Lorca Field Report

Cobain Schofield 1 | Page

“Investigating the load capacity of the Guadalentín and the

effectiveness of check-dams in flood prevention”

Introduction

The Guadalentín river basin in Southern Spain covers an area of 3300km2, flowing through one of the

driest regions in Europe before joining the Segura River near Murcia City (Vicente et al, 2003).

A study was conducted across two days in two separate locations within the Guadalentín basin in Lorca

principality. The first day was spent at Nogalte, approximately 10 miles WbN of Puerto Lubreras, where 6

sites where studied along a 0.8km reach of the channel. The bedrock at Nogalte is Schist. The second day

of study was at Torrealvilla along a strength of channel measuring 0.8km. Torrealvilla is located 5.5 miles

north of Lorca and approximately 19 miles NE of Nogalte in an area of Marl bedrock. Both Nogalte and

Torrealvilla receive a mean annual rainfall amount of 289mm, with average annual temperatures of 16

degrees Celsius (Aemet, 2014). The relative locations of each study area are visible in Figure 1.a below:

Figure 1.a – Map showing locations of Nogalte and Torrealvilla relative to Lorca and Puero Lumbreres

The two study areas can be seen below in Figure 1.b, with the first study area at Nogalte shown in Figure

1.b.i and the second at Torrealvilla in Figure 1.b.ii. Both study sites are shown in the same orientation and

scale. The six individual study sites are also included on each map. From here on in, these sites will be


abbreviated and referred to as NG S1..6 and TV S1..6 for Nogalte sites 1 through 6 and Torrealvilla sites 1

through 6 respectively.

Figure 1.b– Area maps of Nogalte (Figure 1.b.i, left) and Torrealvilla (Figure 1.b.ii, right)

The purpose of the study was to test the hypotheses:

1. The channel has the capacity to transport a large load at bank-full discharge

2. Check-dams are an ineffective way of reducing discharge

Methodology

In order to test the hypotheses, data was collected at each site in the field using an assortment of basic

field equipment. At each site a range of measurements were obtained to compile a dataset with the intent

of testing each hypothesis. The measurements at each site were:

Channel Cross Section – the channel width was measured using a 30m tape measure, using the

top of the left side bank when facing downstream as the first measurement point. The tape was

then pulled tight perpendicular to the channel and fixed at the top of the opposite bank. Next, the

depth of the channel was measured using a 5m tape measure, with depth recorded at every break

in slope. The point at which the vertical and horizontal tapes crossed was used as the point of

depth measurement. This allowed the data to be plotted on a scatter-graph as a cross section,

which is included in Appendix A. Due to the ephemeral nature of the river, the channel was ill-

defined, particularly in Nogalte where vegetation was growing both in and around the channel.

Therefore, a best-effort guess was made as to the bank height using visual clues such as flood

marks.

Sediment B-Axis – 25 particles of sediment were randomly selected across the transect of the

studied channel cross section. The random sampling method employed was to look at the sky and

Figure 1.b.i - Nogalte Figure 1.b.ii - Torrealvilla


for each step made, sample the particle found beneath the big toe. The B-axis of the particle was

then measured using callipers.

Channel Slope – the slope of the channel was measured using a ranging pole, 30m tape measure

and a sighting level. A 30m length of channel was measured, using the cross-section transect as

the middle point of the measurement. A ranging pole was then positioned at the downstream

extremity, while a person was stood at the upstream end with the sighting level. The person with

the sighting level viewed the ranging pole through the sight and used the built-in spirit level to level

their sight before reading off the value on the ranging pole which their sighting level cross-hair

pinpointed. The eye-height of the sighting level reader was then measured and deducted from the

ranging pole reading to give the total fall value across the 30m channel length.

Channel Roughness – the roughness of the channel was recorded for later use when computing

the bank-full discharge and velocity by applying the Manning equation. Given that there is no

categorical way of measuring the roughness of a channel, photographs were taken of each site for

later analysis. Each member of the fieldwork group then used the methods outlined by Benson and

Dalrymple (1967) to make an educated guess of the roughness value. The values were then

collated and the average of the group was taken as the value used in the dataset.

The few pieces of equipment that were used did not require calibration in order to obtain readings. The

equipment used was standard basic field equipment. The accuracies of each piece of equipment follow:

30cm ruler :: 0.001m

30m tape measure :: 0.01m

5m tape measure :: 0.001m

2m ranging pole :: 0.01m

Callipers :: 0.0001m

Sighting level :: analogue; spirit level, no measurable component

All of the equipment offered a suitable level of accuracy for the purpose of the field work, although the high

level of accuracy from the callipers was not required. Due to the nature of the sighting level, an average

was taken of 3 consecutive readings owing to the hand-held nature of the device and the uneven surface

on which the observer was stood.

The data collected using these methods can be applied to formulae such as Manning’s Equation to

generate secondary data such as hydraulic radius, velocity and discharge, despite the channel having very

little or no water in at the time of the study. It is the aim of this report to explore the hypothetical properties

of the river when water is present.


Results

Hypothesis 1:

“The channel has the capacity to transport a large load at bank-full discharge”

Load capacity at bank-full discharge was calculated using the basic equation for suspended sediment

discharge outlined by Hickin (1995), using sediment concentration data collected by Seeger (2007) from

the Guadalentín, as an input into the equation. Due to limitations of time and equipment, sediment

concentration data could not be obtained during the study, however Seeger (2007) lists sediment

concentration as 6.9g/l-1 as a variable of their investigation in the Guadalentín Basin, so this number has

been substituted to work out hypothetical sediment load capacities for the channels which were

investigated as part of this report.

The sediment particles which were sampled as

part of the survey were plotted on the Hjulstrom

Curve (Hjulstrom, 1935) in Figure 2. The graph

in Figure 2 shows how the sediment particles fit

into the Hjulstrom Curve at bank-full velocity for

each site. The particles sampled ranged from

sand to pebbles and cobbles, and were each

randomly selected. Table 1 summarises the

graph in Figure 2.

Figure 2 – Hjulstrom curve showing sediment data from the field study

Table 1 – Sediment Summary, all sites at bank-full

Particles in transport 31

Particles undergoing erosion

265

Settled particles 4

All particles 300

Sediment experiencing some transport

NGS2, NGS3, NGS4, TVS6

Sediment experiencing some erosion

All

Sediment experiencing some setting

NGS2

0.001 0.01 0.1 1

0.01

0.02

0.04

0.08

0.16

0.32

0.64

Sediment Size (m)

Bank-fu

ll velo

city (m

/s-1)

NG S1 NG S2NG S3 NG S4NG S5 NG S6TV S1 TV S2TV S3 TV S4TV S5 TV S6


Additionally, the bar chart in Figure 3 shows the hypothetical load capacity of the channel at bank-full

velocity, although this is purely hypothetical as it is based on third-party data (Seeger et al, 2007). The

actual values expressed by Seeger are likely to differ to the true unknown values at each studied site due

to differences in land use, topography, bedrock, vegetation and more, which all contribute to run-off and

water pH, which are factors affecting the sediment concentration.

Figure 3 – Hypothetical load capacity of the channel at bank-full velocity for each site, assuming perfect

conditions

Hypothesis 2:

“Check-dams are an ineffective way of reducing discharge”

Data computed for this hypothesis is based purely on theoretical scenarios, assumptions of uniformity in

the channel, discharge and flow type. The data used has been collated from a number of sources and

from observations and measurements made in the field.

For the sake of this test, it is assumed that the entirety of the river’s load is composed only of schist

particles, in both the Nogalte study area, which is an area of schist bedrock, and in the Torrealvilla study

area, which is an area of Marl. The two rock types must be the same in order to compare the effects of

43

0.3

0

11

5.3

9

44

8.0

3

88

2.3

7

27

76

.51

13

44

.91

33

77

.03

48

83

.15

48

17

.31

18

12

.15

33

66

.73

38

9.1

5

0

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

NG

S1

NG

S2

NG

S3

NG

S4

NG

S5

NG

S6

TV

S1

TV

S2

TV

S3

TV

S4

TV

S5

TV

S6

Load C

apacity

(kg/m

in)


check dams in each channel. Zhao (2007) states that Schist has a dry density of between 2.60g/cm3 and

3.12g/cm3 and a porosity of 10-30%. Using this information, and assuming that each particle measures

((B-axis*2)*(0.5*B-axis)), the following formula was used to generate the volume of the sediment deposit:

Total volume of deposit (m3): ((((Max bank height*2.5)*100*(Max bank height*2.5))*0.5)*Channel width))

Time to in-fill deposit (minutes): (Total volume of deposit / Load Capacity)

The actual values used from Zhao (2007) were 2.6g/cm3 for schist density and 10% porosity.

Figure 4 – Basic model of check-dam sediment build-up, loosely based on Bussi et al (2012) and field

observations on the guided tour days and report data collection days.

During the study, sites were chosen based on them being at least 400m upstream and downstream of a

check dam to allow the measurements to be as representative of a natural channel as possible given the

time restrictions that were faced. The purpose of this test is to see whether or not check-dams at each of

the studied sites would be an effective way of reducing discharge. Figure 5 shows how the sediment

deposit volume and time taken to fill the deposit change at each site.

Figure 5 – Total volume of sediment deposit (Figure 4) and time taken to fill the deposit for each site

Max. bank height

Check dam (2.5x bank height)

Sediment deposit (length = 100x check dam height)

Downstream

R² = 0.0782

R² = 0.0089

R² = 0.3935R² = 0.88210

5

10

15

20

25

30

35

40

45

50

0

5000

10000

15000

20000

25000

30000

35000

40000

45000

1 2 3 4 5 6

Tim

e to r

each m

ax d

eposit v

olu

me (

min

s)

Volu

me o

f S

edim

ent

Deposit (

m3)

Site

NG Sediment Vol

TV Sediment Vol

NG Time to fill

TV Time to fill


The data in Figure 5 shows how, given ‘perfect’ conditions, the sediment deposit would fill within minutes,

given the full bank-full velocity of the channel is reached at each site, and the sediment load capacity is

also reached for schist sediment in suspension only.

Discussion and Conclusion

Hypothesis 1:

“The channel has the capacity to transport a large load at bank-full discharge”

Figure 2 shows how the observed sediment samples fit into the Hjulstrom Curve after calculating site-

specific discharge and velocities for the Guadalentín river. The sediment data on the graph shows that the

majority of the sediment particles would be above the critical erosion velocity threshold, and would

therefore be experiencing some degree of erosion as they are carried downstream. The largest particle

was a 0.72m boulder which was embedded in the channel bed. This particle is sat comfortably within the

transportation zone of the Hjulstrom curve suggesting that the river would be capable of transporting a

boulder of this size at bank-full discharge. A general observation made at each site was that the amount of

bed load sediment appeared small relative to a comparably sized UK river. There were also large

escarpments of bare bedrock in the channel at all of the studied sites, suggesting that either erosion is low

during ephemeral flow, or that ephemeral flow is so high that it transports a lot of the sediment with it,

before depositing the relatively few remnants when the flow reduces and the river dries out. Given the

existence of the data in Figure 2 and Table 1, which were all collected on-site, it is reasonable to assume

that the latter statement is true and that the river does indeed transport a high amount of sediment during

incidences of bank-full flow.

Vegetation in the channel was also noted as being young and under-developed when compared with the

same species at a higher elevation on the flood plain. The vegetation outside of the channel varied in age,

size and development, whereas the vegetation within the channel was very consistent across species’.

Vegetation cover at Nogalte was much denser than that at Torrealvilla, and there were noticeably fewer

gullies and tributaries at Nogalte. According to Thornes and Brandt (1993), erosion is reduced when

vegetation cover is >30%, which helps to prevent or delay the development of gullys or piping, which

contributes to the sediment load of the river. This could be a question of whether the relatively thicker

vegetation cover at Nogalte compared to Torrealvilla is responsible for this, or whether the Schist bedrock,

which is less susceptible to erosion compared to Marl, is responsible (Zhao, 2007).

Although the hypothetical load capacity does rely on ‘perfect’ conditions, it does give a good idea of

relative load capacities between sites, and shows that 7 of the 12 sites have load capacities of well over 1

metric ton per minute. Unfortunately, there is no way of knowing how accurate this data is without having

made a sediment concentration reading at each site during the study, but this was not possible due to time

and equipment restrictions, and the fact that there was no flow during the study. As this measurement is a

vital component of the suspended sediment equation, there was no option but to locate third party data in


the form of Seeger (2007), which was conducted both within the Guadalentín basin, and in the upper

reaches of the catchment in a similar setting and location to this study. In the context of the actual channel,

however, this value would’ve differed to that used, due to differences in run-off, land use, vegetation cover

and bedrock, to name a few factors.

Hypothesis 2:

“Check-dams are an ineffective way of reducing discharge”

Check-dams are the choice solution of the Spanish Government to combatting flooding in the Guadalentín

Basin. This is evident in the number of check-dams present and under construction, both in the

Guadalentín basin and surrounding catchments, such as the Carcavo catchment. The Carcavo is just

34.9km2 in area and contains 40 check-dams. The check-dams are built to approximately 2.5x the height

of the bank height where possible, although in larger gullies and incised channels they may be built higher

(Conesa-Garcıa et al, 2009).

Using the basic model that is outlined in Hypothesis 2 of the Results section, there are a number of

assumptions made about the system:

Bank height and channel width are constant along the length of the model

The flow rate is constant throughout

The flow type is consistent

The sediment composition is uniform

Assumptions made in other calculations such as sediment concentration are presumed correct

This gives the model and its outputs an elementary nature, but it allows a relative comparison between

sites, and allows for some insight into the circumstances surrounding a hypothetical flood scenario. Figure

5 shows that after the above assumptions are considered, the time taken to fill the right-angle triangle

shaped sediment deposit is between 1.5 minutes and 45.5 minutes at bank-full discharge, for volumes

ranging from 1000m3 to 41000m3. This suggests that the check-dam will only be effective during one

flooding event, which has been observed during flooding as recently as 2000, where sediment back-filled

the dam, effectively diverting the flow above and over the dam down a channel with reduced roughness

and friction. This leads to increased velocity and momentum of water, which increases the erosion

capacity of the flow, leading to vertical and lateral erosion of the channel, piping, gullying and increased

sediment load capacity.

Check-dams are also prone to collapse during or soon after a flood event owing to their hastily built nature

and the stress that they are under from discharge and sediment build up. Casillo et al (2014) cites Nyssen

et al (2004) as quoting that 40% of check-dams built in Ethiopia fail within 2 years of construction.

Collapsed check-dams pose problems if a collapse occurs both during or after a flood for different

reasons; A collapse during a flood has the capacity to unleash tonnes of sediment and water at once,

increasing the erosion and abrasion capabilities of the river. If the dam collapses after a flow event, then

the initial impact is not likely to be very great, but the large boulders which are a by-product of the collapse

and the sediment stored at height with greater gravitational potential, would no doubt be problematic in the


event of a future flood, particularly if there are bridges or structures in or close to the river channel which

could stand in the way of the suspended sediment.

Considering the observed outcome of hypothesis 1, and its potential impacts and influences on hypothesis

2, the data and the subsequent graphs suggest that the river channel is capable of supporting a large

suspended load at bank-full discharge; the load capacities of TV S2 and TV S3 in Figure 3 show load

capacities of over 4.5 metric tons of sediment per minute, which Figure 5 goes on to show that these two

sites would completely back fill a check-dam in under 30 minutes. Once a check-dam is back-filled, it is

useless in subsequent flooding events and only serves to speed up flow and reduce resistance.


References

Aemet. (2014). Lorca XML Data. Available: http://www.aemet.es/xml/municipios/localidad_30024.xml. Last

accessed 29th September 2014.

Benson, M.A., and Dalrymple, Tate. (1967). General field and office procedures for indirect discharge

measurements: US Geological Survey Techniques of Water-Resources Investigations, Book 3.

Bussi, G., Rodríguez-Lloveras, X., Francés, F., Benito, G., Sánchez-Moya, Y., Sopeña, A.. (2013).

Sediment yield model implementation based on check dam infill stratigraphy in a semiarid Mediterranean

catchment. Hydrology and Earth System Sciences. 17 (1), 3339-3354.

Castillo, C., Perez, R., Gomez, J.A.. (2014). A conceptual model of check dam hydraulics for gully control:

efficiency, optimal spacing and relation with step-pools. Hydrology and Earth System Sciences. 18 (1),

1705-1721.

Conesa-Garc ́ıa, C., Garc ́ıa-Lorenzo, R.. (2009). Effectiveness of check dams in the control of general

transitory bed scouring in semiarid catchment areas (South-East Spain). Water and Environment. 23 (1),

1-14.

Ham, D.G. and Church, M., 2000: Bed material transport estimated from channel morphodynamics:

Chilliwack River, British Columbia. Earth Surface Processes and Landforms, 24 (10) 1123-1142.

Hickin, E.J.. (1995). Chapter 4: Sediment Transport. In: River Geomorphology. University of California:

Wiley. 79-88.

Hooke, J., Sandercock, P.. (2012). Use of vegetation to combat desertification and land degradation:

Recommendations and guidelines for spatial strategies in Mediterranean lands. Landscape and Urban

Planning. 107 (4), 389-400.

Nyssen, J., Veyret-Picot, M., Poesen, J., Moeyersons, J., Haile, M.,Deckers J., and Govers, G.: The

effectiveness of loose rock check dams for gully control in Tigray, northern Ethiopia, Soil Use

Management., 20, 55–64, 2004.

Seeger, M. (2007). Uncertainty of factors determining runoff and erosion processes as quantified by

rainfall simulations. Catena. 71 (1), 56-67.

Thornes, J.B., Brandt, J., (1993). Erosion–vegetation competition in a stochastic environment undergoing

climatic change. Environmental change in the drylands: Biogeographical and geomorphological

responses, Wiley, Chichester (1993), pp. 306–320

Vicente, S. (2003). Aquifer overexploitation and desertification dynamics in the Guadalentín basin.

Available: http://digital.csic.es/bitstream/10261/74388/1/Envirowater2003%20final.pdf. Last accessed 2nd

October 2014.

Zhao, J. (2007). Chapter 4 - Properties of Rock Materials. In: Rock Mechanics for Civil Engineers.

Lausanne, Switzerland: Swiss Federal Institute of Technology. Chapter 4, p1-7.


Appendices

Appendix A – Channel cross-section for each site

Appendix B – Sampled Sediment Measurements (sorted)

Appendix C – Assorted Computed dataset

Appendix D – Schist Wet Mass


Appendix A – Cross-section of six sites at Nogalte and six sites at Torrealvilla, showing the changing

channel morphology

0

1

2

3

0

1

2

0

1

2

0

1

2

0

1

2

0

1

2

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

Width of Channel (m)

De

pth

of

Ch

an

ne

l (m

)

Site

1

2

3

4

5

6

Width of Channel (m)

De

pth

of

Ch

an

ne

l (m

)

Site

1

2

3

4

5

6

0

1

2

0

1

0

1

2

0

1

0

1

0

1

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0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22


Appendix B – Sampled sediment measurements (B-axis)

Sediment B-Axis (m)

Day 1 Day 2

Site 1 Site 2 Site 3 Site 4 Site 5 Site 6 Site 1 Site 2 Site 3 Site 4 Site 5 Site 6 Site 7

1 0.001 0.001 0.001 0.002 0.002 0.002 0.003 0.002 0.003 0.015 0.003 0.012 0.004

2 0.002 0.002 0.004 0.003 0.003 0.004 0.019 0.00 0.011 0.017 0.01 0.015 0.013

3 0.003 0.003 0.004 0.003 0.005 0.005 0.02 0.004 0.012 0.019 0.012 0.02 0.014

4 0.004 0.004 0.006 0.004 0.008 0.005 0.03 0.005 0.012 0.019 0.017 0.031 0.016

5 0.004 0.006 0.007 0.004 0.021 0.005 0.035 0.007 0.01 0.021 0.018 0.033 0.02

6 0.006 0.007 0.008 0.008 0.023 0.006 0.036 0.007 0.021 0.022 0.018 0.035 0.024

7 0.006 0.008 0.009 0.008 0.024 0.006 0.045 0.007 0.021 0.024 0.02 0.041 0.027

8 0.007 0.009 0.011 0.009 0.026 0.007 0.047 0.007 0.027 0.025 0.027 0.042 0.044

9 0.012 0.009 0.017 0.012 0.05 0.009 0.053 0.009 0.031 0.026 0.027 0.057 0.048

10 0.012 0.011 0.022 0.012 0.051 0.012 0.053 0.012 0.032 0.031 0.027 0.063 0.059

11 0.014 0.012 0.032 0.012 0.057 0.012 0.088 0.013 0.033 0.03 0.028 0.07 0.061

12 0.016 0.013 0.037 0.013 0.06 0.014 0.104 0.013 0.042 0.038 0.029 0.072 0.063

13 0.016 0.016 0.043 0.014 0.063 0.015 0.115 0.019 0.047 0.042 0.03 0.081 0.066

14 0.021 0.017 0.05 0.018 0.064 0.022 0.094 0.014 0.048 0.054 0.03 0.087 0.067

15 0.021 0.02 0.051 0.022 0.064 0.025 0.122 0.022 0.052 0.056 0.04 0.094 0.069

16 0.022 0.02 0.052 0.042 0.071 0.033 0.154 0.022 0.062 0.06 0.046 0.11 0.07

17 0.026 0.022 0.057 0.052 0.072 0.034 0.17 0.028 0.062 0.062 0.055 0.11 0.087

18 0.027 0.041 0.07 0.063 0.081 0.042 0.172 0.042 0.072 0.063 0.062 0.122 0.088

19 0.03 0.042 0.071 0.074 0.083 0.073 0.174 0.042 0.086 0.064 0.087 0.154 0.101

20 0.03 0.052 0.082 0.08 0.091 0.081 0.182 0.042 0.112 0.083 0.09 0.156 0.167

21 0.036 0.053 0.108 0.087 0.101 0.094 0.182 0.05 0.122 0.096 0.097 0.157 0.169

22 0.037 0.056 0.121 0.101 0.122 0.102 0.209 0.054 0.188 0.132 0.099 0.17 0.173

23 0.04 0.079 0.132 0.102 0.13 0.123 0.284 0.061 0.189 0.144 0.104 0.183 0.19

24 0.054 0.089 0.134 0.131 0.171 0.138 0.304 0.062 0.273 0.159 0.18 0.185 0.284

25 0.062 0.251 0.154 0.178 0.364 0.14 0.566 0.154 0.631 0.432 0.341 0.72 0.55

AVG 0.02036 0.03372 0.05132 0.04216 0.07228 0.04036 0.13044 0.02804 0.08808 0.06948 0.06 0.1128 0.09904


Appendix C – Assorted computed dataset

CSA WP Slope Rough Velocity Discharge ASL HR SC (kg/min)

Max bank height Max Width

Total check infill m^3

Time to fill

NG S1 7.49 12.22 0.0033 0.3 0.139 1.04 0.02 0.612638 430.30 1.9000000 7.2200000 8145.1 18.93

NG S2 13.22 22.92 0.0003 0.6 0.021 0.28 0.03 0.576789 115.39 0.9400000 18.7000000 5163.5 44.75

NG S3 9.01 22.23 0.0173 0.6 0.120 1.08 0.05 0.405139 448.03 0.8200000 18.6500000 3918.8 8.75

NG S4 13.73 26.29 0.0143 0.5 0.155 2.13 0.04 0.52212 882.37 1.4300000 21.0900000 13477.2 15.27

NG S5 14.44 23.67 0.0167 0.2 0.464 6.71 0.07 0.610161 2776.51 0.8800000 20.1000000 4864.2 1.75

NG S6 17.64 20.25 0.0037 0.3 0.184 3.25 0.04 0.87119 1344.91 1.4400000 15.9000000 10303.2 7.66

TV S1 14.20 15.63 0.0150 0.2 0.574 8.16 0.13 0.90867 3377.03 1.9400000 10.4000000 12231.7 3.62

TV S2 31.60 33.08 0.0133 0.3 0.373 11.80 0.03 0.955096 4883.15 1.5000000 29.1000000 20460.9 4.19

TV S3 29.58 27.82 0.0357 0.5 0.393 11.64 0.09 1.063031 4817.31 1.7630000 22.3000000 21660.1 4.50

TV S4 16.60 17.75 0.0190 0.5 0.264 4.38 0.05 0.935374 1812.15 1.7900000 13.1500000 13166.8 7.27

TV S5 33.58 33.58 0.0147 0.5 0.242 8.13 0.06 0.999873 3366.73 2.3500000 24.6000000 42454.2 12.61

TV S6 9.46 17.81 0.0037 0.4 0.099 0.94 0.09 0.531397 389.15 1.1100000 13.7000000 5274.9 13.55


Appendix D – Schist Wet Mass

Schist Mass (kg)

Site 1 Site 2 Site 3 Site 4 Site 5 Site 6 Site 1 Site 2 Site 3 Site 4 Site 5 Site 6

1 0.000003 0.000000 0.000001 0.000011 0.000011 0.000011 0.000039 0.000011 0.000039 0.004826 0.000039 0.002471

2 0.000023 0.000023 0.000092 0.000039 0.000039 0.000092 0.009808 0.000039 0.001903 0.007026 0.001430 0.004826

3 0.000077 0.000077 0.000092 0.000039 0.000179 0.000179 0.011440 0.000092 0.002471 0.009808 0.002471 0.011440

4 0.000183 0.000183 0.000309 0.000092 0.000732 0.000179 0.038610 0.000179 0.002471 0.009808 0.007026 0.042601

5 0.000183 0.000618 0.000490 0.000092 0.013243 0.000179 0.061311 0.000490 0.003142 0.013243 0.008340 0.051390

6 0.000618 0.000981 0.000732 0.000732 0.017399 0.000309 0.066718 0.000490 0.013243 0.015227 0.008340 0.061311

7 0.000618 0.001464 0.001042 0.000732 0.019768 0.000309 0.130309 0.000490 0.013243 0.019768 0.011440 0.098557

8 0.000981 0.002085 0.001903 0.001042 0.025134 0.000490 0.148467 0.000490 0.028147 0.022344 0.028147 0.105946

9 0.004942 0.002085 0.007026 0.002471 0.178750 0.001042 0.212894 0.001042 0.042601 0.025134 0.028147 0.264826

10 0.004942 0.003807 0.015227 0.002471 0.189691 0.002471 0.212894 0.002471 0.046858 0.042601 0.028147 0.357567

11 0.007848 0.004942 0.046858 0.002471 0.264826 0.002471 0.974505 0.003142 0.051390 0.051390 0.031391 0.490490

12 0.011715 0.006283 0.072434 0.003142 0.308880 0.003924 1.608556 0.003142 0.105946 0.078467 0.034876 0.533745

13 0.011715 0.011715 0.113695 0.003924 0.357567 0.004826 2.174851 0.009808 0.148467 0.105946 0.038610 0.759961

14 0.026486 0.014051 0.178750 0.008340 0.374866 0.015227 1.187735 0.003924 0.158147 0.225174 0.051390 0.941659

15 0.026486 0.022880 0.189691 0.015227 0.374866 0.022344 2.596663 0.015227 0.201069 0.251131 0.091520 1.187735

16 0.030453 0.022880 0.201069 0.105946 0.511813 0.051390 5.222738 0.015227 0.340809 0.308880 0.139190 1.903330

17 0.050267 0.030453 0.264826 0.201069 0.533745 0.056205 7.025590 0.031391 0.340809 0.340809 0.237916 1.903330

18 0.056293 0.197114 0.490490 0.357567 0.759961 0.105946 7.276481 0.105946 0.533745 0.357567 0.340809 2.596663

19 0.077220 0.211892 0.511813 0.579470 0.817655 0.556294 7.533274 0.105946 0.909560 0.374866 0.941659 5.222738

20 0.077220 0.402139 0.788456 0.732160 1.077607 0.759961 8.620852 0.105946 2.009047 0.817655 1.042470 5.428875

21 0.133436 0.425788 1.801388 0.941659 1.473330 1.187735 8.620852 0.178750 2.596663 1.265172 1.305122 5.533947

22 0.144868 0.502262 2.533332 1.473330 2.596663 1.517527 13.054940 0.225174 9.501881 3.288954 1.387528 7.025590

23 0.183040 1.410092 3.288954 1.517527 3.141710 2.661040 32.756015 0.324583 9.654315 4.269957 1.608556 8.763736

24 0.450347 2.016211 3.440729 3.214770 7.150302 3.758143 40.175084 0.340809 29.095376 5.748141 8.339760 9.054224

25 0.681618 45.225898 5.222738 8.064845 68.966818 3.923920 259.289739 5.222738 359.272615 115.288842 56.702104 533.744640 Total Load 1.981582 50.515923 19.172137 17.229169 89.155554 14.632213 399.010365 6.697547 415.073956 132.942737 72.416427 586.091598

Lorca Field Report

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Transcript of Lorca Field Report