversion.
stormflow
volumes and
rates of decline in sediment production and
peaks did not appear to be enhanced by the conversion of brush to
Very possibly, grass establishment was too slow
to be more effective than shrub regrowth in retarding surface runoff
fire -induced water
natural breakdown of the
The
erosion.
and
repellency appears to be a key factor in postfire runoff abatement.
If breakdown of the repellent layer is largely time dependent, as it
is believed to be (L. F. DeBano, personal communication), then the
type of replacement cover (i.e., brush or grass) may not be very
important. This problem needs more study.
grass on Watershed C.
Implications to management are that fire effects in chaparral,
though locally disruptive, do not last long, and that establishment of
41
grass after fire may not speed the recovery process. Conversion of
brush to grass is a management option that can be used at any growth
stage to reduce the risk of large, severe wildfires.
Although
conversion also increases stormflows as well as base flows, it does
not cause large amounts of erosive surface runoff, as does wildfire.
References Cited
Brown, J. A. H.
1972.
Hydrologic effects of a brushfire in a
catchment in southern New South Wales.
J. Hydrol. 15:77 -96.
Davis, E. A. and P. Pase.
1977.
Root system of shrub live oak:
Implications for water yield in Arizona chaparral. J. Soil
Water Conserv.
DeBano, L. F.
U.S.
For.
1981.
Water repellent soils: A state -of- the -art.
Serv.
Gen.
Tech.
Rep.
21 p. Pacific.
PSW -46,
Southwest For. and Range Exp. Stn., Berkeley, Calif.
Hibbert, A.
R.
1971.
chaparral to grass.
Increases in stormflow after converting
Water Resour. Res. 7:71 -80.
Hibbert, A. R., E. A. Davis and D. G. Scholl.
1974.
Chaparral
conversion potential in Arizona.
Part I:
Water yield
U.S. For. Serv. Res.
response and effect on other resources.
Pap. RM -126, 36 p.
Rocky Mt. For. and Range Exp. Stn., Fort
Collins, Colo.
Hoyt, W. G.
and H. C. Troxell.
1934.
Forests and stream flow.
Trans. Amer. Soc. Civil Eng., Paper No. 1858, 111 p.
Ingebo, P. A.
1969.
Effect of heavy late -fall precipitation on
U.S. For. Serv. Res. Note
runoff from a_chaparral watershed.
RM -132, 2 p. Rocky Mt. For. -and Range Exp. Stn., Fort Collins,
Colo.
Rice, R. R.
1974.
The hydrology of chaparral watersheds.
In:
Proceedings of the Symposium on Living with the Chaparral [March
30 -31, 1973; Riverside, Calif.].
Sierra Club. pp. 27 -33.
1954.
Hydrological
Rowe, P. B., C. M. Countryman and H. C. Storey.
analysis used to determine effects of fire on peak discharge and
erosion rates in southern California watersheds. U. S. For. Serv.
Calif. For. and Range Exp. Stn., Berkeley, Calif.
Scholl, D. G.
1975.
Soil wettability and fire in Arizona chaparral.
Soil Sci. Soc. Amer. Proc. 39:356 -361.
Sinclair, J. D. and E. L. Hamilton.
fire -damaged watershed.
Separate 629, February.
Amer.
1955.
Soc.
42
Streamflow reactions of a
Civil Eng. Proc. Vol. 81,
ESTIMATION OF SOIL TEXTURE AND PLANT AVAILABLE WATER BY CORRELATION
WITH THE LASER LIGHT -SCATTERING METHOD
R. L. Haverland, D. F. Post, L. R. Cooper, and E. D. Shirley
University of Arizona, Tucson, AZ 85720, and Southwest Rangeland Watershed Research Center, 2000 E. Allen Road, Tucson, Arizona 85719
INTRODUCTION
Particle -size distribution and plant available water are basic inSince the convenput to studies of range, forest and cultivated land.
tional laboratory procedures for determining these parameters are time
consuming, an improved method for making these measurements is desirable.
Weiss and Frock (1976) reported results from an instrument employing the principle of laser light scattering to measure particle -size
distribution.
The instrument was reported to be of high precision, and
yielded reproducible results.
The laser light- scattering instrument
used in this study is the * Microtrac Particle -size Analyzer Model 79910, manufactured by Leeds and Northrup. The particle -size analysis range
of this model is from 1.9 to 176 11m, which does not correspond to the
entire fine earth fraction (< 2 mm) usually characterized by soil scientists.
It is, therefore, desirable to develop predictive equations to
estimate the soil texture of the fine earth fraction. We believe data
from this instrument could be used to predict other soil properties.
This paper reports on using Microtrac data to estimate the plant available water holding capacity and soil texture of Arizona soils.
Two hundred and forty-seven Arizona soils were used in this study.
Most of these soils (approximately 230 soils) are thermic or hyperthermic and arid or semiarid soils of dominantly mixed mineralogy, as described on the Arizona General Soils Map (Jay et al., 1975).
An array
of soil horizons are included, with approximately one half of the samples coming from the A or Ap surface horizons. The other half of the
samples are from the subsurface B or C horizons.
METHODS AND MATERIALS
General Description and Operation of the Microtrac
A brief description of the operation and data output of the Micro trac, and some of the previous work with the instrument, is given below.
The papers by Weiss and Frock (1976), Wertheimer et al. (1978), Haver land and Cooper (1981) and Cooper et al. (1 984) describe, in greater
detail, the operation of the instrument.
*Trade names are included for information of the reader, and do not constitute endorsement by the United States Department of Agriculture or
the University of Arizona.
43
Other research results have been published using data obtained from
this same instrunent.
Cooper et al. (1984) compared the sieve- pipette
and Microtrac methods of particle -size analysis for 10 soils representing a wide range of size distribution.
They concluded that regression
analysis may be used to convert Microtrac results to those of the sieve pipette method with an acceptable degree of accuracy; however, this is
best achieved when done by specific particle -size ranges.
The agreement
between the two methods was highest for the 62 and 31 um and 31 to 16 um
particle size ranges.
These workers also studied the effect of organic
matter and soluble salts on Microtrac results, and they report a slight
improvement in the data when these two components had been removed.
Haverland and Cooper (1981) reported on the effect of sample dispersion
techniques and sample concentrations on Microtrac analyses.
They further studied the relationship between the specific surface data obtained
from Microtrac and other specific surface measurements, and they reported an excellent correlation between the two methods.
The Microtrac measures particle size by low -angle forward- scattering of laser light which has passed through a sample cell (Wertheimer et
al. , 1978).
The laser light is produced by a helium neon source of 0.6
um wavelength, and the nature of scattering is dependent upon the ratio
of particle diameter to the wavelength of the laser light.
The relationship of particle size to the intensity and angle of scatter of the
laser light, after light -particle interaction, is of prime importance in
Microtrac theory. Light intensity is directly proportional to the particle diameter squared, whereas the angle of light scatter is inversely
proportional to the diameter of the particle (Jenkins and White, 1975).
Following the interaction of light and particle in the sample cell,
the light passes through a rotating Fraunhoffer plane optical filter,
which has openings designed to accomodate light fluxes proportional to
respective powers (d2, d3, and d ) of the particle diameter.
A photodetector and microcomputer convert the scattered light into numerical
data describing particle -size distribution.
The concentration is displayed visually, and data is recorded by a digital printer.
Samples are put in aqueous suspension, with two sample cells available for use, depending upon sample concentration. A 4 -liter chamber is
used for an approximate range of 2,000 -40 mg /1, and a smaller 250-ml
chamber can be used for an approximate range of 400 -40 mg /1.
In this
research, we used only the 4 -liter chamber.
Data is provided on 13 channels (size fractions) between 1.9 and
176 um yielding both channel percent and cumulative percent less than
(or cumulative percent greater than).
These size ranges correspond to
one half phi intervals of the Udden - Wentworth scale as expressed by
Krumbein (1934) where phi = -log2 (diameter, mm).
This notation is
widely used in sediment analysis.
Output includes a cumulative graph, a relative volume graph, cumulative and histogram data, and summary data.
Summary data consist of
44
the values, in um, at the 10th, 50th, and 90th percentile points, the
the calculated mean spemean diameter of the volume distribution (um)
cific surface area, and a value (dv) representative of sample concentraAll data, except the sample concentration term, were used in this
tion.
study.
Sample Preparation and Data Computation
,
Sample preparation and dispersion remain important factors for preOur Microtrac sample dispersion was
cise and reproducible analysis.
accomplished by a combination of two treatments, a chemical dispersant,
and the subsequent application of ultrasonics immediately prior to analThe chemical dispersant consists of 53.52 g Na2P02 and 4.24 g
ysis.
Na2CO3 in one liter of distilled water (C. L. Lameris, 1964), which is
A 350
added to the soil at a ratio of 1 ml dispersant /1 gram soil.
watt, 20 KHz ultrasonic probe, with a 1/2 -inch disruptor horn, was submerged into the soil solution, and ultrasonics were applied for 30 secFollowing this dispersion, each sample was wet- sieved with an
onds.
Although 176 um is the upper size limit of
ASTME No. 80, 180 um sieve.
this Microtrac model, the difference between 180 um and 176 um is consiWe included this gravimetric measurement (% > 180 um)
dered negligible.
as an additional "channel of data ". We further used this information to
calculate a "corrected" Microtrac parameter for the 13 channels from 176
The following computation corrects the Microtrac dat -a for
to 1.9 um.
the sand fraction from 180 um to. 2 mm, which was not measured by Microtrac:
¡wt of sample > 180 um
total sample wt. < 2mm
r
X Microtrac data
for)
each size category
l
sand "correct= ed" Microtrac
(1)
data.
The 13 size
The descriptions of all variables are given in Table 1.
categories from 176 to 1.9 Pm are all "corrected" variables. The other
variables-specific surface, mean diameter, 90th, 50th and 10th percenTwo additional variables were
tile were used as recorded by Microtrac.
generated from the basic data, and have been called 'Microtrac sand" and
Microtrac sand = (114 + V5 + V6 + V7 + .67 (V8)), and
Microtrac silt = (.33078) + V9 + V10 + V11 + V12 + V13 + V14 + V15 +
These two variables very closely approximate the size -of sand
V16).
(2.00 -.05 mm) and silt (.05 -.002 mm) particles. However, these calcu"Microtrac silt ".
lations cannot be corrected for that portion of the sample below the
Microtrac Model 7991 -0 sensitivity (< 1.9 um); so the true value would
be lower.
Microtrac data through correlation and regression analyses were related to the percent sand, silt, and clay determined by conventional
procedures (Day, 1965) for pipette or hydrometer analyses. This textural
data came from various sources, including analyses completed by the
authors, by the National Soil Survey Characterization Laboratory, and
soil and water science graduate students at the University of Arizona.
The plant available water content was determined at 1/3 and 15 bars of
In the statistical analyses,
tension, as described by Richards (1965).
Microtrac data was used as the set of 19 independent variables, and X
45
sand, % silt, X clay, % water at 15 bar, and % water at 1/3 bar were the
Variables were retained in the multilinear regresdependent variables.
sion equation which met the 90% confidence level, or which significantly
increased the coefficient of determination.
RESULTS AND DISCUSSION
Relating Microtrac Results to Soil Texture
Correlations were performed in two ways, with the data from all 247
soils combined, and with the data stratified into the 12 textural classes as defined in USDA Handbook 18 (1951). The sample population did not
include any silt textures, but there are data for the other 11 classes.
Table 1 presents the means and standard deviations for each textural
Table 2 presents the
class for all variables used in this research.
predictive equations for all soils and for each textural class, and
lists the coefficient of determination (r2) and significance level of
These equations are produced by a series of computer
each equation.
generated step -wise multiple linear regressions. Generally, the correlations were lower in classes with a high number of samples, but the significance levels were improved.
Figures 1, 2, and 3 show, graphically, the relationship for all 247
soils between the conventionally .determined sand, silt, and clay content
versus the sand, silt, and clay content predicted from Microtrac data.
The percent sand prediction was the best (r2 = .82); silt was next (r2 =
All predictive equations
.68); and clay was least accurate (r2 = .63).
are highly significant, except the sand'determination for the sand textural class and the clay and sand determinations for both loamy sand and
sandy clay textural classes, and although the Microtrac does not measure
The
particles less than 1.9 um, percent clay can still be estimated.
clay fraction is partially predictable from the variation of clay content with the data of the size fractions measured by the Microtrac.
The soil texture of an unknown sample may be estimated either by
preliminary use of the qualitative "feel" method (hand manipulation of
sample), or by preliminary use of the equation set developed for all
Then,
soil textures (Table 2), to select the approximate soil texture.
the appropriate set of equations for that textural class should be used
to improve the prediction.
Relating Microtrac Results to Plant Available Water
Ninety -one soils from the 247 total soils had plant available water
information.
Table 3 presents the correlation coefficient (r value) for
each of the Microtrac size categories. We ran the correlations on both
the "uncorrected" and "corrected" Microtrac data (Eq. 1), and the corrected data gave the better relationships, particularly for the wilting
For the wilting -point (corrected) data, the
point (15 bars of tension)
16 - 11 um, 11 - 7.8 um, 7.8 - 5.5 um, 5.5 - 3.9 um, and the 2.8 - 1.9
11111 size channel, plus the mean diameter and 50th percentile data, showed
.
46
Description of variables and the means and standard deviation
(a) by soil textural classes.
ID Number and
Loamy
Sandy
description
Sand
sand
loam
n* = 5
of variables
n =7
n = 50
Table 1.
Mean
Conventional analysis
V1
% sand
V2
%
silt
V3
%
clay
V4
X .176 -2.0 mm
Mean
a
89.52
5.54
4.96
54.42
3.89
2.75
2.13
14.15
12.01
9.43
5.71
2.94
2.87
2.00
0.64
1.71
1.30
1.78
1.76
1.23
1.98
0.35
79.64
152.80
77.28
8.60
14.26
83.53
4.12
4.11
2.75
1.44
2.50
2.01
0.96
1.16
a
74.9
17.93
7.20
43.15
Mean
a
14.23
9.44
5.00
25.74
63.19
24.80
12.06
29.09
7.44
6.37
3.61
12.96
6.49
7.89
4.59
3.00
3.32
1.44
0.92
1.57
8.98
9.88
7.90
7.37
5.66
4.40
3.02
3.64
1.26
1.26
0.93
1.22
1.57
3.51
2.15
4.59
3.75
2.77
3.34
2.59
2.23
1.69
1.67
1.49
1.62
1.20
1.37
1.61
1.89
Microtrac Analysis
V5
V6
V7
V8
V9
V10
Vil
V12
% 176 -125 um
% 125 -88
% 88 -62
% 62 -44
% 44 -31
% 31 -22
% 22 -16
X 16 -11
% 11 -7.8
V14 % 7.8 -5.5
V13
% 5.5 -3.9
V16 X 3.9 -2.8
V17 X 2.8 -1.9
V15
V18
Specific surf.
V19
Mean diameter
V20
90 percentile
50 percentile
10 percentile
Microtrac silt
Microtrac sand
V21
V22
V24
V28
1.35
1.81
0.92
1.14
1.41
0.14
10.92
4.27
15.25
8.07
5.93
7.25
10.84
11.47
8.18
5.53
4.93
3.35
1.23
2.35
1.53
1.06
2.16
2.04
1.81
0.35
70.60
144.50
62.21
7.99
20.50
77.32
.
18.87
8.04
8.92
10.28
Silt
loam
Loam
n
0.10
11.58
7.73
= 60
4.38
4.98
0.54
54.70
130.30
40.68
3.50
33.81
60.77
0.14
9.08
11.48
12.50
1.04
6.54
7.43
Sandy clay
loam
n = 11
n
= 32
Conventional analysis
V1
V2
X sand
%
silt
clay
V3
%
V4
% .176 -2.0 mm
45.85
34.00
20.17
20.90
9.34
6.86
4.41
9.31
21.85
58.39
18.92
13.35
8.38
6.15
6.32
14.49
*n is the nunber of samples in the given texture class.
47
51.65
23.88
24.38
27.94
10.09
9.32
3.55
10.99
Table 1.
(Continued)
ID Number and
description
of variables
Silt
n
Sandy clay
loam
loam
Loam
n=
= 60
Mean
a
6.27
7.79
7.90
7.58
6.33
4.86
3.63
4.89
4.59
2.94
6.84
6.99
7.97
0.69
43.78
112.41
26.32
3.05
43.61
47.91
2.49
2.43
2.49
2.07
2.11
1.58
1.94
1.93
1.74
1.64
2.47
2.93
3.13
0.17
10.31
15.79
14.82
0.98
8.42
10.42
n = 32
11
Mean
a
Mean
a
Microtrac Analysis
V5
% 176 -125 pm
V6
X 125-88
X 88 -62
X 62 -44
V9
% 44 -31
V 10 X 31 -22
V11 X 22 -16
V12 X 16 -11
V7
-
V8
V13
X 11 -7.8
V14
X 7.8-5.5
X 5.5 -3.9
V16 X 3.9 -2.8
V17 X 2.8 -1.9
V15
V18
V19
V20
V21
V22
V24
V28
Specific surf.
Mean diameter
90 percentile
50 percentile
10 percentile
Microtrac silt
Microtrac silt
2.86
5.17
5.79
7.40
8.76
8.06
7.61
8.00
6.81
5.11
7.17
6.55
6.83
,
Cl ay
Silty clay
loam
loam
n
Conventional analysis
VI X sand
V2 X silt
V3
X clay
V4 X .176 -2.0 mm
0.67
32.66
84.75
19.33
3.41
60.52
32.10
1.97
2.64
2.67
3.07
2.86
1.85
2.38
2.21
2.63
1.56
2.64
2.92
2.67
0.17
9.77
25.83
7.84
1.19
12.67
14.75
n =6
=57
5.43
6.76
7.19
6.09
4.86
4.07
3.27
4.28
3.97
2.77
7.12
7.15
8.42
0.78
40.69
107.48
22.64
2.60
39.52
51.39
2.52
3.51
3.31
2.52
2.09
1.93
2.35
1.96
2.04
1.69
1.50
1.73
2.19
0.14
10.41
24.75
10.69
0.14
9.89
11.04
Sandy
clay
n
=5
35.89
30.50
33.08
17.10
5.93
6.00
3.33
5.18
11.82
54.80
33.17
4.95
4.41
2.75
3.28
5.67
50.57
11.40
38.03
32.73
4.65
1.95
3.18
19.98
3.92
5.28
6.54
6.51
5.52
4.57
4.63
5.77
5.34
3.69
1.64
1.57
2.54
2.07
2.06
1.81
1.94
1.87
2.10
1.92
0.85
2.51
5.41
5.35
6.62
7.88
8.04
9.95
9.67
6.76
0.87
1.64
2.88
1.19
1.77
1.76
2.04
1.54
0.98
0.82
1.45
3.39
5.01
3.83
4.85
2.64
1.22
2.24
Microtrac Analysis
V5
V6
V7
V8
V9
VIO
V11
V12
V13
V14
X 176-125 um
X 125-88
% 88-62
X 62-44
X 44-31
X 31-22
X 22-16
X 16-11
X 11-7.8
X 7.8-5.5
48
4.69
5.47
4.36
3.51
4.22
1.77
4.40
1.10
1.07
0.51
1.49
0.57
Table 1.
(Continued)
ID Number and
description
of variables
Clay
loam
n
Silty clay
loam
= 57
n
o
Mean
Sandy
clay
=6
n
Mean
=5
o
Mean
o
3.03
3.21
2.79
0.14
5.69
16.89
3.19
0.24
5.96
9.05
9.48
9.19
0.97
24.80
70.93
9.60
2.53
45.32
45.13
1.07
2.34
4.66
0.10
7.39
23.98
3.03
0.14
8.18
12.79
Microtrac Analysis
V15
V16
% 5.5 -3. 9
% 3.9 -2. 8
V17
% 2.8 -1.9
V18
Specific surf.
Mean diameter
90 percentile
50 percentile
10 percentile
Microtrac silt
Microtrac sand
V19
V20
V21
V22
V24
V28
9.43
9.80
11.39
0.88
32.34
92. 82
12.78
2.61
50.91
37.18
2. 24
3.04
3.75
0.14
5.51
14.98
5.29
0.39
5.71
6.65
10.93
10.24
10.36
0.87
21.90
57.97
11.22
2.73
71.87
17.28
7.51
Silty
Cl ay
clay
n
Conventional analysis
V1 % sand
V2 % silt
V3
% clay
V4 Z .176 -2.0 mm
7.00
45.00
48.00
8.44
=3
n =
11
3.36
3.22
5.02
12.62
17.11
31.66
50.32
14.28
11.21
7.50
12.33
5.88
1.94
2.04
1.47
2.29
3.34
3.26
4.97
3.56
8.00
2.09
9.81
1.79
7.38
1.36
15.74
5.73
6.99
15.91
16.84
7.36
0.30
1.20
14.48
9.13
38.50 10.99
2.61
6.57
0.17
2.42
69.18 11.44
13.57 16.68
2.65
4.02
5.56
5.23
5.18
4.43
5.66
7.19
6.92
5.06
10.62
10.97
11.73
0.94
26.62
76.27
10.67
2.61
57.76
30.00
2.27
2.99
2.02
2.22
1.91
2.02
1.30
1.49
2.77
2.11
2.98
3.80
4.74
0.20
9.34
27.52
5.15
0.30
7.73
10.32
Microtrac Analysis
% 176 -125 um
% 125 -88
V7 % 88 -62
V8
% 62 -44
V9 % 44 -31
V10 % 31 -22
V11 % 22 -16
V5
V6
V12 % 16 -11
V13 Z 11 -7.8
V14 Z 7.8 -5.5
V15 % 5.5 -3.9
V16 % 3.9 -2.8
V17 % 2.8 -1.9
V18 Specific surf.
V19 Mean diameter
V20 90 percentile
V21 50 percentile
V22
10 percentile
V24 Microtrac silt
V28 Microtrac sand
1.41
1.00
1.24
2.20
2.78
3.85
49
Table 2.
Predictive equations for
textural class.
Texture
class
Dependent
variable
All
Clay
Y
Silt
Y
Sand
Y
Clay
Y
Silt
Y
Sand
Y
t exture
classes
Sand
Clay
Y
sand,
%
silt, and
%
r2
= 12.18 + .90(V14) + 1.65(V15)
+ .07(V19) - .24(V21)
= 12.04 -.46(V5) + .95(V10) 17.84(V18) + .67(V24)
= - 20.71 + .76(V5) + 1.30(V16)
+ 1.110/28)
.63
<
.68
< .01
.82
<
= 6.21 +. 51(V13) - .22(V22)
= 1.39 + .74(V7) + 2.08(V12)
-.100/22)
= 82.33 - .48(V7) + 11. 71(V18)
.99
.55
.99+
.29
.99+
3.54
.84
9.96
= 26.05 - 1.96(V7) + .91(V8)
+ 4.90(V12) - 26.85(V18)
= 63.16 + 2.05(V7) - 6.84(V12)
.99
1.17
.71
23.69
= 7.70 + .44(V13) + 1.08(V17)
- .73(V22)
= 30.03 + .56(V6) + 1.01(78)
+ .95(V10) - .23(V20) + .21(V24)
= 25.54 + .41(V5) - 1.05(V8)
.62
<
.01
.72
<
.01
.77
<
.01
.01
.01
.68(V22)
= 4.93 - .48(V7) + .25(V9)
+ 1.86(V 12)
Silt
Y
Sand
Y
Clay
Y
Silt
Y
Sand
Y
+
Loam
icanc e
level
+ 37.37(V18)
Sandy
loam
clay by
Si gni f-
Predictive equation
+
Loamy
s and
%
Clay
Y
Silt
Y
Sand
Y
.69(V28)
= - 4.81 - 1.69(V5)+ .31(V7)
+ .50(V17) + .29(V20) - .15(v21)
= 103.22 - 55.47(V18) - .38(V19)
- .53(V21)
= - 33.42 - .46(V7) + 1.66(V12)
- 2.02(V15) + 85.38(V18)
.64
<
.01
.74
<
.01
.76
<
.01
= 40.58- 2.07(V7) + 1.79(V8)
.93
.11
.98
1.02
.82
.12
+ 1.12(V21)
Silt
Clay
Y
loam
Silt
Y
Sand
Y
- .84(V12) - .84(V21)
= 3.11 + 7.04(V10) - 2.87(V11)
- 7.22(V13) + 13.57(V15)
- 5.46(V17) + 1.75(V19) - 2.47(V21)
= - 7.37 + .88(V11) + 6.60(V22)
50
Table
2.
ie xture
class
(Continued)
Signif-
Dependent
predict ive equat ion
variable
Sandy
Clay
c lay loam
Silt
icanc e
level
= 47.27 + .58(V12) - 22.98(V18)
- .33(V21)
Y = 1.62 + .33(V21) - 9.21(V22)
Y
+
Sand
r2
.50
.02
.93
< .01
.94
<
.98(V 24)
Y = 2.81 - 1. 16(V12) + 17.48(V18)
.01
+ 0. 78(V28)
Clay
Clay
Y
Silt
Y
loam
Sand
Y
Silty
Clay
c lay loam
Y
Silt
Y
Sand
Y
Clay
Y
Sandy
clay
Silty
c lay
Clay
= 56.09 + 1.39(V17) - 38.00(V18)
- 0.40(V21)
= 22.46 + 1.82(V6) + 1.49(110)
- .87(V19) + 7. 58(V22)
= -28.90 + 1.06(V16) - 1.38(V17)
+ 40.32(V18) + .93(V28)
.23
= 39.97 + .43(V7)
3.06(V8)
.99+
.14
= 52.02 + .46(15) - 2.20(V8)
+ 1.20(V9) + .78(V10)
= 9.49 + 5.51(V8) - 2.66(V9)
- 1.41(V14)
.99+
.10
.99+
.47
Sand
Cl ay
Y
Sand
= 60.90 - 1. 51(V11) - .55(V13)
Y = 43.63 + 1.89(V5) - 1.30(V6)
Y - 4.71 + 1.23(V10) + .71(V13)
Clay
Y
Silt
.51
<
.74
.01
< .01
+ 1.07(V 14)
= 57.51 - 5.54(V14)
Y = 5.64 + .08(120)
Y = 55.95 - 3. 72(V5)
Silt
.27
.99
.99+
.95
6.14
1.56
14.54
.99+
.08
.47
.29
= 168.89 + 5.70(V5) - 6.63(V8)
- 6. 74(V12) + 121.47(V 18)
.94
.46
= 3.37 - 9.12(V5) + 6.49(110)
+ 7.48(V15) - 5.27(V 16) +
.86(V20) - 24.20(V22)
= 2.69 + 2.83(V8) + 2.43(V9)
.99
.04
.97
.35
.99+
.99+
+ 66.47(V22)
Silt
Y
Sand
Y
+ 2.98(V 11) - 2.26(V 14)
- .45(1/20) + .52(V28)
51
Microtrac predicted clay = 12.18 +.9007.8 -5.5 um)
+ 1.6500.5 -3.9 tim) + .07(mean diameter, um) -.24
(50th percentile, um)
r2 = .63
90.8
88.8
LIFE OF Em.. VALLE
78.8
}
68.8
ú 58.8
w 40.8
WWI
u
ó 30.8
W
á 28.8
18.8
.888
i000
I
18.8
i
28.8
1
1
1
38.8
40.0
50.0
I
68.8
t
78.0
1
88.8
i-
98.0
CONUENTIONAL CLAY (x)
Figure 1.
Plot of Microtrac predicted percent clay
(from multiple regression analysis given
above) with the conventionally determined percent clay.
S2
Microtrac predicted silt = 12.04 -.46(176 -125 um)+
.95(31 -22 um) -17.84 (specific surface) + .67(%
Microtrac
silt)
r2 = .68
90.8
88.8
LINE OF EQUAL VALLE
x 70.8
I..
68.8
cn
58.8
.t
O
48.8
u
W
.1
.
-
38.8
P.
á 28.8
10.8
.888
1
008
I
18.8
t
20.0
t
1
t
38.8
40.8
50.0
t
60.8
I
78.8
i
88.0
t-
98.8
CONUENTIONFlL SILT (x)
Figure 2.
Plot of Microtrac predicted percent silt
(from multiple regression analysis given
above) with the conventionally determined percent silt.
53
Microtrac predicted sand = -20.71 +.76( %176 -125 Um)
+ 1.30( %3.9 -22 um) + 1.11 (% Microtrac sand)
r2 = .82
98.8
88.8
78.0
60.8
50.0
48.8
30.0
28.8
18.8
.888
688
18.0
28.6
30.8
48.0
58.8
60.0
78.0
88.0
98.8
CONUENTIONAL SANO (z)
Figure 3.
Plot of Microtrac predicted percent sand
(from multiple regression analysis given
above) with the conventionally determined percent sand.
S4
Table 3.
Correlation coefficients (r values) between Microtrac
variables and the wilting point and available waterholding capacity of 91 Arizona soils.
Wilting point
Uncorrected
Corrected
.176 -2.0mm
Plant available water
Uncorrected
Corrected
-.28
-.50
176 - 125 um
-.59
-.48*
-.43
-.40*
125 - 88 um
-.62
-.46
-.40
-.33
88 - 62 um
-.51
-.33
-.33
-.23
62 - 44 um
-.37
-.17
.01
.03
44 - 31 um
-.24
-.02
-.02
.10
31 - 22 um
- .03
.20
.27
.30
22 - 16 um
.40
.46
.54
.52*
16 - 11 um
.59
.65*
.46
.51
11 - 7.8 um
.53
.64*
.32
.38*
7.8 - 5.5 um
.52
.66*
.14
.30*
5.5 - 3.9 um
.54
.65*
.19
.28*
3.9 - 2.8 um
.49
.58
.16
.24
2.8 - 1.9 um
.50
.60
.08
.18
Specific surface
.57
**
.19*
**
Mean diameter
-.65*
**
-.39
**
90th percentile
-.57
**
-.34
**
50th percentile
-.65*
**
-.44*
**
10th percentile
-.39
**
-.30
**
*Variables selected for the step -wise multiple linear regression equat ion.
**These variables were not corrected for the 176 - 2000 um fraction.
55
the best correlations, having r values of .60 or better.
regression relationship is:
The multiple
% Water at the Wilting Point = -13.88 -.46( %176 -125 um)
+ .60( %16 -11 um) + .41( %11 -7.8 Um) + .62( %7.8 -5.5 Um)
+ .83( %5.5 -3.9 um) +.55 (mean diameter) -.30 (50th percentile).
This relationship has an r value of .75,
(2)
and a significance level of
< 0.01% (very highly significant).
The correlation coefficients for plant available water, defined as
the % water at .33 bar minus the % water at 15 bars of tension, are less
than those for the wilting point alone. Only the 22-16 um and 16 -11 um
size fractions have an r value greater than .50, and there is less difference between the uncorrected and corrected data. The multiple regression relationship is:
Plant Available Water = 17.21 + .10(% 176 -2000 um) + .39
( %22 -16 um) +
.36( %11 -7.8 um) -.31( %7.8 -5.5 um) + 1.03
( %5.5 -3.9 um) -19.89 (specific surface area) -.14(50th
percentile)
(3)
.
This relationship has an r value of
.67 and
a significance level of
< 0.01 %.
SUMMARY AND CONCLUSIONS
Microtrac analysis is very rapid, requiring less than 5 minutes per
sample.
The 13 particle -size ranges, plus the other data, provide a
detailed particle -size distribution look at the soil.
The predictive
equations for each of the textural classes are quite different, and
these should be used to better estimate the percentage of soil separates.
Percent sand can be predicted the most accurately.
However, corrected data (for the .176 - 2.0 mm fraction not analyzed by the Micro trac) should be used rather than the uncorrected data.
Perhaps the greatest future use of Microtrac data will be in predicting soil properties other than soil texture, like plant available
water.
The relationships presented in this paper show there is potential, but additional research and evaluation are needed.
These equations
are not intended for general usage, but rather may be useful criteria in
judging the feasibility of using such an approach. The number of samples
in certain texture classes is low, and it may be found that other stratifications of the data, such as by sample source area, may be more useful.
The variability in the conventional analyses of % sand, % silt, %
clay, plant available water and water content at wilting point needs to
be studied.
We believe there is a significant coefficient of variability in these laboratory measurements that has affected our results. It
is expected that Microtrac models capable of analyzing particles of
smaller size should produce somewhat higher correlations, particularly
for predicted % clay.
56
REFERENCES
Cooper, L. R., R. L. Haverland, D. M. Hendricks and W. G. Knisel.
1984.
Microtrac particle -size analyzer:
An
alternative particle -size
determination method of sediment and soils.
Soil Sci. 138:138 -146.
Day, P.
R.
1965.
Particle fractionation and particle -size analysis.
Methods of Soil Analysis, Part 2, Agronomy 9, C. A. Black, D. D.
Evans, J.
L. White, L. E. Ensminger and F. E. Clark (eds.), Am. Soc.
Agron., Madison, WI.
In:
Haverland, R. L. and L. R. Cooper.
1981.
A rapid particle size analyser of sediment and soils. Hydrol. and Water Resour. in Arizona and
the Southwest, Proc. Ariz. Sec., Am. Water Resour. Assn., Hydrol.
Sec., Arizona -Nevada Acad. of Sci. 11:207 -211.
Jay, J. E.,
Guernsey.
Y.
H.
1975.
Havens, D. M. Hendricks,
Arizona General Soil Map.
Jenkins, F. A. and H. E. White.
Hill, 4th Edition.
Krumbein, W.
C.
1934.
Sed. Petrol. 4, 65 -77.
1975.
D.
F.
Post
and
Fundamentals of Optics.
C.
W.
McGraw-
Size frequency distribution of sediment.
J.
.
Lameris, C. L.
1964.
The mechanical analysis of soils in general, and
the effect of anions on the dispersability of lateritic soils in particular.
Neth. J. Agric. Sci. 12, No. 1:40 -56.
Richards, L. A.
1965.
Physical condition of water in soil. Methods of
Soil Analysis. Amer. Soc. Agron., Inc., Publisher, Madison, WI, Part
I :128 -151.
Soil Survey Staff.
1951.
Soil survey manual.
U.S. Dep. Agr. Handbook
18, U.S. Govt. Printing Office, Washington, D.C.
Weiss, E. L. and N. H. Frock.
1976.
Rapid analysis of particle -size
distributions by laser light scattering.
Powder Tech. 14:287 -293.
Wertheimer, A. L., H. N. Frock and E. C. Muly. 1978.
Effective utilization of optics in quality assurance.
Spie 129:49 -58.
57
CASA DEL AGUA: RESIDENTIAL WATER CONSERVATION RETROFIT
by
Richard Brittain, K. James DeCook and Kennith Foster
INTRODUCTION
Casa del Agua is Tucson's water demonstration model home. It is in the
advanced stages of retrofit. Conversion and use of the house, which is owned by the
City of Tucson, is a cooperative project of City of Tucson Water, the University of
Arizona Office of Arid Lands Studies and College of Architecture, the Southern
Arizona Homebuilders' Association (SAHBA), the Southern Arizona Water Resources
Association (SAWARA), and others. The retrofitting process is being aided by
contributions of labor or materials by Tucson Water, The Estes Company, Miller
Homes, Desert Survivors, Job Corps, Garden America, Columbia Materials,
Benjamin Plumbing and Supply, Thetford Energy Saving Products, and General
Electric Sales. In addition, the City of Tucson - Pima County Metropolitan Energy
Commission proposes to participate in the application and demonstration of energy
conservation features. Thus, the model home truly represents a community effort.
PURPOSE AND PRINCIPAL COMPONENTS
Casa del Agua is both a research facility and a demonstration site. It provides
an opportunity for reseachers to test and document the operational results of
residential water conservation systems, and for the public to see close -up water and
energy conservation devices and methods, and to benefit from ongoing evaluation of
the system's practicality, cost, and performance characteristics.
Full implementation of the retrofit design would change the typical daily per
capita residential water input of more than 100 gallons (Figure 1), all supplied by
groundwater, to a three -source daily per capita input requiring less than 35 gallons
of pumped groundwater (Figure 2). Thus, the required input of groundwater is
reduced by two -thirds and a further result is that the per capita flow discharged to
the sewer system is reduced from about 60 gallons to about 5 gallons per day, a
reduction of more than 90 percent.
The basic design components for water conservation are a rainfall harvesting
system and a greywater reuse system. Additional features are installation of water saving fixtures indoors, relandscaping outdoors, and a greenhouse installation. The
rainwater harvesting system incorporates eave gutters and downspouts for rain
collection, two steel storage tanks with combined capacity of 14,500 gallons, and a
filtration chamber. The harvested water can be used for toilet flushing and
evaporative cooler supply (Figure 3), or possibly as partial supply for landscape
irrigation.
The greywater system will require modification of the house drain, waste, and
vent system. It will collect discharged water from tub, shower, lavatory, washing
machine and kitchen sink (except the garbage -disposal side), all of whicn will be
directed through one of two parallel greywater treatment systems, one mechanical
and the other biological. The mechanical filter is a commercially available system
59
leakage
41 GPCPD
outdoor consumption
C
car washing
swimming pools
Irrigation
J
variable
variable
variable
r3 GPCPD (6%)
12.2 GPCPD (20%)
clothes wash /dish
drink /cook
18.3 GPCPD (30%)
bathing /personal
27.6 GPCPD (46%)
Figure 1. Fiscal Year 1979 Tucson residential water use.
(Consumptive use distribution adapted from Milne, 1976)
variable
102 gallone
daily per capita
water consumption
Indoor consumption
81 GPCPD
toilets
JIyl.r
1600 gal.
?SP3,:
pump
pump
chlorHralor
pr.aur lank
33.6 OPCPD
municipal water npul
:: I
e.:.,.,'r3i
. : t.
i...i
Ods hap
Illem.nl lank
guyw.l.d slorag.,
12000 gal.
a.11l.mnl lank
and
ralnwal.r loups
II
I
moist
valve.
....... ..: .......
car washing - variable
Hrlyallen - wadable
3 GPCPD drink /cook
e OPCPD dish wash
l 3 OPCPD c1o1M wash
16 3 OPCPO bih /p.donal
I1
s,
'I
consumad
_Orono
1
1
1
I
\
1
I
A
.r
pllc
r
1
1
1
1
1
I
occlonal
dlviÌv r
runoll
gloundwald
6 GPCPO
Ot
I
`b,
3+ M
QöP,ë:.¢:.iïicf'P
GQ-
.. IV .w 4.1
ó
16 GPCPO
vap. coulai - varlebl
l WNSl u)
OUTDOOR: 11 GPClID
Figure 2. Theoretical water use for the demonstration water conservation project.
constant
Iheorelically
supply
1 efficiency
area
catchment
upon
supply
dependent
I
16 OPCPO
ln calchm.nl
backup
4
human
ç7
4
wash.
s
landscape
storage
0 S weekly
slow sand filter
hyacinths
Igreywater
4
toilet
.
runoff events
OS between
As- storage volume changes
- flowmeters
hose
bibb
storage
IRooftop Runoff
cooler
Figure 3. Water Flow Diagram
shower /tub/
machine consumption
lavatory
watermaide
sink
4
to house
standard water meter
Fresh Water
called WaterMaide, which uses a settling tank and a cartridge filter. The biological
system contains two water hyacinth aquaculture tanks in which the plants absorb
nutrients and help break down contaminants, and a slow- sand -filter intensive garden.
The treated water will flow by gravity to an 800 -gallon storage tank, from which it
can be pumped to supply landscape irrigation (Figure 3), or possibly can be used for
toilet flushing.
The following table illustrates one possible set of water quantities that may be
produced and consumed under the proposed design. Many variations may be possible
under actual operating conditions - for example, reducing water use in the
evaporative cooler by shading or night use, or using some direct rainfall for
landscape watering by appropriate earth contouring.
Example of Water -Use Budget for
Rainwater and Greywater
(Gallons /Year)
Rainwater Supply
Rainwater Use
15,500
(Cooling)
(Toilets)
9, 500
6, 000
1/
2/
3/
15,500
Greywater Supply
24,100
4/
Greywater Use (Landscape)
24,100
5/
1/
Based upon collection area (rooftop) of 2,500 ft¿, 11 inches annual
rainfall, and runoff efficiency of 90 percent.
2/ Assuming average water use of 90 gal /day for 105 days,
3/ Assuming 1.5 gal /flush, 5 flushes /day /person, and 2.2 persons per house-
hold. (Note: Actual use may be 1.0 to 1.5 gal /flush.)
4/ Assuming 30 gal /day /person greywater produced, and 2.2 persons per
household.
5/ New landscape design to match greywater supply.
Water -saving fixtures include the Thetford Superinse, IFO Cascade and Eljer
Ultra -One toilets, rated at about i gallon per flush, and low -flow shower -heads and
faucet outlets.
63
The greenhouse is the location for the greywater purification system that
houses the water hyacinths. The greenhouse also provides space for a food garden
and partial shade for the south -facing wall of the house.
Through the new landscape design and strategic re- planting using drip
irrigation, outdoor water consumption will be significantly reduced. Low -water -use
plant species will be selected from SAWARA's list of endorsed drought- resistant
plants. Vines will be placed to grow on trellises and shade sun -exposed walls,
contributing to energy savings.
CONCLUSION
Casa del Agua will be officially opened for public visitation by the end of
September 1985. A "hands -on" policy will permit visitors to activate portions of the
system. A family has been selected to occupy the residence, providing real -life
conditions and assisting in the testing and monitoring of water and energy
conservation features.
64
ARIZONA WATER INFORMATION CENTER:
FOUNDATION AND ACTIVITIES
Kennith E. Foster
University of Arizona, Office of Arid Lands Studies
845 N. Park Ave.
Tucson, Arizona 85719
L.G. Wilson
University of Arizona, Water Resources Research Center
Tucson, Arizona 85721
INTRODUCTION
During the 10 -year period from 1965 to 1975, Arizona experienced the most
Rising population, improved agricultural
rapid rate of growth of any state.
markets and completion of new steam electric generating plants have added to
the state's water requirements. This growth will require prudent and wise use of
the limited water resources available to the state.
Water resources available for use in Arizona can be categorized as groundwater, Colorado River water and surface water other than the Colorado River.
Development of groundwater has been the most significant single factor supporting recent economic growth in the southern part of the state. Many areas depend
totally on groundwater as the source of supply. Under 1970 normalized
conditions, 60 percent of total use in the state was from groundwater supplies. At
the 1970 level of development, groundwater reserves were being depleted at a
rate of about 2.2 million acre -feet each year, whereas the natural replenishment
was only about 300,000 acre -feet.
Water scarcity, allocations and reallocations of water among competing uses
and rising costs of water service will create a continuing agenda of water and
water -related issues. Many of these issues will have to be addressed by
government decision -makers and legislators. Every segment of the public will
feel the impact of these decisions.
The Central Arizona Project, (CAP) upon completion, will reduce ground-
water overdrafts, but will not relieve the constraint of water scarcity as a
determinant of economic growth and quality of life in the state. In addition, the
Arizona Groundwater Management Act of 1980, which calls for progams and plans
for water management and conservation, will work toward balancing the hydrologic budget of the state.
Effective use, management and conservation of Arizona's water resources
will require a high order of political leadership, sound laws and policies,
engineering and other technological innovation, and - perhaps most critical -a
65
solid base of information upon which to base management programs and public
understanding and support of sensible courses of action.
THE NEED FOR IMPROVED INFORMATION SERVICES
Many public agencies and private institutions in Arizona generate and
distribute information about water. There is not now, however, a central point
within the state to which public planners and /or government decision makers can
go for information about Arizona's water resources.
Among the kinds of
information most frequently sought is information about water use, stream flow,
local or regional water level elevations, water availability and local water quality,
current and future quantities of effluent generated by a community, CAP
allocations to communities, research results, conservation possibilities, water
allocations, water law, policy issues, projects and proposals.
ONGOING PROGRAMS AT THE UNIVERSITY OF ARIZONA
The University of Arizona is a prime source of research on water resources
and is a repository of academic and professional skills. It is the state's land grant
institution with a special responsibility for service to the people, and today is
deeply involved in developing and disseminating information about diverse aspects
of water use, conservation and management. This information now emanates
from several colleges and research units.
The Water Resources Research Center (WRRC) now functions as an intrastate coordinator for research, working with governmental agencies and other
research institutions to formulate research priorities of funds. WRRC also
provides information services through access to national databases and an in -state
outreach program through its Water Resources News Bulletin and Water
Resources Project Information Bulletin. News material and joint editors are
contributed by the University of Arizona's Office of Arid Lands Studies, the
Arizona Department of Water Resources, the Arizona State Land Department,
and the Arizona Department of Health Services. More than 1,000 people receive
these quarterly publications.
The Cooperative Extension Service maintains a network for conveying
information to the people of the state, conducts educational programs off campus,
conducts problem solving and adaptive research, and facilitates public understanding and discussion of public policy issues.
The Office of Arid Lands Studies (OALS) has operated a regional terminal of
the DOE -RECON bibliographic system, which contains several nationwide files
including that of the Water Resources Scientific Information Center (WRSIC).
In addition to these ongoing services, several water -related teaching depart-
ments and research units of the University (the Agricultural and Engineering
Experiment Stations, Bureau of Geology and Mineral Technology, Department of
Hydrology and Water Resources, Tree Ring Laboratory, and Environmental
Research Laboratory are a few examples) employ various mechanisms for
disseminating research results and responding to inquiries in the field of water
66
data and information. Research support exceeds $4 million annually. Work is
being conducted in 19 departments by 44 investigators. Academically, the
University offers 198 courses directly related to water in five colleges.
ACTIVITIES OF THE ARIZONA WATER INFORMATION CENTER
The University of Arizona is in a unique position to fill the need for an
Arizona Water Information Center. As described above, the University is already
providing many information services. It possesses a breadth of expertise and
talent not represented in any other single agency. Development of the Center
will be a natural evolution and expansion of activities already underway in the
University.
Formal establishment of the Center will enable the University to undertake
a number of tasks not now being performed. In addition to providing data on
water resources and uses, it could bring water interests and professionals together
to consider better ways to resolve problems and conflicts and to plan future water
policies and programs.
Activities and programs of the Center are described as follows:
Publications
The WRRC, OALS, Arizona Department of Water Resources (ADWR),
Arizona Department of Health Services (ADHS), and the State Land Department
collaborate to produce the Arizona Water Resources News Bulletin and the
Arizona Water Resources Project Information Bulletin. These are unique in terms
of statewide water information pertaining to research as well as upcoming events,
new publications, and water resource issues. More than 1,000 copies of each are
mailed quarterly to a wide sector of Arizona's population. These publications will
become bi- monthly series as a vital link to Arizona's readers.
Other publications will include a series of information papers, addressing
topics of special interest as selected or approved by the Water Information
Coordinating Committee (WICC) (described below). These papers will summarize
the current state of knowledge on the selected topic, will identify information
gaps and research needs, and will clarify water resource issues under public
discussion. Preparation of the information papers would be assigned by contract
to the appropriate state agency personnel, faculty of the state's university
system, or consultants.
Referral Services
The Center will act as a focal point for response to or referrals of inquiries
to the appropriate individual, department or agency within the state. A WatResources Information Directory consisting of guides to more than 130 w'
related organizations has been compiled and published by the College of Agr
ture as an aid in the referral service.
67
In addition, the Center will initiate a project activity file which will
culminate in a bibliography of research projects and personnel involved in water
resources research in Arizona. This file will be maintained on computer and be
available for research by keyword, drainage basin, and /or investigator. This
activity will build upon a project file compiled in 1976 for the Lower Colorado
River Basin, by the WRRC and OALS under the auspices of the U.S. Bureau of
Reclamation (USBR) and the Office of Water Research and Technology, U.S.
Department of the Interior.
Conferences and Educational Activities
In response to the growing need to better inform the general public as well
as selected groups in matters pertaining to water resources, the Arizona Water
Information Center will organize and carry out conferences and workshops on
water topics of current interest. The educational skills and facilities of the
University can be made available through the Center for a variety of informational activities. Seminars or roundtable discussions can be held either at the
University or throughout the state, to facilitate discussion and exchange of
information on water -related subjects.
Information aids such as audio /video tapes, slide presentations, or graphics have
been developed and can be used to illustrate specific topics such as water
allocation, groundwater contamination, rainfall harvesting or other areas of
current interest.
Computerized Hydrologic Information
This area of service is to provide computer terminal access to water data
and information systems housed in the various federal and state agencies, such as
the ADWR. The University now accesses by computer terminal several informa-
tion bases relevant to Arizona's water. These are the Department of Energy
RECON databases, which includes the Office of Water Research and Technology's
Selected Water Resources Abstracts and Project Information File, the American
Geological Institute's GEEOREF, U.S. Geological Survey's (USGS) WATSTORE,
U.S. Department of Agriculture's (USDA) CRIS, and Environmental Protection
Agency's STORET. Other files to be accessed include the Electric Power
Research Institute's Water Supply Computerized Information Directory.
On the state level, Arizona water data are stored primarily in the ADWR,
The Center will respond to inquiries for such data
either by referral to the appropriate agency, or by terminal access as described
USGS, and ADHS offices.
above.
OPERATION
A Water Information Coordinating Committee (WICC) will be established to
provide a linkage with other institutions actively involved with water research,
management, data collection, dissemination, and /or use. The function of the
committee will be to assure a close communication among existing operational
68
programs and agencies and to provide policy direction for center activities. The
committee will include, but not be limited to, university representation from the
appropriate colleges and /or research units; the ADWR, the ADHS and other state
agencies; the USGS, the USBR, and other federal agencies; and representatives of
the water users sectors, such as municipalities and industries. An annual rotating
chairman will be selected by the group. Staff support will be provided by the
Center.
The Center will be an operational unit of the University of Arizona, jointly
administered by the WRRC and OALS. A full-time manager will report to the
directors of the WRRC, OALS and the WICC. A water resources specialist will
act as a liaison between the Center, the Cooperative Extension Service, county
agents, community development specialists, and the entire range of water interest
groups. Personnel will include, in addition to the center manager, an editor,
secretary, and technical staff as needed. The Center manager will utilize faculty
from other departments or consultants on a negotiated basis, where analytical
research is needed. Ordinarily, personnel will be be housed in the Center for this
function. Center personnel will assist in responding to inquiries, referral to other
resources, and organization of workshops or special conferences.
69
AN EMPIRICAL EVALUATION
OF THE COSTS OF GROUNDWATER OVERDRAFT
David B. Bush and William E. Martin
Department of Agricultural Economics
University of Arizona
Tucson, Arizona 85721
Abstract
Estimates of the variable costs for groundwater pumping and
overdraft in Central Arizona are compared to the price of water
delivered via the Central Arizona Project (CAP). The respective
marginal costs of supplying irrigation water through each of the
two alternative sources are compared to the marginal demand for
water by farmers. Finally, the relative cost competitiveness of
groundwater versus CAP water is evaluated against a number of
alternative rates of energy cost escalation and groundwater
decline.
Introduction
Groundwater is being withdrawn from Arizona's aquifers far
more quickly than it can be replenished through natural or
induced recharge.
Annually the volume of overdraft amounts to an
average of about 2.2 million acre feet (Arizona Statistical
Rates of groundwater decline range from near zero
to five feet or more per year (U.S. Geological Survey, 1983). The
continuing dewatering of the aquifers upon which Arizonans depend
Review, 1984).
for two thirds or more of their water supply has resulted in
steadily rising groundwater pumping costs.
Water resource planners in the state have long sought means
to end the imbalance between the rate of groundwater extraction
and the rate of replenishment. With the authorization of the CAP
by Congress in 1968 and the passage in Arizona of a comprehensive
groundwater management code in 1980, efforts are under way to
eliminate groundwater- overdraft through both reducing the demand
for and augmenting the historic supply of water.
The purpose of the CAP is to alleviate a substantial portion
of the groundwater overdraft problem in Central Arizona by
introducing a large new supply of imported water to replace some
of the demand for mining the groundwater stock.
Beginning as
early as the end of 1985 and continuing for at least the next 50
years, an average of over a million acre feet of Colorado River
water will be imported annually to cities and farms in Central
Arizona.
71
By law, irrigators accepting delivery of project water must
use it as a substitute, not a supplemental supply. Every unit of
CAP water they use must take the place of an equal quantity of
groundwater no longer pumped.
Consequently the rate of
groundwater extraction and groundwater overdraft and decline is
expected to slow dramatically.
Shallower future pumping lifts
will hold the rate of future increases in the cost of groundwater
pumping to a lower level than there would have been otherwise.
It
is commonly believed that without such intervention to
forestall the deterioration of the groundwater supply, increasing
water scarcity will eventually drive irrigated agriculture and
the rest of the Arizona economy into decline.
One major agricultural
irrigation district
which has
contracted to receive CAP water, the Central Arizona Irrigation
District (CAIDD) is selected for a case analysis.
The CAIDD was organized in 1964 with the express purpose of
forming a public entity to contract for the delivery of Colorado
River water via the CAP. Located in western Pinal County, it
includes within its exterior boundaries approximately 144,000
acres centered about the town of Eloy.
Approximately 88,000
acres have a recent history of irrigation and are eligible for
irrigation and participation in the CAP under state and federal
guidelines (Arizona Department of Water Resources, 1983). The
CAIDD formally agreed to purchase CAP water in 1984.
Initial
deliveries are expected by 1989.
An Overview of Overdraft Cost Accounting
Typically the marginal cost of groundwater pumping in
Central Arizona ranges between approximately $30 and $50 per acre
foot. In 1984 the estimated marginal cost for CAP water was
If groundwater is generally
far less expensive than CAP water, on what grounds would a
rational economic decision maker choose to replace groundwater
between $63 and $67 per acre foot.
with CAP water?
Implicit in the popular view of Arizona's water
problems is the notion that the cost of groundwater overdraft is
so significant that, were it taken into account, the marginal
cost of groundwater would approach or exceed the marginal cost of
CAP water.
The intent of the following analysis is to quantify the
various costs associated with groundwater overdraft. If the
addition of these costs makes groundwater appear to he more
expensive than CAP water, then the CAP may be an economically
viable alternative to the continuation of groundwater overdraft
at its current rate.
If instead the contrary is found to be
true, then the CAP may not be a rational water supply alternative
to mining ground water stocks for the forseeahle future.
72
Land Subsidence
Subsidence in Central Arizona results from the dewatering of
deep underground aquifers, which causes the sediment beds to
compact and the overlying land to sink and crack.
McCauley
(1973) attempted to assess the annual costs of subsidence -
related damages in western Pinal County, an area totally
dependent upon a heavily overdrafted groundwater supply used
almost entirely by irrigated agriculture. Western Pinal County
has experienced some of the most severe groundwater overdraft
conditions in Arizona, and continues to receive a great deal of
publicity in the state over its subsidence- related problems.
McCauley's estimate of the .total annual cost of the repairs
for subsidence -related damages
to land, wells, irrigation
ditches, roads and transportation rights of way, and urban and
domestic structures, as summarized in Table 1, equals about
$0.50 per acre foot of overdraft per year in constant 1984
dollars. At the time of his research the annual rate of
groundwater decline in Western Pinal County averaged about five
feet per year. Assuming a simple linear relationship between the
rate of groundwater decline and the severity of subsidence
damages, the annual marginal cost of subsidence equals about
$0410 per acre foot of overdraft per foot of groundwater decline.
Water Quality
Frequently groundwater quality deteriorates as pumping lifts
decline.
However,
it is difficult to detect any clear
relationship between water quality and groundwater depth to lift.
Some of the lowest quality water used for irrigation in Central
Arizona comes from some of the shallowest wells.
While water
quality in the area is expected to fall over the coming years,
most if not all of that change is expected to result from the
importation of CAP water and not from deteriorating groundwater
supplies.
Boster (1977) reported that the estimated salinity of
CAP water will average about 940 parts per million (ppm) when
deliveries begin in J986. Locally the salinity may range from
400 ppm up to 1200 ppm, while the area -weighted average salinity
of groundwater in Pinal County is 670 ppm. It appears that the
replacement of groundwater with CAP water will improve water
quality for some users,
and lower it for others.
On the
average, water quality will probably he worse with the CAP and
not better.
For the sake of simplicity, all considerations of water
quality are excluded from the following analysis.
Since CAP
water will tend to be of a lower quality than groundwater,
overlooking this factor may bias the conclusions slightly in
favor of the CAP.
73
Table
1.
Estimated Total Annual Cost of Subsidence
in Western Pinal County
Agriculture
Land Leveling
Crack Repair
Well Repair
Ditch Repair
$60,000
60,000
57,250
10,000
$187,250
$187,250
Transportation
Highways
All Other
15,500
4,300
$19,800
19,800
Domestic and Urban
Structures
Total. $1970
$207,050/yr divided by:
$207,050
1,100,00 acre feet of groundwater used
annually in Pinal County in 1970
_
74
$0.1882 /af/yr in 1970
Table
1, continued.
Annual rate of groundwater
decline western Pinal County
in 1970
=
5 ft /yr
Annual cost of subsidence
per acre foot per foot of
groundwater decline
=
$0.1882 / af / 5 ft
_
$0.0376 / af / ft ($1970)
$1984 Price Index Multiplier
2.5632
$1984 Annual Cost per acre foot
=
=
($0.0376) * (2.5632)
$0.0965 / af / ft ($1984)
ANNUAL COST OF SUBSIDENCE
About 10 cents per acre foot per foot of
the current rate of groundwater decline,
75
Pumping Depth to Lift
The progressive increase in pumping depths to lift is by far
the single greatest cost associated with groundwater overdraft.
Not only are the additional costs potentially significant, but
the effect is both permanent and cumulative,.
A change of a
single foot in a pumping lift causes the pumping lifts in every
subsequent year to he at least one foot deeper than they would
have been otherwise. When the water table falls at a rate of a
foot per year, the additional costs mount steadily; a rate of
decline of one foot per year means that pumping lifts would be
one foot deeper than otherwise in the following year, two feet
deeper in the year after than, three in the year after that, and
so on.
Changes in groundwater pumping technology, the real cost of
capital and labor, and changes in the level of demand for
irrigation water may either mitigate or magnify the ultimate
effect of groundwater decline on the viability of irrigated
agriculture.
In order to isolate out the pure economic impact of
groundwater overdraft alone, it is assumed that all factors
except the unit cost of energy and the pumping depth to lift
remain constant.
The additional fixed and variable pumping costs associated
with a unit increase in pumping lifts may be represented by a
perpetual stream of equal annual payments.
The present worth of
a perpetual stream of additional costs may he determined by
dividing the increased annual cost by the given rate of interest.
Increased Variable Pumping Costs
Changes in variable
pumping costs over time are dependent upon two factors, the
energy cost of pumping and pump maintenance.
Both costs are
functions of the depth to lift.
An irrigation well operating at
100 percent efficiency would require 1.024 killowatt hours of
electricity in order to lift one acre foot of water one foot.
Assuming the average irrigation well in Central Arizona operates
at 54 percent efficiency, the amount of energy required to lift
one acre foot of water one foot is equal to 1.024 divided by
or about 1.896 kilowatt hours. The product of 1.896 and the
cost of electricity per kilowatt hour, multiplied by the pumping
depth to lift, will give the energy cost of recovering one acre
foot of water.
0.54,
The unit cost for electricity in the CAIDD is 25 mils, that
is, $0.025 per kilowatt hour. The unit cost for well maintenance
is estimated to be $0.011438 per acre foot per foot of lift.
The
pumping depth to lift is assumed to he 620 feet (Hathorn, 1984).
The estimated pumping cost in the CAIDD is therefore equal to
(1.896 * 0 .025 * 620 = $29.39 per acre foot, and the maintenance
cost to (620 * 0.011438) _ $7.09 per acre foot. The total
76
variable cost of pumping is the sum of the energy and maintenance
costs, or $29.39 + $7.09 = $36.48 per acre foot.
Suppose groundwater in the CAIDD were overdrafted to the
extent that as a result of current pumping activity the water
table fell one foot.
The additional variable cost of pumping
would he evaluated as the sum of the additional energy and
maintenance costs for one additional foot of lift, or
[(1.024/0.54) * $0-.025
+ $0.011438 = $0.058845.
Assuming a real
annual rate of interest of 4.0 percent, the present value of the
additional variable pumping costs associated with a decline in
pumping lifts of one foot would he equal $0.058845 divided by
0-.04,
or $1-.47 per acre foot of groundwater pumped.
A change in the price of energy would have a significant
impact on future variable pumping costs too.
Suppose that
groundwater conditions in the CAIDD remained static, but that the
real cost of energy increased by one percent, to $0.02525 per
kilowatt hour.
The total additional cost of groundwater pumping
would he equal to $0.2939 per acre foot, for a present value of
$7.35 per acre foot.
Increased Future Fixed Pumping Costs
As pumping lifts
increase, more piping, tubing, and bowls must be added to the
well structure in order to extend the reach of the pump as it
"chases "'the receding water table.
Eventually the burden of the
additional hardware may increase the pump's horsepower
requirements to the point where a larger motor is needed. A
larger motor may in turn require larger diameter tubes, shafts,
and the replacement of other parts. Assuming that well yield does
not change over time, the average fixed cost of pumping will
increase in proportion to the increase in the
capital investment.
size of the
In order'to trace the rate of increase of average fixed
pumping costs as pumping lifts increase, a number of different
hypothetical irrigation wells representing a variety of different
groundwater conditions in Central Arizona were "constructed."
Well specifications were derived from Hathorn's (1984) pumpwater
budgets and from conversations with the managers and engineers of
several different irrigation districts in the study area.. Once
the original well budgets were determined, each well was
"rebuilt" several times to reflect the necessary additional
investments each well would have to have in order to pump water
from successively deeper lifts.
Several dozen fixed cost estimates were thus arrived upon
and well
capacities from 800 to 1600 gallons per minute. Table 2 shows
the averaged fixed cost per acre foot of pumped groundwater at
various depths to lift. For what be called a "representative"
for pumping lifts ranging from 200 to 1000 feet,
77
Table 2.
Pump
Lift
Relationship of Average Fixed Groundwater Pumping
Costs in 1984 to Pumping Depth to Lift.
Various Estimated Fixed
Pumping Costs
(ft)
200
250
300
350
400
450
500
550
600
650
700
750
800
850
900
950
1000
1050
($ /af)
($ /af)
5.56
6.64
8y 96
8.03,
9.08,
9.35
9.82, 10.40
9.32, 10.62, 10.76
8.61, 9.94, 11.13,
8.31
9.10, 10.26, 11.69,
9.09
14.69, 14.11, 12:85, 10.63
12.08, 11.59, 15.142, 12.05
15.83,
9.20
10.38, 15.86, 13.19, 15.87
16.38
15.93,
16.68,
11.63,
13.72,
16.80,
11.90
17.145,
18.02,
18.14,
16.86
19.01
13.62,
16.88,
16.78,
17. 13,
14,:40,
16.06,
18.62
19.94,
Average
Fixed
Cost
"Fitted"
Average
Fixed
Cost
($ /af)
5=56
6.64
8.96
8.69
9.77
6.0
6.8
7.6
8.4
9.2
10.23
10.0
10.8
9.50
10.04
12.85
1 1. 6
14..34
13.2
12.4
13.31,
10.38
13.97,
17.77,
17.60,
12.13
14.13
14.0
13.85
11.46
18.33
14.54
14.8
15.81
15.6
14.141,
14.33
16.48
16.4
19.06
16.86
19.01
17.50
17.2
18.0
18.8
19.11
17-.50
19-.6
Overall, average fixed pumping costs appear to increase
at a rate of approximately 80 cents per acre -foot per 50 feet
of increased lift.
Sources:
Hathorn, Scott. Arizona Pumpwater Budgets.
Cooperative Extension Service, 1984.
Johnston Pump Company. Johnston Engineering
Data Book. # 751.
Personal Communications, Dr. Scott Hathorn, 1984. .
78
well, fixed costs are projected to increase at a rate of
approximately $0.80 per acre foot per additional 50 feet of
pumping lift, or $0.016 per acre foot per additional foot of
lift..
The present value of the stream of increased future costs
is equal to $0.40 per acre foot per foot of additional lift.
Marginal Social Cost
of Groundwater Pumping and Overdraft
Calculation of the Marginal Social Cost
The full marginal social cost (MSC) of groundwater pumping
and overdraft at any given rate of pumping is found by adding
together the private costs of groundwater pumping and the social
costs of groundwater overdraft.
Discussions with district authorities and examinations of
historic well records indicated that in
the CAIDD it is
reasonable to assume that between 1984 and 1985 the real cost of
energy will increase
percent, while the groundwater depth to
lift will increase three feet.
The MSC for groundwater pumping
in the CAIDD per acre foot is therefore equal to
1
MSC = private pumping costs + the costs of overdraft
_ $36..48 + additional variable pumping costs
+ additional fixed pumping costs
+ subsidence damage repair costs
_ $36.48 + [(3 * $1.47) + $7.35] + (3* $0.40) + (3 * $0.10)
=
$(36.48 + 13.26)
_
$49.74 per acre foot.
Marginal Social Cost of Groundwater Pumping and
Overdraft Compared to the Variable Cost of CAP Water
The marginal cost for delivering CAP water to the head of
the CAIDD's distribution system, were it delivered in 1984, would
have been about $57 per acre foot.
It is assumed that
approximately 10 percent of all CAP water delivered to the
district will be lost through seepage or evaporation before it
ever reaches the farm headgate.
Dividing the $57 by a delivery
efficiency factor of 0.90 yields an effective marginal cost for
CAP water of $63.33 per acre foot.
79
Even when all the significant costs of groundwater overdraft
in 1984 are taken into consideration, the variable cost of CAP
water would still be approximately $13.50 per acre foot more
than the variable cost of pumping groundwater.
Under reasonable
assumptions about the full social cost of groundwater pumping in
1984, it does not appear rational to substitute CAP water for
groundwater in order to control overdraft.
Demand for Irrigation Water
Holding the costs of all other variable factors of
production constant,
the short run production decision rule for
the use of irrigation water on any one crop is as follows. If
the cost of an additional unit of water is less than or equal to
the returns that it would earn on that crop in production, then
it would be worthwhile to obtain and use the additional unit of
water. The maximum amount that a farmer would pay for the water
is simply that sum which would exactly equal the return he could
expect to earn through employing that last productive unit.
Whenever a farmer has the opportunity to pay less than the
maximum amount he is willing to pay for water, he would be better
off using the water. He would then not only meet all of his
variable costs of production, but would also earn additional
revenue with which to help cover the fixed costs for the entire
farm.
If the farmer had to pay more than the maximum amount for
his water, all other factors held constant, then he would fail to
meet even the variable costs of production.
Under those
circumstances, the farmer would he better off by not using as
much water to produce the crop,
the crop at all.
or perhaps even by not producing
Figure 1 illustrates the approximate aggregate demand for
irrigation water for use in the production of major crops in
Pinal County. The demand for water was determined in the
following manner. For each crop a gross revenue per acre was
calculated on the ha,sis of average yields and unit crop prices.
All short run (variable) costs of production except the cost of
water were summed up and subtracted from the gross revenue total.
The remainder constituted the maximum amount that the farmer
could pay for water and still at least cover his variable costs
of production.
The maximum willingness to pay for water on the
marginal crop is a measure of the marginal willingness to pay,
that is, the demand for water.
The most significant marginal crop grown in the CAIDD is
wheat, with a marginal willingness to pay for water in 1984 of
about $55 per acre foot.
Clearly, at a marginal extraction cost
of $36.48 and a marginal social cost of $49.74
the typical
80
600
800
1000
Wheat i
Alfalfa
1984.
of Water.
1000's of
Acre Feet
Sorghum
__,,Barley
Safflower
Hathorn, Scott.
Arizona Field Crop Budgets, Pinal County.
1984.
Arizona Pecan Budgets, Pima and Pinal Counties-.
460
Cotton
Sources:
200
Pecans
Vegetables
Demand for Irrigation Water in Pinal County in 1984.
25-
50-
75
100
125
150-,.
Figure 1.
Value of
Last Unit
of Water
Added or
Deleted
farmer could and would afford to use groundwater to grow wheat.
This is not the case for CAP water, however. The marginal cost
of water from this source is far in excess of the marginal value
product of water for the production of wheat.
Costs and Returns to Irrigation Water in Pinal County:
Alternative Scenarios
Table 3 illustrates the marginal social cost of groundwater
pumping and overdraft for the CAIDD under 32 alternative
scenarios.
Each scenario represents a different possible
combination of increases in the unit cost of energy and the
pumping depth to lift between 1984 and 1985.
Given this matrix
of possible costs for groundwater, it is a simple matter to
determine the circumstances under which the cost of groundwater
would make irrigation cost prohibitive, and the circumstances
under which CAP water would be a suitable alternative to
groundwater mining.
The marginal value products for an acre foot of water on the
principle crops in Pinal County are $95.22 on cotton, $65.39 on
alfalfa, and $55.18 on wheat. At a unit cost of about $63, CAP
water could only become a competitive source of supply for
farmers in the CAIDD if the cost of groundwater were at least
that high. However, if the marginal cost of water were in fact
at that'level, wheat would be forced out of production and
alfalfa could he produced with only the barest margin of net
returns.
It appears that agriculture would already have to be in
decline due to the high cost of irrigation water,
which it
currently is not, in order for the CAP to be worthwhile.
Under what circumstances would the high cost of groundwater
threaten to drive significant acreage out of production and make
the CAP a suitable alternative?
In the absence of a real
increase in the unit cost of energy of at least 3 percent, there
are few cases in which the rate of groundwater decline could
become costly enough to make CAP water competive with
groundwater.
Meanwhile, most crop production would continue in
spite of the cost of-overdraft.
Of the three major crops examined, only wheat appears
susceptible to being taken out of production on a significant
scale if groundwater overdraft costs were significant. The demand
for irrigation water would probably not he reduced under any
reasonable set of assumptions about groundwater decline and
energy cost esalation. Demand for water to irrigate alfalfa
would remain unaffected except under severe energy cost and
groundwater decline conditions. Cotton
all.
82
would not he affected at
Table 3.
Marginal Social Cost of groundwater
pumping per acre foot in 1984, under
alternative energy cost
escalation
and groundwater decline scenarios,.
Central Arizona Irrigation District - Final County
Depth to lift
Cost of electricity
Marginal Private Pumping Cost
Marginal Cost of Cap Water
Marginal Value Product
of Water on
Upland Cotton
Alfalfa
Durham Wheat
Discount rate
620
25.00
36.48
63.33
=
=
=
=
feet
mils /Kwh
$/ of
$ /af
95.22 $ /af
65.39 $ /af
55.18 $ /af
=
=
=
4
percent
Energy Escalation Rate
(percent increase over inflation)
Groundwater
Decline (ft)
0
1
2
3
0
36.48
43.83
51.18
58.53
1
38.45
45.80
53.15
60.50
2
40.42
47.77
55.12
62:47
3
42.39
49.74
57.09
64.44
4
44,.36
51.71
59.06
66.41
5
46.33
53.68
61.03
68.38
8
52.24
59.59
66.94
74-.29
10
56.1T
63.53
70.88
78.23
Annual increased variable cost per foot of decline:
Annual increased fixed cost per foot of decline:
Subsidence cost per foot of decline:
$
Total increased annual costs per foot of decline:
$ 1.97
Total increased annual costs. per
percent real
increase in the unit cost of electricity:
$ 7.35
1.47
0.40
0.10
1
Conclusions
Declining groundwater tables have undoubtedly led to higher
pumping costs than there would have been in the absence of these
declines. That is not the same thing as saying, however, that
groundwater overdraft is a serious problem for farmers in Central
Arizona, or that it is worthwhile to replace some groundwater
mining with imported water via the CAP at the present time,
The marginal social costs of groundwater overdraft are
generally still too small to justify the conservation measures
that the state of Arizona wishes to encourage. The marginal cost
of CAP water is still too large relative to the marginal social
cost of groundwater pumping to justify its substitution for
groundwater.
The potential benefits of the CAP do not appear to
make the project an economically viable alternative
to
groundwater pumping in Central Arizona.
References
Arizona Agricultural Statistics-. Arizona Crop and Livestock
Reporting Service, Phoenix, Arizona.
1983 - 1984.
Arizona Department of Water Resources-.
Water Service
Organizations in Arizona.
Phoenix, Arizona.
August, 1983.
Arizona Statistical Review.
Planning Division, Phoenix,
Valley National Bank, Economic
Arizona.
40th Annual Edition,
September, 1984.
Arizona Water Commission. Phase I Arizona State Water Plan:
Investory of Resource and Uses July, 1975.
Bookman-EdmonstonEngineering, Inc.
Central Arizona Irrigation
and Drainage District..
An Addendum to Engineering Report in
Support of Application for Federal Loan Under Public Law 130 for
Construction of an Irrigation Distribution System. Phoenix,
Arizona,.
December, 4982.
Boster, Mark A. and William E-. Martin.
"Economic Analysis of the
Conjunctive Use of Surface Water and Ground Water of Differing
Prices and Qualities:
Technical Bulletin No
University of Arizona.
A Coming Problem for Arizona Agriculture."
235.
1977.
Agricultural Experiment Station,
Bush, David. Costs and Returns to Irrigation Under the Central
Arizona Project:
Alternative Futures for Agriculture.
Unpublished M.S. Thesis, Department of Agricultural Economics,
University of Arizona, Tucson, 1984.
84
Hathorn, Scott.
Arizona Field Crop Budgets. Final County.
Cooperative Extension Service, College of Agriculture, University
of Arizona, Tucson, Arizona.
1984.
Hathorn, Scott,
Arizona Pecan Budgets. Pima and Final Counties.
Cooperative Extension Service, College of Agriculture, University
of Arizona, Tucson, Arizona.
1984.
Hathorn, Scott.
Arizona gumgwater $ udgets. Final County.
Cooperative Extension Service, College of Agriculture, University
of Arizona, Tucson, Arizona.
1984.
Johnston Engineering Data Book. #751.
Glendora, California.
Undated.
Johnston Pump Company,
Water
Kelso, Maurice M., William E. Martin and Lawrence E. Mack.
Supplies and Economic Growth in an Arid Environment:
An Arizona
University of Arizona Press, 1973..
Case Study.
McCauley, Charley.
Management of Subsiding of Subsiding Lands:
an Economic Evaluation.
Unpublished Doctoral Dissertation,
University of Arizona, 1973.
U.S-. Geological Survey.
Unpublished file on historical well data
and depths to water in selected areas of Central Arizona,
Tucson, Arizona, 1983.
.
85
ECONOMIC FEASIBILITY OF ARTIFICIAL RECHARGE AND RECOVERY OF
IMPORTED WATER IN BUTLER VALLEY, ARIZONA
J. M. Abe and B. C. Saliba
Department of Hydrology and Water Resources
Department of Agricultural Economics
University of Arizona, Tucson, Arizona
ABSTRACT
Artificial recharge of ground -water basins is a viable means
supplementing existing surface storage facilities. Benefits attributed
of
to
underground storage (as contrasted with reservoir storage) of excess surface
water include reduced evaporative losses, reduced water quality degradation,
increased
security
against
uncertainty
of
streamflows
inexpensive storage with minor environmental impact.
and
potentially
Butler Valley, one of
several alluvial basins adjacent to the Central Arizona Project aqueduct, is
being investigated by the Water Resources Research Center (University of
Arizona, Tucson) to determine the feasibility of artificially recharging excess
surface water into the valley aquifer.
This paper presents the current economic investigation of the Butler Valley
Project given available technical and institutional information. The purpose of
the paper is to present an economic overview of the problem, describe current
research, and suggest future research methodology. Review of literature
evaluating the economic feasibility of recharge projects and discussion of
specific economic problems arising from this project will be central themes of
this paper.
INTRODUCTION
Among several policies to secure Arizona's future water supply, conjunctive
management of surface water and ground water offers considerable flexibility to
existing water facilities. A multidisciplinary research team from the Water
Resources Research Center (University of Arizona, Tucson) is examining the
technical, institutional and economic elements affecting the artificial
recharge /recovery of imported water using the Butler Valley aquifer. Three
potential sources of water are the Central Arizona Project (CAP) aqueduct, the
Bill Williams River and natural surface runoff within the Valley. To simplify the
current economic research , the CAP aqueduct is considered the sole source.
The feasibility of the other two sources will be determined as institutional
constraints become better defined.
The paper includes an overview of the Butler Valley Project, a review of
literature evaluating the economic feasibility of recharge projects, and a
summary of on -going economic research. The physical and institutional setting
of Butler Valley provides an interesting case study of underground storage of
87
water in Arizona. An important function of this paper is to develop an analytic
framework in which all costs and benefits to society can be adequately
assessed.
OVERVIEW OF BUTLER VALLEY PROJECT
Physical Setting
Butler Valley is a flat alluvial basin located in west -central Arizona
aproximately 40 miles southeast of Parker, Arizona (Figure 1). The basin,
encompassing about 160 square miles, lies between the Bill Williams River and
the CAP Granite Reef Aqueduct. The upper end of the Valley is about four
miles south of the Alamo Reservoir on the Bill Williams River, while the lower
end is less than a mile up gradient from the Cunningham Wash siphon of the
CAP aqueduct.
Determining
the
technical feasibility of a recharge
project requires
evaluation of the general hydrogeologic characteristics of the ground -water
basin. Gravity and seismic surveys were used to delineate the basin's perimeter
and bottom. The boundary between permeable and impermeable rocks shown in
Figure 1 also marks the boundary of the alluvial ground -water system. The
average saturated thickness of the aquifer above the bedrock is about 700 to
800 feet. Outflow of ground water through the notch in the southwestern edge
of the basin appears to be neglible indicating that the aquifer is a closed
hydrologic system.
Technical studies evaluating storage and transmissive properties of the
valley aquifer include evaluation of geologic and geophysical logs, performance
of aquifer tests and synthesis of water level and other pertinent data from
previous reports. At the current static water level of about 1275 feet above
mean sea level (the water level is relatively flat), the quantity of ground water
in storage is estimated to be about 12 million acre -feet. Assuming a drawdown
of 200 feet from the current static water level, the amount of recoverable
ground water is about 1 to 1.5 million acre -feet. Artificial recharge of water
into the basin was simulated with a computer ground -water model. The model
indicates between 200,000 - 600,000 acre -feet of storage exists above the
current water level (assumed to be the natural steady -state level). Total storage
between the anticipated water levels considered in this report (elevation 1075 to
1350 feet) is about 2 million acre -feet.
Institutional Setting
The institutional side of the Butler Valley Project
is
both dynamic and
complex. Unresolved institutional problems include:
(1)
granting authority to the Central Arizona Water Conservation
District (CAWCD) to recharge municipal allotments of CAP water
into Butler Valley.
88
BOUSE
HILLS
Scale:
b
,Miles
F
A
A
1/4-4
c,pq
-i }'rF
14//
Bi/! Williams River
*,qsy
BUTLER VALLEY
Phoenix
Parker
CAP GRANITE REEF
CA P
AQUEDUCT
AQUEDUCT
Tucson
EXPL ANAT /ON
T T T BOUNDARY BETWEEN PERMEABLE AND IMPERMEABLE ROCKS
Figure 1:
Map showing physical boundaries of Butler Valley; inset
map showing location in Arizona.
89
I
5t
(2) needed amendments to the 1980 Groundwater Management Act to
sanction and protect underground storage of water
interaction of water and electric power: debate over
(3)
apportionment of electric power between the CAWCD and two
electric power utilities - Arizona Public Service (APS) and Salt
River Project (SRP)
conflict between municipalities
and agricultural groups over
availability of recharge water.
(5) legality of providing subsidized power rates to a private operator
of recharge proejct
(6) role of Federal government in recharge projects
(4)
In order for the economic analysis to proceed, a recharge project is assumed
to be institutionally feasible. As information becomes available in these problem
areas, the economic analysis will incorporate institutional constraints
accordingly.
Direction of Economic Analysis
The economic feasibility of a recharge project in Butler Valley is dependent
on technical and institutional factors. At the present phase of cost estimation,
technical constraints bear more weight in the analysis. As economic research
progresses into social impact analysis, institutional constraints will count as
heavily as technical constraints.
The following research objectives have been defined for the economic
research component of the Butler Valley study:
(1)
description of potential project scenarios in terms of costs and
scale
(2) definition of studies scope - those regions and people affected by
the project
(3) identification of data sources
(4) determine constraints affecting the project
(5) determine the best means to compare alternative recharge scenarios
(6) decide the direction and magnitude of social impact analysis
(7) determine the appropriate discount rate and time horizon
Review of Literature
Overview
Although technical material on artificial recharge is abundant, those
articles and reports dealing specifically with the economic feasibility of
recharge projects are few in number. An annotated bibliography of literature
evaluating the economic feasibility of recharge projects is being compiled
continuously as new material is uncovered. Some important material discovered
in the literature search include California Department of Water Resources
90
(1983), Wilson (1979), Nebraska Water Resources Institute (1975), Frankel (1967)
Harpaz (1970) Mawer and O'Kane (1970) and Saunders (1967). Conjunctive
management of surface water and ground water is tied intricately with other
resources (energy, land, etc.) and potential water uses (agricultural, municipal,
industrial, etc.) Literature dealing with these issues, water valuation, and water
transfers include Jeske, et al. (1980), Kelso, et al. (1973), and Hartman and
Seastone (1970).
Economic Criteria Used in Evaluating Recharge Projects
Is recharge project needed?
Water
project development consists of
the following five stages:
(1)
reconnaissance report, (2) feasibility report, (3) final design and preparation of
contract documents, (4) construction and (5) operation (Goodman, 1984). The
Butler Valley Project is currently at stage 2. Whether or not a water project
should be implemented is determined at this level. A frequent error in water
planning and management is to project water demand as a function of population
growth exclusively. The planner assumes a constant water use per capita and
builds a supply system to meet extrapolated water demand (Kindler and Russel,
1984). An expensive water project is built which ultimately leads to higher
water rates. The consumer responds to higher water rates by using less water.
The net result: water supply development before it is needed because planners
failed to consider the effect of changing water rates on demand.
The relatively low value placed on water has lead to wasteful use even in
an arid environment. Kelso, et al. (1973) refer to the water valuation problem in
Arizona in the following passage: "the Arizona water problem is more a problem
of the lack of man -made institutions (policies) for developing and transferring
water than a problem of physically short supplies." These authors suggest that
water should be dealt with as an economically scarce resource. A pricing
scheme should be developed to exclude the lowest -valued uses. Furthermore,
Kindler and Russel (1984) draw a distinction between water "demand" and
"requirement ".
Requirement refers to those water users unaffected by price
changes (price inelastic). These uses would include all activities necessary for
sustaining life - a relatively small portion of total water use. Water demand,
which is affected by price changes (price elastic), represents the remaining
much larger portion of water use. Modification of water demand through water
pricing strategies, as suggested by these authors, is gaining popularity as an
alternative to supply augmentation. Reasons for increased popularity include
expense of water projects, scarcity of available sites, increasing energy costs,
water transfer disputes and decreasing federal involvement (cost- sharing with
state and local governments).
While reducing water demand can postpone water supply development, it is
only one of several policies needed to satisfy Arizona's future water needs.
Arguments favoring pricing strategies as suggested by Kelso, et al. (1973) and
Kindler and Russel (1984) assume water to be a market good. In Arizona, the
current political atmosphere favors a perception of water as a public good. The
91
passage of the 1980 Groundwater Management Act and recall of Tucson water
officials after a rate increase (Martin, et al., 1984) demonstrate an unwillingness
to allow market forces to fully control water use. Persuasive measures such as
the "Beat the Peak" ads are the preferred method to lower water demand.
Given the reluctance to adopt pricing strategies, supply augmentation will
continue to play a significant role.
The economic analysis of the Butler Valley Project will compare several
recharge schemes with the baseline scenario of no recharge project. The next
three sections provide an overview of general costs and benefits of artificial
recharge and point out relevant items affecting the Butler Valley Project.
Benefits of artificial recharge
Harpaz (1970) listed the following benefits of artificial recharge:
(1) store excess water outputs in anticipation of dry periods.
(2) reduce overdraft and water -table decline
(3) establish gradients to prevent intrusion of low- quality waters
(4) minimize loss of relatively shallow wells
(5) maintain or improve existing aquifer quality
(6) dispose of and /or purify wastewater
(7) store water locally for conjunctive operation and use in emergency
(8) minimize excessive evaporative loss from impounded water
(9) minimize excessive biological growth
(10) reduce subsidence
(11) reduce dangers inherent in surface impoundment of water
(12) replace natural recharge lost through increasing urbanization and /or
lining of channels
Benefits 1,7,8,9 and ll apply to the Butler Valley Project. Seasonal and
long -term storage of excess surface water are the primary project objectives.
Benefits 8, 9 and ll are due to the intrinsic advantages of underground storage
over surface storage. These advantages include: reduction of evaporative losses,
elimination of flooding of land behind reservoirs, elimination of risk due to
potential dam failure and savings in treatment costs. Associated cost savings
include minimal land purchases, reduced insurance premiums, elimination of
construction and operation costs of treatment facilities and reduced
expenditures for environmental impact studies.
Additional benefits not mentioned explicitly by Harpaz (1970) include flood
control, continued land use above storage facilities and potential exploitation of
existing ground -water reserves. Although the Butler Valley Project would allow
for storage of floodwaters, mitigation of floods in nearby streams is negligible
due to the time and power requirements for diverting water into the Valley.
Significant flood mitigation requires diversion (under gravity flow) and spreading
of large volumes of water within short time periods (Nebraska Water Resources
Institute, 1975). Recharge of water into Butler Valley is limited by (but not
exclusively) pumping expense and distance from diversion point to recharge site.
92
Artificial recharge offers significant advantages in urban and coastal areas.
In addition to continued land use (often high value), underground storage allows
treated wastes and surface runoff to replenish ground -water resources (Cohen
and Durfor, 1967). Prevention of salt -water intrusion is a considerable benefit in
coastal areas. Orcutt (1967) mentions the aesthetic quality of recharge ponds on
parks and golf courses. In urban areas largely dependent on ground water
(Tucson, AZ and Long Island, N.Y.), the underlying aquifer also acts as a
conduit for distributing water to users. Additional benefits realized in populated
areas include reduction of ground -water overdraft, pumping costs and well
replacement costs.
Butler Valley, a remote valley in Arizona, lacks the previous benefits
attributed to urban and coastal areas. Land use in Butler Valley is limited to
minor agricultural operations. However, an artificial recharge project provides
an option of using the land to some extent in the future. In addition,
environmental impact to plants and animals in the Valley appears to be minimal
when compared to surface storage facilities.
Existing ground -water reserves pumped from Butler Valley can be used to
augment surface water supplies. Irregular timing of "wet" and "dry" events
require a recharge facility to handle storage of large quantities of water in
short time periods. Removal of existing ground water also creates storage for
recharge during "wet" periods and thus improves the efficiency of the recharge
operation.
Costs of artificial recharge
Factors contributing to the cost of artificial recharge include:
(1) quantity of recharge water
(2) quality of recharge water
(3) recharge method
(4) distance from diversion point to recharge site
(5) energy requirements and available power rates
(6) land requirements and value of land
(7) type of operator: public vs. private
(8) hydrogeologic characteristics of aquifer
(9) institutional constraints
(10) opportunity costs of storing water
Given the technical and institutional considerations listed above, selection of
project scenarios should attempt to provide insight into all feasible recharge
designs. The number of scenarios should be kept at a minimum. The physical
and institutional setting of Butler Valley provides an excellent opportunity to
evaluate alternative recharge designs as well as examine the effects of
exogenous institutional and socioeconomic factors.
The interdependence of water and other resources requires evaluation of
93
opportunity costs associated with water projects. Buras (1982) discusses the
interaction of water and energy in Arizona. As mentioned earlier, debate over
apportionment of electric power may pose a considerable constraint to the
Project. Estimates of pumping costs may vary dramatically due to the range in
power rates used in calculation. To adequately evaluate costs, potential benefits
attributed to alternative power consumption should be assessed to some degree
as opportunity costs to the Project.
Land, another resource closely related to water, frequently influences the
cost of a water project. The opportunity cost associated with surface storage
sites is often quite high. These costs include flooding of agricultural land,
impact on wildlife, and lost scenic beauty along rivers. Opportunity cost of land
for an artificial recharge project such as Butler Valley seems to be minimal.
Additional opportunity costs of recharge projects include materials and
equipment, human capital, and financing. Examples of materials and equipment
are concrete, steel, plastics, bulldozers, drilling rigs, etc. Human capital
expended during project development includes labor, engineers, scientists,
lawyers, managers and other supporting staff. Finally, financing a recharge
project might tie up funds that could be used for services more beneficial to
society.
Do benefits of storing water exceed costs of artificial recharge?
After identifying all costs and benefits of a recharge project, monetary
values should be assigned to as many items as possible. A cost -benefit analysis
requires conversion of the time stream of benefits and costs to equivalent
present values (Sassone and Schaffer, 1978). The net present value (NPV =
present value of benefits - costs) is calculated for all project scenarios. Only
projects yielding NPV greater than or close to zero should be considered
economically desirable. Among several projects yielding positive net benefits,
the one with the highest NPV should be chosen.
The discount rate (r) and time horizon (T) chosen can greatly affect the
calculation of NPV. For this reason, a sensitivity analysis is normally run to
assess the impact on NPV of varying rates and time horizons. A range of NPV's
for corresponding discount rates and time horizons is usually presented in the
final results.
The
social
impact
component
distributional effects of the project.
of a cost -benefit analysis
These items might include:
addresses
(1) equity - distribution of costs and benefits among water using groups
(2) regional effects - impact of project on surrounding region
(3) effects difficult to evaluate in monetary terms
(4) uncertainty of supply and demand
The concept of "option value" seems appropriate in assessing benefits and /or
costs from an artificial recharge project (Bishop, 1982). Option value may be
94
defined as the value of assuring access to a resource when future use is
uncertain. Two conditions making option value relevant in resource evaluation
are irreversible changes and incomplete information. At least in the short run,
both conditions seem to prevail in Butler Valley. Uncertainly in future supply
(i.e. stream flows of the Colorado) and demand (i.e. future value of water)
suggest a severe lack of information. Two examples of irreversible (or at least
costly to reverse) changes in Butler Valley include: (1) water not stored but
needed later and (2) building an expensive recharge project when one is not
needed. Assessing the option value of resources involved in project development
may be important in adequately determining economic feasibility.
SUMMARY OF CURRENT RESEARCH
Given available technical and institutional information, current economic
research involves selection of recharge scenarios and estimating engineering
costs. Three hydrogeologic factors affected the selection of recharge scenarios:
(1) depth to water, (2) presence of confining beds and (3) confined vs.
unconfined conditions. Several simplifying assumptions made in recharge system
design include:
(1) annual artificial recharge equals 100,000 acre -feet
(2) all injection wells are identically designed; each with a capacity of
1000 gpm
(3) all recovery wells are identically designed; each with a capacity of
2000 gpm
(4) well spacing: four wells per square mile
(5) recharge capacity via spreading is directly proportional to
spreading area
(6) distribution system costs are estimated using reasonable engineering
design
Five scenarios were selected for the cost -benefit analysis:
(1) PLAN A: spreading basins /recovery wells
(2) PLAN B: injection /recovery wells (moderate storage capacity)
(3) PLAN C: spreading basins and injection wells /recovery wells
(4) PLAN D: injection /recovery wells (high storage capacity)
(5) PLAN E: baseline scenario - no recharge project
Engineering costs for Plans A -D are currently being calculated. See Figure 2 for
design of each plan. Engineering costs include construction of diversion,
distribution and recharge facilities, energy requirements to recharge and
recover water, construction of treatment facilities (to prevent clogging of
injection wells) and miscellaneous operation and maintenance.
CONCLUSION
Review of literature evaluating artificial recharge projects helped define
95
g.
LTTT r<
CAP GRANITE AEEP
AQUEDUCT
CAP GRANITE REEF
AQUEDUCT
EXPLANATION
EXPLANATION
TT? OQUN04A7 BETWEEN PERMEABLE AND IMPERMEABLE ROCKS
555 BOVNOART BETWEEN PERMEABLE ANO IMPERMEABLE ROCKS
X
O INJECTION WELLS
RECOVERY WELLS
INJECTION /RECOVERY WELL
SPREADING AREA
PLAN B:
PLAN A: SPREADING BASINS/ RECOVERY WELLS
INJECTION /RECOVERY WELLS
(MODERATE STORAGE CAPACITY)
EA
+
p-
}
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CAP GRANITE REEF
AQVEDUCT
EXPLANATION
EXPLANATION
TT T BOUNDARY OETAEEN vERMEAGLE ANO IMPERMEASLE ROCKS
T T T BOUNOARY OETwEEN PERMEABLE ANO IMPERMEAOLE ROCKS
o INJECTION WELLS
XL
O
RECOVERY WELL!
INJECTION WELLS
INJECTION/ RECOVERY WELLS
INJECTION / RECOVERY WELLS
SPREADING AREA
PLAN C:
SPREADING BASINS AND INJECTION WELLS /
RECOVERY WELLS
Figure 2:
PLAN D:
INJECTION / RECOVERY WELLS
(HIGH STORAGE CAPACITY )
Configurations of Plans A -D
96
the economic problem and identify the best research approach.
An initial
comparison of the selected projects will involve calculation of annual costs for
each alternative. The annual cost approach is equivalent to the net present
value calculation. Dividing these annual costs by 100,000 acre -feet /year will
provide an estimate of annual cost /acre -feet of artificially recharged /recovered
water. As the analysis progresses, benefits and additional costs will be
identified. Monetary values will be assigned to these items whenever possible.
Social impact analysis will provide insight into distributional effects that are
not easily evaluated in monetary terms.
Two important economic concepts to be addressed in this study are
opportunity cost and option value. The opportunity costs of water and other
resources used in the Project (energy, land, etc.) should be included in the
economic analysis. Although these items have been identified, the method of
evaluation remains to be determined. The option value of a recharge project
may alter the conclusions considerably. Standard cost- benefit analyses often
neglect this important aspect of project evaluation. To adequately assess all
benefits and costs of a recharge project in Butler Valley, both opportunity costs
and option value should be included with direct benefits and engineering costs.
References Cited
Bishop, R. 1982. Option value: an exposition and extension. In: Land Economics,
Vol. 58, No. 1.
N.
1982. Energy and water resources interactions in Arizona. In:
Hydrology and Water Resources in Arizona and the Southwest. Proceedings
from 1982 meetings of Arizona -Nevada Academy of Science.
Buras,
California Department of Water Resources.
storage program: final report.
1983. Chino Basin groundwater
Cohen, P. and C. Durfor.
1967. Artificial- recharge experiments utilizing
renovated sewage -plant effluent - feasibility study at Bay Park, New York,
U.S.A. In: Artificial recharge and management of aquifers: Symposium of
Haifia, International Association of Scientific Hydrology, Pub. No. 72
Frankel, R. 1967. Economics of artificial recharge for municipal water supply.
In: Artificial recharge and management of aquifers: Symposium of Haifia,
International Association of Scientific Hydrology, Pub. No. 72.
Goodman,
A.
1984.
Principles of Water Resources Planning.
New York:
Prentice -Hall, Inc.
Harpaz, Y. 1970. Practical experiences of well recharge. Artificial Groundwater
Research Conference, Paper No. 12, University of Reading, England.
Hartman, L. and D. Seastone. 1970. Water Transfers: Economic Efficiency and
97
Alternative Institutions. Baltimore, Maryland: Johns Hopkins Press.
Jeske, W. (ed.). 1980. Economics, Ethics, Ecology: Roots of Productive
Conservation. Published by Soil Conservation Society of America.
Kelso, M., et al. 1973. Water Supplies and Economic Growth in an Arid
Environment: An Arizona Case Study. Tucson, AZ: University of Arizona
Press.
Kindler, J. and C. Russel. 1984. Modelling Water Demands. London, England:
Academic Press.
Mawer, P. and J. O'Kane. 1970. Economic feasibility of artificial recharge.
Artificial Groundwater Recharge Conference, Paper No. 2, University of
Reading, England.
Nebraska Water Resources Institute. 1975. A cost -benefit presentation of
several
artificial recharge schemes in the Upper Big Blue River Basin:
final report.
Orcutt, R. 1967. An engineering- economic analysis of systems utilizing aquifer
storage for the irrigation of parks and golf courses with reclaimed waste
water. Desert Resources Institute Report HW -5, Nevada University.
Saunders, B. 1967. A procedure for determining the feasibility of planned
conjunctive management of surface and ground water. Utah State University.
Sassone, P. and W. Schaffer. 1978. Cost -Benefit Analysis: A Handbook. London,
England: Academic Press.
Scalmanini, J. and V. Scott. 1979. Design and operational criteria for artificial
ground -water recharge facilities. University of California, Davis.
Wilson, L.
1979. Artificial ground -water recharge: a review of methods and
problems. Water Resources Research Center, University of
Arizona.
98
EVALUATING THE EFFECTIVENESS OF
CURTAIN WELLS AGAINST SUBSURFACE
FLOODING IN YUMA, ARIZONA
by
Don W. Young and Earl E. Burnettl
ABSTRACT
During 1983 the Colorado River experienced high flow conditions due to abnormally
high snowmelt and runoff from the upper basin states.
Flood stages provided sufficient hydraulic head to force water under the protective levee system around Yuma,
Arizona.
This caused inundation of adjacent urban and agricultural lands from the
subsequent rise in the local water table. A methodology was planned whereby a series
of "curtain wells" would be installed and the water table pumped down to below grade
conditions.
The feasibility of accomplishing this was studied by retrofitting an
existing agricultural well located on "Yuma Island ", and installing a series of monitor wells in a two -dimensional array perpendicular and tangential to the levee. The
production well was pumped continuously for eight weeks, and the subsequent drawdown
within the piezometers monitored.
Based on the data gathered, it was possible to
predict the effectiveness of the proposed curtain well system.
INTRODUCTION
The City of Yuma, located in the extreme southwestern portion of Arizona (See Figure
1), experienced flooding conditions during 1983 due to a rise in the local water
Flood stage conditions on the Colorado River were caused by abnormally high
table.
winter snowmelt and spring rainfall within the upper basin region (See Figure 2).
The higher than normal river flow had two major effects on the river itself, and on
the adjacent lands.
One, scour occurred dropping the river bed as much as nineteen
feet (19 feet) in the vicinity of Yuma; and two, the increased hydraulic head forced
river water out into the hydraulically connected local aquifer causing the water
table to rise to very near, and even above the land surface outside the protective
levee system (See Figure 3).
Considerable damage was caused to urban and agricultural lands in the Yuma area due
to the rise in water levels.
Internal and external loading on the city's sewage
system caused numerous ruptures of the sewer pipes.
Leach fields became inoperative
there
was
fear
and
of
septic
tanks
contaminating the
groundwater
aquifer.
Considerable structural damage occurred to foundations and settling of some buildings
was reported.
1The authors are, respectively, Senior Hydrologist, Arizona State Land Department
1624 West Adams, Phoenix, Arizona 85007, and Chief, Geology and Groundwater Branch,
U.S. Bureau of Reclamation, Yuma Projects Office, Yuma, Arizona 85365.
99
FIGURE
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DURING HIGH FLOWS
MOVIMENT OF RIVER RECHARGE 10 GROUND WATER
Thousands
of
agricultural
acres
were
inundated,
with
subsequent
immediate
crop
losses, and future salt loading problems.
Flooded agricultural lands became ideal
breeding habitat for disease carrying mosquitoes and other water borne insects. The
threat of encephalitis to animals and man was potentially very great.
A benefit of
the flooding, if it could be said there was one, was to the aquatic bird and fish
habitat which flourished in the marsh -like conditions.
Upon the onset of the flood, the YUMA FLOOD CONTROL TASK FORCE was formed, which was
comprised of local city and county representatives, federal and state agencies which
had expressed interest in the problem and /or could lend technical expertise toward
finding a solution, and other public and private entities having interest and /or
jurisdictional concerns in formulating a solution (See Appendix A).
The results of the TASK FORCE study indicated that the best possible solution to the
flooding problem would be to install a series of "curtain" wells (sometimes referred
to as "barrier ", "cut -off" or "dewatering" wells).
These wells would be drilled,
ideally at strategic locations on the land side of the protective levee system and
pumped continuously, discharging water back to the Colorado River, thereby effecting
a drawdown of the water table.
What was uncertain at the time, however, was whether the proposed wells could indeed
get ahead of the subsurface inundation.
Little was known, however, of the rate of
flux of river water into the aquifer.
It became immediately evident that an ex ante
evaluation of the potential effectiveness of the curtain wells was required before
monies could be committed for well installation.
A project was undertaken jointly between the Arizona State Land Department (ASLD) Hydrology Section and the Yuma Projects Office of the Bureau of Reclamation (USBR) to
study these questions and make recommendations to the Task Force as to the feasibility of proceeding with installation of the curtain wells. This paper describes the
methodology used, and the results of that investigation.
METHODOLOGY
Retrofitting of Auza Well
A field reconnaissance was made by ASLD and USBR Staff personnel to locate potential
well sites which could be utilized for test purposes.
Several criteria were used in
evaluating each site; 1) proximity of well to levee, 2) depth of well and where perforated, 3) general age and condition of well, 4) time constraints due to commitment
to other uses, 5) ownership of well and apurtenant property, 6) projected retrofit
and operating costs
costs
and,
contractural
7)
arrangements between well
and
landowner(s).
Of the eleven (11) original sites that were identified, one site appeared to lend
itself ideally to the project.
This was an agricultural well located on State of
Arizona leased land within what is known as "Yuma Island" (or as "Fantasy Island" or
"No Man's Land" in some references) (See Figure 4).
The well was named after the
State's agricultural lessee, Pete Auza, who currently farms the surrounding acreage.
103
AIJZA HLL TEST SITE
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104
VALLEY
The vertical turbine Auza well was determined to have been installed in about 1951 to
depth of 200 feet, fully cased and perforated between 115 -160 feet (personal
a
conversation with Pete Auza, October 1983).
Unfortunately, the local stratigraphy
was not well delineated (no drillers log was available), and it was impossible to
know from what depths the greatest influx of river water was occurring, although it
was thought to be within the 25 -200 foot range.
The existing power plant (diesel) was not adaptable due to its cooling system, so the
BOR installed a portable 190 h.p. water cooled diesel engine.
A replacement pump
head was also installed on the existing CASCADE vertical turbine assembly.
After
installation, a short duration pump test was made.
A portion of the existing discharge pipe was cut away and a new twelve (12) inch PVC
discharge line laid from the pump, through the levee, to the river.
A meter
(McCrometer) was installed within this line and the pump discharge calibrated using
the meter, and also by the drop test method.
Discharge (Q) was calculated to be 3938
(average) gallons per minute (gpm).
Installation of Monitor Well Piezometers
Two
lines
of piezometer were
installed --one
line west and one line north of the
pumped well.
Sixteen piezometers in all were placed to depths of 5 feet (seven
wells), 20 -30 feet (six wells), and 140 feet (two wells). The five - footers were hand
driven.
The 20 -30 footers were power augered, and the two 140 - footers were drilled
with a mud rotary rig into the coarse gravel zone.
Each piezometer well was
completed with 12 -inch diameter galvanized pipe with 14"x18" galvanized or 12 "x24"
stainless steel well points (see Figure 5).
AREA GEOLOGY
The Auza Well is located near the head of the Colorado River Delta.
Typically, the
Yuma area deltaic deposits consist of loose, saturated deposits of sand, silt, clay
and gravel.
A log of one of the two 140 feet deep observation wells indicated a
stratigraphic sequence that is believed to be typical of the area (Appendix C).
This
sequence consists of an upper fine -grained layer (mostly clean sand with lenses of
clay or silt) about 100 feet thick underlain by a highly permeable layer of fine to
coarse rounded gravels and cobbles mixed with sand about 50 foot thick.
Normally
this so- called "Coarse Gravel Aquifer" is underlain by predominately sand with occasional
lenses of clay or minor gravel deposits up to several
thousand feet in
thickness. At the Auza site this lower sand zone (or Wedge Zone) is probably no more
than a few hundred feet thick before encountering either Bouse Formation marine clays
or bedrock of igneous or metamorphic type.
PUMP TEST
The pump test was begun on October 4, 1983 at 1020 MST and ended on December 2, 1983
at 1500 MST, for a total of sixty (60) days.
Calculated flow ranged from 4540 gpm to
3938 gpm.
This variability was due to adjustments in the engine speed during the
early days of the test.
The average pumpage rate stabilized close to 3938 gpm for
the major portion of the test period, and this is the figure used for computational
purposes.
Based on this figure, about 1000 acre feet (af) of water were pumped over
the duration of the test.
105
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106
Piezometer Drawdown
Each monitor piezometer was sounded immediately before start up of the
After start up, sounding intervals began at every one (1) hour for the
(8) hours; thereafter every three (3) hours for the next ninety -six (96)
periodicty of reading varied increasingly from then on as changes in
became incrementally less and less over time.
Auza well
first eight
hours.
The
water level
Figures 6 and 7 show the drawdowns of the shallow piezometers. The drawdown amount
and time to equilibrium was about the same as for recovery. The two 140 deep wells
had not been installed at the beginning of pumping.
It is important to note that the
river level was declining at a rate of about 3 feet per month during the test. This
decline rate when superimposed on the drawdown hydrographs of the piezometer allows
the drawdown produced solely by the pumping of the well to be derived.
Piezometer
3W -20 yielded the clearest drawdown record, including a recovery period in the second
week of October when the well was temporarily off.
This well showed that about one
week was required to produce an equilibrium drawdown of 1.1 feet.
Piezometer Recovery /Transmissivity
After 60 days of nearly continuous pumping, the well was turned off at 1500 MST
December 2, 1983.
The last few hours of pumping yielded a cummulative average
pumping rate of 3,938 gpm which was the value used for transmissivity computations
and simulation of pumping effects.
There were occasional brief shutdowns during the
60 days for maintenance purposes.
Unfortunately, all attempts to gain entry for
pumping
level
and
recovery measurements
of
the
inch
20
pumped
well
were
unsuccessful.
Good recovery data were obtained from seven of the sixteen observation wells. The
remaining wells were dry or plugged.
The 20 foot shallow and 140 foot deep observation wells 730 feet North of the pumped well, in concert with the 20 foot shallow
and 140 foot deep observation wells 100 foot West of the pumped well, provided the
most useful data.
These 140 foot deep observation wells were completed, using commercial well points, within the coarse gravel aquifer of the Colorado River deltaic
deposits.
The wells were constructed of 11-2 inch steel pipe installed in a six (6)
inch mud - rotary drilled hole.
Two well point sections were installed at the bottom
providing four (4) feet of screened length.
The
recovery data clearly shows more
change
in
the deep piezometers than
in
the
shallow piezometers.
For example, the 4N shallow well recovered about 1 foot compared to a 1.7 foot recovery for the 4N deep well, both wells being 730 feet away
from the pumped well.
The 3W shallow well recovered 1.5 foot compared to 5.1 feet
for the 3W deep well.
Results are consistant with many other Colorado River delta
well tests.
Recovery times as indicated by Figures 8 and 9 were quite rapid. The
deep observation wells appear to have recovered fully within one day whereas the
shallow water table observation wells took about one week.
Just before pumping shutdown, the equilibrium drawdown levels of the 3W and 4N piezometers showed that the difference between the water table surface and the piezometric
surface became less as the distance from the pumped well increased (3.1 feet 100 feet
away, and 0.7 feet 730 feet away).
107
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Application of standard residual drawdown analysis (see for example USBR's Groundwater Manual, 1981 pgs. 114 -121) of the deep observation wells 100 feet West and 730
feet North of the pumped well yielded transmissivity values of 825,000 and 1.1
million gallons per day per foot respectively (Figure 10).
No field tests were available for specific yield of the water table aquifer nor for
the storage coefficient of the semi -unconfined coarse gravel aquifer although a specific yield of about 20 percent and storage coefficient 1.x10 -4 are commonly used for
similiar materials in the Yuma area.
CONCLUSIONS & RECOMMENDATIONS
After sixty days of testing, it was apparent that pumpage of a well on the land side
of the levee could indeed overcome the influx of river water to a sufficient extent
as to effect the needed drawdown of the local water table.
The Auza well test confirmed the belief that strategically placed dewatering wells in and around the Yuma
area would reduce the ground water flooding that was presently being experienced, and
could prevent future flooding events due to high river flows.
Based on the information gained by this test it was possible to advise the YUMA FLOOD
CONTROL TASK FORCE that the installation of curtain wells would be an effective solution to the area's subsurface flooding problems.
Armed with this data, the TASK
FORCE proceded with its plans to obtain funding and to install the curtain well
system.
As of this writing, twelve (12) of the proposed eighteen (18) wells are
installed and operating, and have proved effective in alleviating Yuma's flooding
problems.
More detailed results of the effectiveness of the curtain wells system is
reserved for future presentation.
Another analysis that is currently being pursued is an attempt to model the aquifer
system using the Auza well test data.
The multi- aquifer system interfaced with surface boundary effects present unique difficulties in modeling.
This endeavor is
being pursued by the authors, utilizing a finite difference (Prickett- Lonnquist) and
a finite element (Golder) model on the USBR Cyber and USBR VAX 750 computers located
in Denver, Colorado, and Yuma, Arizona, respectively.
Results of this research
should be available within a few months.
ACKNOWLEDGEMENTS
The authors wish to acknowledge the aid and assistance of their respective agency
staff persons who contributed enumerable hours of hard work during the course of this
project, and report preparation.
.Specifically, we wish to thank Penny Ahearn,
Cliff Morris, Shane Mulvaney, Dennis Watt and Fred Croxen
Dee Fuerst, Art Cornelius, Carl Rich and Deanna Hulse (ASLD).
(USBR);
Tim
Erwin,
REFERENCES
Kruseman, G.P. and N.A. de Ridder,
Data ", ILRI, Bulletin 11, 1970.
"Analysis
and Evaluation of Pumping Test
Mock, Peter, Earl Burnett and Bruce Hammet, "Report on the Digital Computer Model
Standard of Yuma Area Groundwater Problems Associated With Increased River Flows
In The Lower Colorado River From June 1983 to June 1984 ", ADWR, April 1985.
"Colorado River Flood of 1983, Findings and Emergency Solutions Report ",
FLOOD CONTROL TASK FORCE, September 1983.
112
YUMA
ENIINIME
MMINEIE
51111BEN
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1
l
REFERENCES
CONTINUED
"Ground Water Manual ", U.S. Department of Interior, U.S. Bureau of Reclamation,
(USBR), 1981.
"Ground Water Status Report -1983, Yuma Area - Arizona, California ", Volume No. 1,
USBR, February 1985.
Yuma Daily Sun, Yuma, Arizona, 1983 -84 (various issues).
114
APPENDIX A
Representative Membership On Yuma Flood Control Task Force
115
CHAIRMAN
Don Fortney, County Engineer, Yuma County Public Works
FEDERAL MEMBERSHIP
I.
U.S. Bureau of Reclamation
Bill Plummer
Larry Dozier
Bob Brose
Ken Trompeter
Ken Sidebottom
Tom Boyer
Earl Burnett
2.
Bureau of Indian Affairs
William McAnally
3.
U.S. Geological Survey
Richard J. Trenck
4.
U.S. International Boundary and Water Commission
Al Goff
5.
U.S.D.A. /Agricultural Stabilization & Conservation Srv.
Norm Harrison
6.
U.S.D.A. /Soil Conservation Service
Allan R. Powers
7.
U.S.D.A. /Lower Colorado River R.C. &D.
John Colvin
8.
Corps of Engineers
Don Gross
Joe Dixon
Bill Crull
Abnish C. Amar
Bob Douglas
9.
Federal Emergency Management Agency
Al Hann
Frank L. Kishton
C. Carney Moran
STATE MEMBERSHIP
10.
Arizona Department of Water Resources
Wesley E. Steiner
Phil Briggs
Frank M. Barrios
Ed Nemecek
Steve Jenkins
Peter Mock
11.
Arizona Division of Emergency Services
Bob Tanguy
Dick Lockwood
Donald Hornecker
Michael Reichling
12.
Arizona Department of Health Services
Jon M. Counts
Susan Keith
Jane Lange
13.
Arizona Department of Transportation
Bill Higgins
14.
Arizona State Land Department
Bill Allen
Don Young
COUNTY & CITY MEMBERSHIP
15.
Yuma County Department of Public Works
Bob Hampshire
Roger Schoenherr
16.
Yuma County Emergency Services
Jerry L. Chapman
17.
Yuma County Health Department
Larry Leach
Lenor Stewart
18.
City of Yuma
Benny Gonzales
Larry Hunt
Joe Meier
Charles McBride
117
19.
Town of San Luis
Jess Vella
20.
Town of Somerton
Bob Beeman
PRIVATE & PUBLIC MEMBERSHIP
21.
Yuma County Water Users Association
Berry Kehl
22.
El Paso Nat. Gas Company
Gerald Grieder
23.
Arizona Public Service
Larry Nelson
24.
Mountain Bell
R. L. Maytum
Task Force members were asked to participate based on specific knowledge and exper-
tise that selected individuals could provide or a group of individuals as agency
representatives could provide.
through
the
combined
efforts
Report findings and proposed solutions were developed
of
the
task force members
but
does
not necessarily
represent an official sanction or position from any of the agencies represented.
118
APPENDIX B
Geologic Log of 140 Foot Monitor Well - Auza
119
GEOLOGIC LO(.. JF DRILL HOLE
SHEET
HOLE
PROJECT CRFW &LS
Pilot Hole
FEATURE
AW -1P (Auza)
STATE
(C -8 -22) 18 ddc (1965 Yuma East AZ -CA Quad Map)
LOCATION
Arizona
COORDS
BEGUN November 28, 1983 FINISHED November 29, 1983. OUND ELEV 130' (topo map) ODEPTHO
STEM LOGGED BY
Greq Bushner
OTHER LOGS
ON November 29.
Greg Bushner
E
NOTES ON DRILLING
METHOD, EQUIPMENT,
HOLE SIZE, MUD LOSS,
CASING, CAVING,
NATURAL GAMMA LOG
COMPLETION, ETC.
Increasing Radiation
REVIEWED
RANGE ,0
BY
(FEET)
ELEy 124.40 feet
LMR -354B
Earl Burnett
INTERPRETATION
DEPTH LITHO.
(FEET) LOG
ELEV.
-
WATER LEVEL MEASURED ON December 6, 1983
,UIPMENT USED
Drilling Time, Drilling Character
-
N
'DEPTH TO WATER 5.60 feet
GEOPHYSICAL LOGS Self Potential, Single Point Resistance, Natural Gamma
RUN 8Y
OF
(BASED ON ALL LOGS)
.
0
Drilling Rig:
USBR
Failing 1500, truck
mounted.
-:.'
-
0 -27' Sand; fine, loose, brown, scattered
thin clay layers.
' ''-:-'
Driller:
R. Rodriguez
:
°
--
to
Drilling Method:
Direct rotary with bentonite mud.
Drilled
-
with 6 -3/4 -inch tricone
bit.
,*
.
.';
20
Completion:
Installed 1 -1/2 -inch
galvanized conduit plus
_ _'_
----
loss
three 2 -1/2 -foot by
1 -1/4 -foot wire wrapped
30
27 -32' Clay; fat, brown.
-=__
=--
32 -38' Sand; fine, loose, brown.
--
screens and 1/2 -foot
wash back valve at
bottom.
-
.
=-_'_''
Washed hole with fresh
water. Stickup =
2.0 feet; Elevation top
of pipe = 132 feet
40
- " ':
=
38 -42' Clay; fat, brown.
42 -65' Sand; fine, loose, dark gray
with a lense of fat brown clay at
58 -59'.
Caving and Mud Loss:
50
No caving; heavy mud
-i
at 117 feet.
60
,.
r=.
Q-.
..
.
.
_
65 -77' Clay; fat, brown.
....
'
,
-
80
_
:
77 -95' Sand; fine, loose, dark gray
with some fine gravel.
90
.cá
; Q;
,
9p' áób
120
CRFW&LS
b.0 ó
100;: ó°Q,
Arizona
AW-1(P)
GEOLOGIC LOG vF DRILL HOLE
SHEET
FEATURE
Pilot Hole
HOLE
AW-1 P
BEGUN
PROJECT
LOCATION
STEM LOGGED BY
(C -8 -22) 18 ddc (1465 Yuma East h7- Cl_Cyad Map)
Greg Bushner
NOTES ON DRILLING
METHOD, EQUIPMENT,
HOLE SIZE, MUD LOSS,
Drilling Charartar
-
E
-
WATER LEVEL MEASURED ON
REVIEWED BY
RANGE 10
2
ELEV 124.40 feet
December 6, 1983
MR -3548
Farl Burnett
NATURAL GAMMA LOG
ELEV. DEPTH LITHO.
(FEET) (FEET) LOG
CASING, CAVING,
COMPLETION, ETC.
N
feet
-DEPTH TO WATER 5.60
ON Nnvamhpr 79. 14R$OUIPMENT USED
Greg Bushner
OTHER LOGS Drilling Time
COORDS
(topo map) DDEPTHD
'
GEOPHYSICAL LOGS Self Potential, Single Point Resistance. Natural Gamma
RUN BY
OF
STATE Ariinna
CRFtI&LS
November 28, 1983 FINISHED November 29, 19p4_
KstiOUNO ELEV 130
2
"
.
INTERPRETATION
(BASED ON ALL LOGS)
Increasing Radiation
4901,
100
95 -140' Gravel; fine to coarse, well
rounded, well graded river gravels.
° 8p Some cobbles.
:037
oo
III_lIIo
a
¡.
d7,O é ó
ao
_
oafo0
a
g?0
,
o
--4
ea 60i
1 20
e e.
_0se,
^d
8
...e:1,,,
_
bs
1
30 -? Ott,
:0,.1 0o
0o8os
-DOS%
O DO v
.
1 40 .ate °Q
1 50
1 60
1 70
1 80
-
1 90
-
121
^.
200
-
Ari7nna
2
..,.
AW-1 ( P )
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