1
The problem with determining irrigation conveyance seepage: which value is correct? 1
June E. Wolfe III1†, Peter M. Allen2, John A. Dunbar2, and Haw Yen1 2
1 Blackland Research and Extension Center, Texas A&M AgriLife Research, 720 East Blackland 3
Road, Temple, Texas 76502, USA 4
2 Department of Geosciences, Baylor University, Waco, Texas 76798, USA 5
6
† Corresponding author; email: [email protected] 7
Tel: (254) 774-6016 8
2
Abstract 9
This study compared three seepage estimates of the Hawaiian Commercial & Sugar concrete-10
lined irrigation canal system in Maui, Hawaii, to evaluate logging flow meter technology applied 11
to the inflow-outflow method. Numerous tests were conducted under short reach and highly 12
variable flow conditions to determine if seepage in excess of 15% of inflow could be detected 13
using paired acoustic Doppler flow meters. Results were contradictory; comparing instrumental 14
uncertainty to seepage indicated flow measurement uncertainty overwhelmed seepage signal in 15
the majority of cases. Additionally, computed seepage rates, in excess of 15% of inflow, were 16
consistently higher than generally accepted maximum rates for concrete-lined canals by several 17
times. Measured values ranged from -1.926 to 0.786 m.day-1, which resembled un-lined sand or 18
gravel bed materials. A theoretical global seepage calculation ranged from -0.183 to -0.281 19
m.day-1, an average system loss of -0.219 m.day-1 while a single direct measurement, using the 20
ponding method, indicated -0.116 m.day-1 of loss, within a limited test reach area. 21
Keywords: Irrigation; Seepage; Acoustic Doppler flow meter, Measurement uncertainty 22
1. Introduction 23
Canal seepage contributes to irrigation losses and occasional gains. Seepage estimates may 24
be obtained through numerous techniques, which often yield considerably different results. 25
Determining which estimate to rely upon poses a significant problem. In general, theoretical 26
estimation is the least certain as it combines expert opinion and incidental information with 27
actual measurements; results cannot be certain unless validated through actual measurement. 28
Direct measurement, via point or ponding methods, is considered most accurate but is spatially 29
limited, disrupts canal flow, and does not reflect actual operating conditions. Indirect 30
3
measurement, via the inflow/outflow method, does not interrupt conveyance operations, but is 31
technically challenging and suffers accuracy due to high uncertainty associated with random 32
variation in flow measurement. This study considers theoretical, direct, and indirect seepage 33
estimates to evaluate logging acoustic flowmeter technology. 34
Global or system-wide, theoretical accounting requires flow estimates to be made at the 35
head and tail of the system. Major inflows, abstractions, and routine irrigation volumes between 36
the head and tail are also assessed (Pontin et al. 1979). Compared with theoretical estimates, 37
empirical methods provide more satisfactory information for management purposes. 38
Direct measurements of seepage represent a volume loss over a defined area and provide, 39
perhaps, the best estimate available (Alam and Bhutta, 2004). While they enjoy lower 40
uncertainty they suffer significant operational restrictions. Temporary dams must be installed 41
and seepage varies with changing head values and porosity, due to sedimentation (Weller and 42
McAteer 1993). Ponding tests are most attractive because they are not dependent upon the test 43
reach size and the technical requirements are low. A practical how-to presentation of ponding 44
method field techniques and basic calculations is given by Leigh and Fipps (2009). A 45
comprehensive mathematical treatment is conducted by Weller (1981) who describes two 46
methods, calculation approaches, and provides extensive treatment of measurement uncertainty. 47
Table 1 is a summary of accepted irrigation canal seepage rates, determined by direct 48
methods, for concrete-lined and various natural materials. There is high variability within and 49
overlap among the materials listed and there are many exceptions (Leigh and Fipps, 2009; 50
Sonnichsen, 1993; USDA, 1993; Worstell, 1976). In general, the maximum expected seepage 51
rate for damaged concrete-lined channels is approximately 1 foot per day (Worstell, 1976). 52
4
Indirect measurements of seepage represent volume loss as the difference between two flow 53
measurements, made at two points, over a defined area. The inflow/outflow method is 54
particularly attractive because it does not interrupt canal operations and accurately represents 55
working conditions (Weller, 1981). However, the indirect measurement approach is very 56
sensitive to test reach area and imposes a high degree of instrumentation complexity. Most 57
troubling is the problem of cumulative random variation in flow measurements which are often 58
large enough to completely mask the seepage signal. The inflow-outflow method is therefore 59
best suited to high-seepage canals where losses are greater than the measurement variance (Alam 60
and Bhutta, 2004). The method may be applied at various scales, from an entire irrigation system 61
to isolated test reaches. However, measurements are best over long reaches, with appreciable 62
seepage and without diversions. It is often difficult to obtain sufficiently accurate flow 63
measurement data, particularly for short reaches with low seepage rates because accuracy 64
decreases as the percentage of flow lost to seepage decreases (ANCID, 2003). Maintaining 65
channel conveyance levels constant during measurement is also critical. 66
Modern acoustic flow meters demonstrate ~5% precision under laboratory conditions, 67
however because seepage is frequently less than 5% of flow in a given reach, it often cannot be 68
distinguished from measurement uncertainty. In order to test new flow meter technology with 69
1% accuracy rating, numerous inflow/outflow measurements were conducted in experimental 70
reaches of varying length and concrete liner condition using paired, logging acoustic Doppler 71
flow meters. Heavily damaged reaches were suspected to exhibit seepage greater than expected 72
maxima as others have noted that severely damaged concrete canal linings may seep more than 73
unlined canals due to cracks, insufficiently sealed diversions, etc. (Laycock, 1993). 74
75
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2. Materials and Methods 76
The Sontek IQ Plus acoustic Doppler current profiler (IQ), a bottom-mounted flowmeter, 77
was selected to make inflow-outflow measurements because it was designed specifically for 78
open channel irrigation flows with a specified precision of ~ 1% of measured velocity. The 79
instrument utilizes a 5-beam design, combining a vertical depth beam with four velocity profiling 80
beams. The vertical depth beam complements a high-resolution pressure sensor to determine 81
water level that is then used to compute channel cross-sectional area based on user-entered 82
survey data. By syncing two instruments in time, concurrent inflow and outflow discharge 83
volumes within the test reach could be determined. After correcting for conveyance times 84
between the two measured points, estimates of seepage losses or gains were made by simple 85
subtraction. 86
Seepage expression can take many forms. Two common expressions include: 1) length per 87
unit time and 2) percentage of conveyance volume. When expressed as a function of canal 88
dimensions using the inflow/outflow method, seepage volume is determined by the difference 89
between inflow and outflow divided by the product of the test reach length and wetted perimeter; 90
which is defined by the height of the water under conveyance and the canal geometry. 91
Weller (1981) suggested the following simplified expression which results in a seepage 92
value expressed as length per unit time (e.g., m.day-1). Measurements expressed in this manner 93
ignore scale by removing the area and may be easily compared with results of other studies, 94
following any required unit conversions. When computing seepage values be especially aware 95
of time units as flow is usually reported as an instantaneous value (i.e., seconds) while seepage is 96
most commonly reported as a daily value. 97
𝑆 =𝑄𝑜𝑢𝑡− 𝑄𝑖𝑛
𝑊𝑝𝐿 [1] 98
6
Where: 99
𝑆 = Seepage with respect to time (m.day-1) 100 𝑄𝑖𝑛 = Measured flow volume into the test reach with respect to time (m3.sec-1) 101 𝑄𝑜𝑢𝑡 = Measured flow volume out of the test reach with respect to time (m3.sec-1) 102 𝑊𝑃 = Wetted perimeter length (m) 103 𝐿 = Reach length (m) 104 105 106
Note that if measured outflow is greater than inflow, the algebraic sign result from equation 107
[1] is plus (+), signifying a seepage gain. Conversely, if measured outflow is less than inflow 108
then the sign is minus (-), signifying a seepage loss. The sign difference is sometimes ignored in 109
the literature and may lead to confusion. 110
Seepage is also commonly expressed as a percentage of the flow. This provides a means to 111
standardize or normalize results for relative comparisons. Simplified for two measurement 112
points of an irrigation canal test reach (i.e., inflow and outflow), it is calculated as: 113
114
𝑆% = 𝑄𝑂𝑢𝑡−𝑄𝑖𝑛
𝑄𝑚𝑎𝑥 .100 [2] 115
Where: 116
S% = Seepage with respect to the maximum flow volume (%) 117
𝑄𝑜𝑢𝑡 = measured volume flowing out of the test reach, with respect to time (m3.sec-1) 118
𝑄𝑖𝑛 = measured volume flowing into the test reach, with respect to time (m3.sec-1) 119
𝑄𝑚𝑎𝑥= maximum volume flowing into or out of the test reach, with respect to time (m3.sec-1) 120
100 = Factor conversion to percent 121
122
The percent seepage may range from 0%, when Qout and Qin are equal, to ±100%, when either 123
Qin or Qout has no flow (Wilberg et al., 1998). 124
With any measurement, uncertainty must be considered. As with seepage, uncertainty can 125
be determined, expressed, and interpreted in various ways, dependent upon user application. The 126
7
uncertainty in seepage determined by the inflow/outflow method is due to many contributing 127
factors, the most influential being: inflow and outflow measurements, test reach dimensions, test 128
reach storage, inputs, take-outs, rainfall, and evaporation (Weller, 1981). Martin and Gates 129
(2014) quantified the numerous error sources associated with the method and found that more 130
than 80% was due to random errors in the flow measurements which can be broken down into 131
variations in space and time, instrument precision, and human error. Computing a percent 132
seepage error (Se%) is useful for determining if the seepage signal exceeds the error associated 133
with flow measurements, when normalized to maximum test reach flow. Seepage uncertainty, 134
due to flow measurement uncertainty, simplified for two measurement point of an irrigation 135
canal test reach, is calculated as: 136
137
𝑆𝑒% = 𝑋𝑄𝑜𝑢𝑡𝑄𝑂𝑢𝑡 + 𝑋𝑄𝑖𝑛
𝑄𝑖𝑛
𝑄𝑚𝑎𝑥 . 100 [3] 138
Where: 139
𝑆𝑒 % = Seepage uncertainty with respect to max inflow or outflow from test reach expressed as 140
percent (%) 141
XQin = estimated accuracy of inflow measurement; <0.05 = excellent, 0.05 = good, 0.08 = fair, and 142
> 0.08 = poor. 143
XQout = estimated accuracy of outflow measurement; <0.05 = excellent, 0.05 = good, 0.08 = fair, 144
and > 0.08 = poor. 145
Qin = measured discharge into the test reach with respect to time (m3.day-1) 146
Qout = measured discharge out of the test reach with respect to time (m3.day-1) 147
𝑄𝑚𝑎𝑥= maximum discharge into or out of the test reach with respect to time (m3.day-1) 148
100 = Factor conversion to percent 149
150
Note that when seepage, expressed as percent with respect to maximum flow volume, is equal to 151
or greater than the seepage uncertainty error, the error in the measurement is greater than the 152
seepage signal and the seepage signal cannot be detected. Flow measurement accuracy is 153
8
dependent upon the instrument precision and its application under field conditions. Note that 154
manufacturer specifications determined under ideal conditions cannot represent actual field 155
results. Typical reported field ranges for propeller type flow meters include: <5% = excellent, 156
5% = good, 8% = fair, and > 8% = poor (Wilberg et al., 1998). Many experts suggest that under 157
field conditions acoustic meters can resolve only 5% of inflow, at best (ANCID, 2003). 158
Manufacturers have reported laboratory accuracies of 1% however; independent testing indicates 159
they may range over 15% (Hiner et al, 2012, USGS 2015). 160
As seepage volume loss is highly dependent upon test reach area, measurement error can 161
sometimes be overcome by increasing the reach area (i.e., the product of length and wetted 162
perimeter). Reach length is the variable within greatest control of the researcher. The wetted 163
perimeter, dependent upon water conveyance level, may also be controlled, although to a lesser 164
extent. The reach length required to overcome flow measurement error may be calculated using 165
the following equation, modified from Weller and McAteer (1993). 166
167
𝐿 𝑄𝑚𝑎𝑥 𝑋𝑄𝑚𝑎𝑥
𝑊𝑝 𝑆 86400 [4] 168
169 Where: 170
𝐿 = Length of test reach (m) 171 𝑄𝑚𝑎𝑥 = Maximum flow into or out of the test reach with respect to time (m3.sec-1) 172 𝑋𝑄𝑚𝑎𝑥
= Uncertainty in the maximum flow measurement, as percent (%) 173 𝑊𝑝 = Wetted perimeter of test reach (m) 174
𝑆 = Seepage expected from reach, length with respect to time (m.day-1) 175 86400 = Factor conversion from seconds to days 176 177
178
Maximum flow volume, wetted perimeter, expected seepage rate, and maximum flow 179
measurement error must be estimated based upon field conditions, operator experience, and 180
equipment manufacturer specifications. For example: if expected maximum flow = 0.708 m3.sec-181
1, manufacturer specified flow meter accuracy = 4%, estimated average wetted perimeter = 9.14 182
9
m, and expected seepage = 0.219 m.day-1, then the minimum test reach length required to 183
observe a seepage signal greater than the instrumental measurement error is: (0.708 x 0.04 / 9.14 184
x 0.219) x 86400 = 12215 m. Be especially aware of time units as flows are usually reported as 185
instantaneous values while seepage is most commonly reported as daily values, requiring the 186
conversion factor of 86400 seconds per day. 187
HC&S officials estimated their conveyance losses using a global accounting method. 188
Overall system seepage loss was estimated by considering inflow volumes from rain-fed canals 189
and supplementary wells, and abstraction volumes to power plants, minor canals, and irrigated 190
fields. Inflow volumes were calculated from a flume water level at the head of the system. 191
Diversions to power plants and minor canals were determined by similar means. Diversions to 192
field irrigation were calculated based on pipe dimensions, operational pressures, operational 193
time, and irrigated area. Total system conveyance loss was estimated to be approximately 15% of 194
inflow volume. An average daily global system seepage rate was determined by multiplying the 195
monthly mean inflow volumes ranging from 446,094 to 686,914 m3.day-1(average 534,241 196
m3.day-1), based on data from 2010 through 2013, by the 15% percent inflow volume loss 197
estimate and dividing by the canal conveyance area of 1,202,659 m2, for the total system, 198
determined by GPS. This yielded a global seepage rate that ranged from 0.183 to 0.281 m.day-1 199
and averaged 0.219 m.day-1, values within accepted ranges for concrete-lined irrigation canals 200
(Table 1). 201
A representative test reach 107 meters in length was selected by HC&S to provide a 202
reference seepage value, determined by direct measurement, using the ponding method. The 203
selected reach’s concrete lining was considered to be in average condition, relative to the entire 204
system. Water loss due to seepage was calculated as function of the change in canal cross-205
10
sectional area, measured by the change in water level, over time. Water level was recorded at one 206
minute intervals with an ISCO 4230 Bubble Flow Meter (ISCO, Lincoln NB), for approximately 207
36 hours. 208
Multiple indirect measurements across the HC&S plantation were made using the inflow-209
outflow method between July and December 2013. Two logging flowmeters (Model: IQ Plus, 210
Firmware version 1.54, Manufacture: SonTek, San Diego, CA) were used to make concurrent 211
discharge measurements at the inflow and outflow locations of 7 selected test reaches 212
representing concrete lining conditions ranging from no damage to highly damaged (Photo 1). 213
Portable mounts were designed to allow manageable positioning and operation of the IQ 214
instruments during the numerous short-term deployments. Each mount consisted of a 0.63 cm 215
thick by 30.48 cm square steel plate welded to 2.54 cm diameter by 1.22 m long steel tubes along 216
each side (Photo 2). Ropes attached to the front tubes and back portion of the plate facilitated 217
instrument alignment during deployment. After arrangement, guide ropes were allowed to drift 218
behind the instrument in order to minimize interference and flow measurement variation. IQ Plus 219
logging tracked instrument placement stability (i.e., changed in pitch and roll) during 220
deployment. 221
Suitable test reaches were identified by examining maximum available continuous reach 222
lengths and visually-assessed liner conditions between diversions (i.e., inflow points, withdrawal 223
points, connection ditches, and reservoirs). A cross-sectional survey, to determine channel 224
geometry at each inflow and outflow measurement location, was completed using standard rod 225
and level surveying techniques described by Harrelson et al. (1994). Cross-sectional information 226
was programmed into the IQ Plus flow loggers to convert water level measurement to flow area. 227
Acoustic Doppler flow velocity measurements, combined with flow area, yielded instantaneous 228
11
discharge estimates. Each IQ Plus was programmed for a 2 minute integrated velocity 229
measurement interval and deployed three times for 1-3 day periods within test reaches. 230
Following each test, paired two-minute data were reviewed to identify concurrent periods of 231
steady instrument position, water stage, and water velocity between the inflow and outflow 232
points. Water conveyance time for each reach (i.e., lag time) was estimated by dividing the 233
average inflow and outflow velocities by the reach length. The final inflow and outflow values 234
were computed as the average of 30 individual, 2 minute measurements (i.e., 60 minute period). 235
3. Case Study and Results 236
The Texas A&M AgriLife Research / Blackland Research and Extension Center - Water 237
Science Laboratory (AgriLife) was tasked with verifying irrigation canal seepage estimates for 238
the Hawaiian Commercial and Sugar (HC&S) plantation located in, Puʻunēnē, Maui, Hawaii, as 239
part of a study developing a water balance model to aid plantation management with water 240
allocations. The East Maui Irrigation company operates Hawaii's largest irrigation system, a 241
network of six canals on the north flank of Haleakala volcano diverting surface water from rain 242
fed streams in northeast Maui to supply >14,000 hectares of the HC&S plantation (Cheng, 2012). 243
HC&S field personnel documented canal conditions through visual observation, photographs, 244
and geo-referencing with a handheld global positioning system (GPS) unit. 245
Results for the single ponding test showed the average seepage rate, after an initial 2 hour 246
equilibration period, was -0.116 m.day-1; a value well within accepted range for concrete-lined 247
irrigation canals (i.e., <0.305 m.day-1). 248
Seepage measured by the in/out method and computed by Equation [1] ranged from a 249
maximum loss of -1.926 m.day-1 to a maximum gain of 0.786 m.day-1. Seepage expressed as 250
percent of maximum inflow or outflow from the test reach computed by Equation [2] ranged 251
12
from -23.07% to 23.44% of the maximum flow. Flow measurement uncertainty computed by 252
Equation [3], using double the equipment manufacturers expected instrument error of 1% (i.e., 253
2% of flow measurement value), ranged from 3.53% to 3.99%. The resulting test reach length 254
required to overcome measurement uncertainty computed by equation [4], using measurement 255
uncertainties computed by equation [3] and an assumed maximum expected seepage of 0.305 256
m.day-1 for heavily damaged concrete, ranged from 182 to 3049 meters. Inflow-outflow test 257
results from six other studies, including one from Maui (Cheng, 2012), examining seepage in 258
concrete-lined canals were included for comparison (Table 2). 259
Figure 1 further illustrates the problem with measurement uncertainty experienced by 260
AgriLife and others. Plotting the maximum conveyance volume against seepage as percent of 261
maximum conveyance volume, shows that the many reported inflow-outflow measurements, in 262
concrete lined canals, fall within instrument uncertainty ranges. Other sources of uncertainty 263
(channel storage, etc.) further exasperate the problem. 264
4. Discussion and Conclusions 265
Global accounting suggested an average seepage rate of -0.219 m.day-1 while one reference 266
test using the ponding method yielded a seepage rate of -0.116 m.day-1. These estimates are both 267
spatially limited. The global value represents seepage loss over 100% of the system while the 268
ponding value represents seepage loss of only ~0.16% of the system; thus one is too general 269
while the other is too specific. Although both estimates are reasonable and within reported range 270
for concrete-lined canals, they lack information on seepage variability across the system; 271
therefore they have limited usefulness toward irrigation management decisions. In contrast, 272
AgriLife inflow-outflow measurement yielded spatially variable values ranging which ranged 273
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from -1.962 to 0.908 m.day-1. This result represents the opposite problem, too much variation. 274
So which value is correct? There is no definitive answer. 275
Despite known method limitations, experimental test reaches representing the range of canal 276
lining physical conditions, from excellent to poor, were evaluated with the inflow/outflow 277
method to determine if seepage rates greater than HC&S’s global estimate (i.e., 15% of system 278
inflow or ~-0.72 ft.day-1) could be observed using paired, state-of-the art, logging ultrasonic flow 279
meters. In 7 of 21 cases, the theoretical canal area required to overcome measurement 280
uncertainty, determined by Equation [4], was available. Six tests exceeded the 15% theoretical 281
seepage estimate however; no seepage rate estimates in this group were comparable to the global 282
or reference value. Two estimates fell within the expected range of <±0.305 m.day-1 for concrete 283
lined canals (Table 2) however, one test indicated a seepage loss (-0.283 m.day-1) while the other 284
indicated a seepage gain (0.259 m.day-1). Additionally, concrete lining condition did not appear 285
to affect seepage rate; linings in good condition seeped as much or more than linings in poor 286
condition and vice versa. Although some amount of seepage was likely taking place, it is more 287
likely that these results represent random error in flow measurement, due to surging flow 288
conditions and water conveyance lag times (i.e., canal storage), rather than actual seepage. In 289
short, the uncertainty in the flow measurements consistently overwhelmed the seepage signal 290
even in reaches with heavily damaged concrete linings and suspected high seepage. This result 291
was not unexpected as previous researchers have warned that ultrasonic flow measurement 292
uncertainty is likely to range between 5% and 15% of actual flow, under field conditions 293
(ANCID, 2003; Heiner et al., 2012; Weller, 1981;). Recent USGS testing of the SonTek IQ Plus 294
found discharge estimates ranged between -2.4 and -11.6% of the reference value and recent 295
firmware updates have improved flow measurement precision to between -1.6 and -7.9% 296
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(Fulford and Kimball, 2015) however, lacking sufficient canal reach lengths and steady 297
conveyance, results from future attempts would likely be similar to those here presented. 298
The difference between inflow and outflow measurements, as a percentage of the flow 299
(Figure 1), must be larger than the instrument error in order to be detected (i.e., signal must be 300
greater than the noise). Seepage rates thus obtained, in concrete lined canals, when compared to 301
direct measurements, often yield values comparable to sand or gravel channel beds rather than 302
concrete lined channels (see Tables 1 and 2). This observation leads to the conclusion that many 303
reported seepage rates, determined by the inflow-outflow method, may represent measurement 304
uncertainty rather than actual seepage. 305
References 306
Akkuzu, E., 2012. Usefulness of empirical equations in assessing canal Losses through seepage 307
in concrete-lined canals. J. Irrig. Drain Eng., 138(5), 455–460. 308
Alam M. M., and M. N. Bhutta. 2004. Comparative evaluation of canal seepage investigation 309
techniques. Agricultural water management. 66:65-76. 310
ANCID (Australian National Committee on Irrigation and Drainage), 2003. Open channel 311
seepage & control Vol 1.4 – Best practice guidelines for channel seepage identification and 312
measurement. Goulburn-Murray Water, Tatura, Victoria, Australia. 313
Bahramloo, R., 2010. Evaluation of conveyance efficiency and seepage loss in concrete-lined 314
irrigation canals and this effect on water resource saving. Paper I-13. Hamadan Research 315
Centre for Agriculture and Natural Resources, Hamadan, Iran. 316
Cheng, C. L., 2012. Measurements of seepage losses and gains, East Maui irrigation diversion 317
system, Maui, Hawai’i: U.S. Geological survey Open-File Report 2012-1115, 23p. 318
Fulford, J. M., and S. Kimball, 2015. Hydraulic laboratory testing of SonTek IQ Plus. U. S. 319
Geological Survey Open Report 2015-1139. Denver, Colorado. 320
Harrelson, C. C., C. L Rawlins, and J. P. Potyondy, 1994. Stream channel reference sites: an 321
illustrated guide to field technique. Gen. Tech. Rep. RM-245. Department of Agriculture, 322
Forest Service, Rocky Mountain Forest and Range Experiment Station. Fort Collins, CO. 323
Heiner, B. J., T. B. Vermeyen, R. F. Einhellig, and W. W. Frizell, 2012. Laboratory evaluation 324
of open channel area-velocity flow meters. U.S. Department of the Interior, Bureau of 325
Reclamation Technical Service Center, Hydraulic Investigations and Laboratory Services, 326
Hydraulic Laboratory Report HL-2012-03. Denver, Colorado. 327
Kilic, M. and G. I. Tuylu, 2011. Determination of water conveyance loss in the Ahmetli 328
Regulator Irrigation Syste in the Lower Gediz Basin, Turkey. Irrigation and Drainage, 329
60:579-589. 330
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Kinzli, K. D., M. Martinez, R. Oad, A. Prior, and D. Gensler, 2010. Using an ADCP to 331
determine seepage loss in an irrigation district. Agricultural Water Management. 97:801-332
810. 333
Laycock, A., 1993. Precast Parabolic Canals-Revelation and Revolution in Pakistan. In F. A. 334
Zuberi and M. A. Bodla (eds) Proceedings Workshop on Canal Lining and Seepage. 18-21 335
October, Lahore, Pakistan. pp. 469-487. 336
Leigh, E. and G. Fipps, 2009. Measuring seepage losses from canals using the ponding test 337
method. Texas A&M AgriLife Extension. Pub.No. B-6218. College Station, Texas. 338
Martin, C. A. and T. K. Gates, 2014. Uncertainty of canal seepage losses estimated using 339
flowing water balance with acoustic Doppler devices. Journal of Hydrology. 517:746-761. 340
Pontain J. M. A., L. Abed, and J. A. Weller. 1979. Prediction of seepage loss from the enlarged 341
Ismailia Canal. Report Number OD28, Hydraulics Research Station, Wallingford, Oxon, 342
United Kingdom. 343
Sonnischsen, R. P., 1993. Seepage rates from irrigation canals. Open file technical report 93-3, 344
Washington State Department of Ecology. Lacey, Washington. 345
USDA (United States Department of Agriculture), 1993. Part 623 National Engineering 346
Handbook, Chapter 2, Irrigation water requirements. 284 pg. 347
USGS (United States Geological Survey), 2015. Evaluation of AD flowmeters. 348
Wilberg, D. E., R. L. Swenson, B. A. Slaugh, J. H. Howells, and H. K Christiansen, 1998. 349
Seepage investigation for Leap, South Ash, Wet Sandy and Leeds Creeks in the Pine Valley 350
Mountains, Washington County, U. S. Geological Survey. Water-resources investigations 351
report 01-4237. Utah. 352
Weller, J. A., 1981. Estimation of errors in canal seepage derived by discharge measurement in 353
static and dynamic conditions. Report No. OD 25. Hydraulics Research Station, 354
Wallingford, Oxon, United Kingdom. 355
Weller J. A., and P. McAteer, 1993. Seepage measurement techniques and accuracy. In F. A. 356
Zuberi and M. A. Bodla (eds) Proceedings of the International Workshop on Canal Lining 357
and Seepage. 18-21 October, Lahore, Pakistan. pp.171-190. 358
Worstell, R.V., 1976. Estimating seepage losses from canal systems. Journal of the Irrigation 359
and Drainage Division. 102:137-147. 360
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ACKNOWLEGMENTS 361
This project was funded by grants from the United States Department of Defense and the 362
Department of Agriculture - Natural Resources Conservation Service (USDA-NRCS). 363
364
17
365
366
367
Photo 1. Heavily damaged section of concrete-lined HC&S irrigation canal 368
369
370
371
372
373
Photo 2. SonTek IQ Plus acoustic flow meter attached to portable mount. 374
375
376
377
378
Figure 1. Maximum flow (Qmax) verses seepage (S%), as percent of maximum flow, computed by 379
Equation [2] for AgriLife and other studies (data in Table 2). Measurement uncertainty ranges 380
for ±2, ±5 and ±8% of maximum flow, computed by Equation [3] are shown for comparison. 381
18
382 Table 1. Reported seepage rates for concrete and unlined canal bed materials.
Material Low (m.day-1) High (m.day-1) Average (m.day-1)
Concrete 0.003 0.305 0.073 Clay 0.015 0.290 0.076 Loam 0.198 0.366 0.287 Sand 0.259 1.003 0.475 Gravel 0.360 1.768 1.372 Sources: Worstell, 1976; Sonnichsen, 1993; USDA 1993, Leigh & Fipps, 2009 383
1
Table 2. Results from inflow-outflow studies including: test reach measured length, mean wetted perimeter, mean inflow, mean outflow, computed seepage, 384 uncertainty, and required length to overcome measurement uncertainty. 385
Study Canal/Reach
Test
Reach
Length
Mean
Wetted
Perimeter
Qin
Mean
Inflow
Qout
Mean
Outflow
Measur
ement
Error1
Eq. [1]
Seepage
Eq. [2]
Seepage
Eq. [3]
Seepage
Uncertainty
Eq. [4]
Required
Length2
(m) (m) (m3.sec-1) (m3.sec-1) (%) (m.day-1) (%) (%) (m)
Kinzli et al.
(2010)
Williams Lateral 2870 3.50 0.610 0.580 0.05 -0.258 -4.92 -9.75 2471
Socorro Main 4440 10.75 6.460 6.230 0.05 -0.416 -3.56 -9.82 8519
Sili Main 5730 5.50 0.470 0.410 0.05 -0.165 -12.77 -9.36 1211
Peralta Acequia 5870 5.40 0.570 0.510 0.05 -0.164 -10.53 -9.47 1496
Albuquer Main 6720 6.20 1.460 1.220 0.05 -0.498 -16.44 -9.18 3338
Belen Highline 7040 16.86 6.290 6.220 0.05 -0.051 -1.11 -9.94 5289
Akkuzu
(2012)
Menemen Ka1 195 3.18 0.041 0.036 0.05 -0.595 -10.54 9.47 181
Menemen Sa2 325 4.01 0.090 0.081 0.05 -0.551 -9.27 9.54 317
Menemen Sa1 341 5.48 0.208 0.178 0.05 -1.400 -14.55 9.27 538
Menemen Ke1 345 6.10 2.497 2.324 0.05 -7.074 -6.90 9.65 5801
Menemen R3 393 6.10 2.539 2.453 0.05 -3.096 -3.38 9.83 5898
Menemen Se1 459 7.39 5.103 5.029 0.05 -1.870 -1.44 9.93 9786
Menemen R2 540 5.20 1.576 1.541 0.05 -1.058 -2.18 9.89 4295
Menemen R1 575 5.90 2.208 2.200 0.05 -0.206 -0.37 9.98 5303
Menemen Ke2 625 5.24 2.170 2.016 0.05 -4.039 -7.06 9.65 5868
Menemen U1 662 8.26 5.827 5.750 0.05 -1.225 -1.33 9.93 9999
Menemen Se2 675 7.61 5.065 5.011 0.05 -0.907 -1.06 9.95 9433
Menemen L2 1040 11.80 11.429 11.274 0.05 -1.090 -1.35 9.93 13728
Menemen L1 2245 13.10 17.520 17.312 0.05 -0.610 -1.19 9.94 18955
Kilic et al.
(2011)
Ahmetli 1 295 10.50 11.200 10.900 0.05 -8.368 -2.68 9.87 15118
Ahmetli 4 300 10.40 11.900 11.700 0.05 -5.538 -1.68 9.92 16217
Ahmetli 2 1110 11.40 12.400 10.600 0.05 -12.290 -14.52 9.27 15417
Ahmetli 3 1290 11.50 12.500 12.200 0.05 -1.747 -2.40 9.88 15406
Ahmetli 5 1600 10.70 10.000 9.790 0.05 -1.060 -2.10 9.90 13246
2
Table 2 Continued
Bahramloo
(2010)
Asadabad 1200 1.29 0.370 0.350 0.05 -1.116 -5.41 9.73 4065
Kabodrahang 1570 1.37 0.430 0.385 0.05 -1.808 -10.47 9.48 4449
Hamadan 2000 1.25 0.380 0.295 0.05 -2.938 -22.37 8.88 4309
Nahavand 2800 1.10 0.280 0.235 0.05 -1.262 -16.07 9.20 3608
Toiserkan 2800 1.10 0.250 0.195 0.05 -1.543 -22.00 8.90 3221
Bahar 3100 1.27 0.390 0.315 0.05 -1.646 -19.23 9.04 4352
Cheng
(2012)
Koolau K7-8 306 4.57 0.578 0.572 0.05 -0.350 -0.98 9.95 1791
Koolau K9-10 322 4.57 0.708 0.827 0.08 6.983 14.38 14.85 4101
Koolau K9-10 322 4.57 0.714 0.731 0.05 0.998 2.33 9.88 2265
Koolau K11-12 660 4.57 1.253 1.235 0.08 -0.527 -1.47 15.88 6215
Koolau K1-4 885 4.57 0.082 0.068 0.05 -0.309 -17.66 9.12 254
AgriLife
(2013)
Hamakua 15 405 3.22 0.503 0.492 0.02 -0.723 -2.17 3.96 886
Hamakua 15 405 3.36 0.723 0.693 0.02 -1.926 -4.19 3.92 1222
Hamakua 15 405 3.43 0.819 0.815 0.02 -0.282 -0.56 3.99 1352
Hamakua 6 427 2.68 0.664 0.674 0.02 0.786 1.55 3.97 1425
Hamakua 6 427 3.27 1.502 1.495 0.02 -0.464 -0.50 3.99 2607
Hamakua 6 427 3.33 1.581 1.562 0.02 -1.157 -1.20 3.98 2694
Haiku 30 1105 2.86 0.082 0.092 0.02 0.260 10.39 3.79 182
Haiku 30 1105 2.86 0.079 0.102 0.02 0.628 22.63 3.55 201
Haiku 30 1105 2.86 0.080 0.105 0.02 0.671 23.44 3.53 208
Kauhikoa 4 1117 3.12 1.389 1.360 0.02 -0.708 -2.06 3.96 2522
Kauhikoa 4 1117 3.08 1.385 1.333 0.02 -1.319 -3.80 3.92 2547
Kauhikoa 4 1117 3.19 1.473 1.428 0.02 -1.093 -3.06 3.94 2616
Lowrie 13 1192 6.51 2.148 2.038 0.02 -1.223 -5.12 3.90 1869
Lowrie 13 1192 6.17 2.798 2.717 0.02 -0.960 -2.92 3.94 2572
Lowrie 13 1192 5.87 3.156 3.088 0.02 -0.841 -2.16 3.96 3049
Haiku 34 1564 3.30 0.793 0.848 0.02 0.909 6.40 3.87 1457
Haiku 34 1564 3.59 1.263 1.234 0.02 -0.447 -2.30 3.95 1997
Haiku 34 1564 3.75 1.391 1.442 0.02 0.754 3.55 3.93 2181
Lowrie 32 2341 2.44 0.147 0.116 0.02 -0.470 -21.19 3.58 341
Lowrie 32 2341 2.44 0.147 0.113 0.02 -0.514 -23.07 3.54 342
Lowrie 32 2341 2.52 0.560 0.474 0.02 -1.264 -15.39 3.69 1262 1 Italicized (unreported) uncertainties assumed to be good (0.05), based upon USGS instrument rating methods. AgriLife uncertainties based on double manufacture values. 386
3
2 Required length to observe seepage above instrument measurement error level calculated based on a maximum expected seepage of 0.3048 m.day-1 387 3 Bolded AgriLife indicate tests with sufficient reach length to overcome instrument error and computed seepage values within expected seepage range 388