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

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† Corresponding author; email: jwolfe@brc.tamus.edu 7

Tel: (254) 774-6016 8

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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