UvA-DARE (Digital Academic Repository) Geomorphology … · Mombasa, Kenya. Sand transport rates, wind speed and direction were measured on the berm zone adjacent to the beach

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Geomorphology of the Malindi Bay coastal sand dunes

Abuodha, J.

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Citation for published version (APA):Abuodha, J. (2000). Geomorphology of the Malindi Bay coastal sand dunes Amsterdam: UvA FysischeGeografie

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CHAPTER 4

Field experiments on aeolian sand transport

ABSTRACT

From January 1993 to December 1993 experiments were carried out to quantify the magnitude and direction of sand blown by wind from the beach to the coastal dunes at Malindi, about 120 km north of Mombasa, Kenya. Sand transport rates, wind speed and direction were measured on the berm zone adjacent to the beach. Arens' omnidirection vertical sand traps were used to measure the vertical distribution ofsal tat ing flux, provide qualitative insight into the importance of creeping sand and grain size response to wind conditions operating in the field. Wind speed and direction were respectively measured using a cup anemometer and a windvane both placed at about 6.5 m height, and the data was automatically recorded, processed and stored in a CR10 programmable datalogger.

Along the Malindi Bay coast, the amount of creeping material was observed to be more important in the total mass flux, perhaps in proportions higher than suggested in wind tunnel studies by other authors such as Bagnold (1954) and Visher (1969). Visual observations and trap measurements indicated that sand transport near the surface was frequently higher than the estimated saltation.

The results of grain size distribution analysis of the trapped sediments generally showed a pattern of increase in the percentage of small grains and a reduction in sorting with increasing height above the grain bed. However, at higher wind speeds some coarser grains were transported above the 10 cm sand collector, finer grains tended to be transported near the surface and sorting variations with height became increasingly complex, probably as a result of the change in transport mechanisms.

The results of sand transport rates showed a good relationship between the fifth power of wind strength above threshold and the amount of saltating sediment caught. An empirical formula for this relationship is proposed based on the regression results to show the basic trend of sediment transport at Malindi Bay for the whole year; qs - 0.0013(Wind vector) - 257.665, with r-value = 0.90, n = 17, and correlation is significant at p < 0.05.

The results of vector analysis of the hourly average wind speed and direction for the whole year suggested that the winds from a southerly direction dominated sand transport processes at Malindi Bay during 1993, whereas winds from the northeast which were of relatively low speed and effective for a very short period of less than three months were less important in the annual shifting of sand. The described wind climate when compared with long term wind data for the Malindi area reported by the Kenya Meteorological Department (1984), and considering the distribution of dunes in the north and south of the Sabaki river, the results seem to suggest that 1993 was anomalous.

FLORISTIC COMPOSITION AND VEGETATION ECOLOGY 11

INTRODUCTION

Dunes existing on the immediate landward side of the shoreline provide coastal defence against erosion, are sites for water extraction and are renowned for their unique natural values and aesthetic features. The contemporary concept of coastal zone management regards dunes as an integral dynamic feature of coastal protection. Basic data on dune dynamics are therefore necessary in formulating action plans for their management and conservation to mitigate deleterious effects resulting from increasing agricultural, recreational, urban and industrial pressures. In addition, dunes are threatened by coastal erosion arising from the postulated sea level rise. On the other hand, the advance of mobile sand into cultivated areas and settlements at Mambrui (including the township) require artificial stabilization. It is therefore necessary to understand the dynamics of the coastal dune environment in order to manage sand movement and, if necessary institute counter measures.

Comparisons between the potential sand movement based on wind data with actual measured rates for the coastal environment have been attempted, though with mixed results, for example, Kuhlman (1958), Kubota et al (1982), Hunter et al. (1983), Rutin (1983), Illenberger & Rust (1988), Goldsmith (1989), Sarre (1989a), Kroon & Hoekstra (1990) and Arens (1994b). However, (i) none of these studies was done on a tropical shoreline, (ii) the tremendous contribution of the creep load has never been given due consideration and (iii) some of these studies lack a reliable reference for actual transport against which potential transport predicted by the conventional transport equations can be compared. Typical constraints to comparison of sand transport and wind velocity in the field are the determination of the actual value of threshold wind speed (Rutin, 1983; Arens, 1994b) and experimental errors arising from efficiency of traps (Arens & van der Lee, 1993).

Reliable sand transport rates are difficult to obtain in the natural field environment and many scientists have attempted to derive both empirical and theoretical formulae, which sometimes include such constraints as moisture content, salt crusts, sediment texture and density, creep/saltation ratios, beach width and slope, wind gustiness, vegetation, local accelerations, etc.

This research was conducted in order to determine:

1. The rate of sand transport and grain size distribution with height,

2. The effective dynamic factors operating during sand transport,

3. The relationship between wind speed and direction, and sand transport,

4. The dominant direction of sand transport for 1993, and evaluate the landward component of

sand transport vector.

STUDY AREA

The study area (Figure 4.1) is a coastal strip stretching from Malindi to Mambrui, a distance of approximately 10 km and which has a tropical climate. It is situated within the latitudes 3°06'S and 3° 12'S, and bounded westward by the transition zone from active to stabilized dune forms. The area is geographically set within a wide open bay near the mouth of the Sabaki river. The

78

bay is bordered eastward by long and wide (300-400 m), low-gradient beaches (intertidal zone) with an average gradient of about 1°. Reef patches and sheltered lagoons occur in the northern and southern extremities where narrow beaches (100-150 m) are found. The Malindi Bay coast is a rapidly prograding shoreline; the beaches are also characterized by swash bar and runnel features.

The dunefield is situated on the seaward fringe of the coastal plain which is also associated with complex dune ridge systems, prograding shorelines, raised coral reef complexes and marine platforms (terraces). Near Mambrui are developed large dune ridges.

The dunefield under consideration has an area of approximately 7 km2. The main components of the dune morphology are the foredunes, deflation planes, pockets of vegetated hummocky dunes and transgressive amorphous sand sheets. Mobile barchan dunes are found downwind of moist sebkha surface and migrating in conformity with the predominant wind. Further inland from the shoreline, barchanoid dunes change into transverse dunes, compound crescentic dunes and gently undulating sand sheets.

1 40P07 '

N

0 • , 1 1 1

Sabaki river

%^=&"*j£

/ / l _ — s .

— 3P10' jtC V

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MALINDI

\ ja t ty i i r . .J?ööf

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MAMBRUI A-V- . -VMBR/I I

/ J"-JMBR/IO

yj /MBR/Q //^»,^ /

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3K/4

^-^/ ShoreTnö

- ' " ' " " . Cad r©öfa

Trtnaöct

INDIAN

OCEAN

1 \ vc f i oo ib Gcma Pfcr |

Figure 4.1: Map of the study area showing sites of measurement.

The sediments transported from the beach into the aeolian system are fine to medium grained, ranging from 0.50o to 4.00o (average mean grain size is 2.150 or 0.22 in mm) and very well to poorly sorted, values ranging between 0.13 to 0.74o (average sorting is 0.39o, Chapter 6). The sand composition has notable quantities of heavy minerals particularly on the berm zone and dunes, where they are found as lag deposits (Abuodha & Nyambok, 1991; Chapter 6).


The main dune vegetation includes Cordia somalensis, Halopyrum mucronatum, Ipomoea pes-caprae, Tephrosia purpurea (dunensis), Scaevola plumieri and Vernonia homilantha.

Climate

The Kenya coast experiences an equatorial monsoon climate with southeast monsoon season operating from April to October and northeast monsoon season from November to March (Kenya Meteorological Department, 1984). The Malindi area has a humid climate with average rainfall of 1058 mm a"1 and a mean temperature of 26.2°C (Table 4.1). There are two rainy seasons. Rainfall maxima occur in April/May and October/November with over half the annual precipitation falling between April and June, during the southeast monsoon.

Malindi has an average of 92 rainy days per year (162 days in 1993, the days with trace, < 1 mm, are included to conform to the definition of the Kenya Meteorological Department); the precipitation is usually concentrated in showers which are correlated with strong winds. The total precipitation during the second wet spell in October/November is relatively small (see Table 4.1). The potential evaporation averages about 1904 mm a"1 and is nearly twice the mean annual precipitation.

Monthly variations in air temperatures are small as shown by long term meteorological data (Table 4.1). The peak air temperatures are usually experienced between March and April which have average maximum temperatures of 31-32°C, whilst the coldest months are usually July, August and September with average minimum temperatures of 21-22°C. Diurnal air temperature variations are usually within the range of 7-9°C, although the maximum and minimum temperatures recorded at Malindi are 35.6°C and 18.9°C respectively from 1962-1980. At Malindi the yearly mean of the relative humidity at 0300 hours, 0600 hours and 1200 hours are respectively 88, 78 and 70%.

Table 4.1 -.Annual long term average climatological data for the years 1962-1980 for Malindi area, Malindi Airport Meteorological Station (Kenya Meteorological Department, 1984).

Month

January February March April May June July August September October November December

Rain (mm)

ll 17 36 163 298 154 91 64 47 68 75 35

1058

No. of rainy days

2 2 3 11 17 12 12 9 7 6 6 3

92

Wind Speed ü m s'1

4.8 5.0 4.5 4.8 5.0 5.3 5.3 5.0 5.0 4.5 4.0 4.2 4.8

RH-0300 %

88 88 89 90 88 84 85 86 88 90 92 90 88

RH-0600 %

78 77 75 80 83 80 81 79 76 75 77 78 78

RH-1200 %

67 65 66 69 75 72 73 72 69 68 70 68 70

Sunshine (hrs)

9.4 9.2 9.2 7.2 7.1 7.6 7.7 8.4 9.1 9.3 9.4 9.6 8.6

Evaporat. (mm)

172 179 193 178 145 149 128 144 154 160 162 140

1904

Maximum temp.(°C)

30.8 30.9 31.8 31.1 28.8 27.9 27.3 27.4 28.3 29.6 30.5 30.8 29.6

Minimum temp(°C)

23.3 23.5 23.9 24.2 23.4 22.6 22.0 21.6 21.7 22.2 22.8 23.4 22.9

Solar radia. Wm-2

232 227 224 211 186 185 184 200 213 218 233 224 211

•Rainfall, number of rainy days and evaporation data are totalled whereas the rest are averages. RH = Relative humidity.

80

Table 4.2:Climatological data for the Malindi area collected at Malindi Airport Meteorological Station during 1993.

Month

January February March April May June July August September October November December

Rain (mm)

81.0 0.0 0.0

121.3 204.4 284.1

55.5 32.9 20.6 46.2 21.6 54.8

922.4

No. of rainy days

8 0 0

11 26 29 24 19 17 13 6 9

162

Wind Speed Q m s"'

4.4 5.0 4.5 4.6 5.1 4.6 5.9 5.7 5.7 5.0 3.8 3.9 4.8

RH-0600 %

82 77 68 81 84 50 77 80 74 76 71 75 75

RH-1200 %

72 64 60 66 73 47 73 69 66 70 66 67 66

Sunshine (hrs)

10.9 9.7 9.8 7.5 8.6 5.9 5.9 7.6 8.7 9.2 9.8 9.7 8.6

Ev aporat. (mm)

164 NA NA 149 130 NA NA NA NA NA NA NA

-

Maximum temp °C

30.4 31.4 32.8 31.9 29.8 28.3 27.2 27.3 28.3 29.4 31.4 31.6 30.0

M i) i m urn temp °C

23.8 23.9 25.3 25.0 24.6 23.2 22.0 21.9 22.1 23.1 23.2 24.3 23.5

Mean temp

°C 27.0 27.5 28.0 28.2 26.9 25.5 24.6 24.6 25.1 26.3 27.3 28.1 26.6

Solar radia. W i -i

216 239 201 203 195 160 175 182 202 206 231 213 203

* Rainfall and number of rainy days are totalled whereas the rest are averages. RH = Relative humidity.

Table 4.Z:Climatological data for the study area collected at station set on the beach at Mambrui between January 1993 to December 1993. Position of instruments are given in Figure 4.4.

Month

January Februarv March April May June July August September October November December Averages

Avg. RH-1 % 83.5 82.1 83.3 83.4 85.9 83.8 80.4 83.2 82.0 85.5 87.2 88.7 84.1

Max. RH-1 % 90.7 94.7 95.5 96.8 99.5 99.9 96.9 99.9 98.6 99.7 99.9 99.9 97.7

Min. RH-1 % 74.4 65.1 68.1 59.9 66.5 63.6 56.1 63.9 66.2 71.4 76.7 75.7 67.3

Avg. RH-2 % 86.8 86.3 86.4 NA NA NA NA NA NA NA NA NA 86.5

Max RH-2 %. 93.7 97.1 98.3 NA NA NA NA NA NA NA NA NA 96.4

Min. RH-2 % 74.7 65.7 64.0 NA NA NA NA NA NA NA NA NA 68.1

Avg. Tem.1

°C 28.0 28.0 28.4 29.0 27.7 26.4 25.9 25.5 25.8 26.6 28.0 27.8 27.3

Max. Tem.1

°C 30.3 30.8 31.1 31.0 30.9 29.9 28.5 27.7 28.0 28.3 29.6 29.7 29.7

Min. Tem.1

°C 24.8 24.4 23.6 24.9 23.8 21.3 21.2 21.9 21.8 23.1 23.9 24.9 23.3

Avg. Tem.2

°C 27.9 28.0 28.4 NA NA NA NA NA NA NA NA NA 28.1

Max. Tem.2


Min Tem.2


Solar rad. VV m2

226.0 233.0 221.0 204.0 NA NA NA NA NA NA NA NA 221.0

Wind speeds ms-' 7.61 5.45 5.46 6.25 6.70 8.23 8.65 7.89 7.67 6.85 5.49 5.93 6.85

*RH = Relative humidity. February = has lowest wind speed July = has highest wind speed

Tables 4.1 and 4.2 allow comparison of long term average values of weather parameters with what was obtained in 1993. The wind depends on monsoonal air currents. The strongest winds are produced during the southeast monsoon (Figures 4.2, 4.3a and 4.3b). The data, to be convincingly extrapolated to long periods of time, was put in the context of long term climate data for the study area. Apart from the number of rainy days and the wind climate, 1993 seems to be a normal year, with 13% less rain, 0.5°C warmer and relative humidity 3% less (Tables 4.1 and 4.2).

When the data for the Malindi Airport Meteorological Station is compared with those obtained from the beach at Mambrui (Table 4.3), it appears that the beach has much more wind, is slightly cooler and has a higher relative humidity. The discrepancy is not surprising considering geographic difference since the former station is located 3 km inland (Malindi Airport Meteorological Station is situated about 10 km south of Mambrui and about 3 km inland along

81 FLORISTIC COMPOSITION AND VEGETATION ECOLOGY

the Malindi-Mombasa road).

9.0

8.5-

8.0-

" 7.5-E

"2 7.0-CD

5. •a 6.5 H

c

6.0-1

5.5-

5.0

m—~d

Jan. Feb. Mar. Apr. May Jun. JuL Aug. Sep. Oct. Nov. Dec. Month

Figure 4.2: Monthly average wind speeds measured at Mambrui during 1993. The strongest winds are experienced in July.

a.

•***«.-: • • " :

150 200 250 300 Wind direction (degrees)

350 400

82

b.

July

August

September

October

November

December

0 50 100 150 200 250 300 350 400 Wind direction (degrees)

Figure 4.3: Prevailing wind direction (in degrees) during 1993 for the period ranging: (a) January-June and (b) July-December.

METHODS

The one year field experiments ranging from January 1993 to December 1993 involved continuous monitoring of meteorological parameters and periodic measurement of aeolian sand transport rates. The instruments were positioned at the beach-dune interface, on the sub-horizontal berm zone (Figure 4.4).

Measurement of meteorological parameters

Time averaged wind speed and direction data were simultaneously collected for 12 months (January 1993-December 1993) and consisted of measurements made 24 hours daily from the shore. A meteorological station was set up, consisting of an 8 m long pylon mast located just above the mean high tide level. During the normal flood tides, sea water level did not reach the mast, but during spring tides it reached the base of the mast. Because of this (seawater), and due to the deleterious effect of transported sand particles, all instruments were set above 1 m height from the surface. The general beach profile and the arrangement of instrumentation are shown in Figure 4.4. The signals were recorded by a datalogger every five seconds, and this information was subsequently processed and stored as one-hour averages. The maximum and minimum values of wind speed, temperature and humidity during this period were also recorded.


S P Z ^

ROTfJ-

VANFA

Beach^^^^"^

n SLM

M O p CAM Legend

CAM

VANE

ROT

SLM

SP

Cup anemometer at 6.5 m

Wind vane at 6.5 m

Rotronic at 7.77 m

Solarimeter at 8.0 m

Solar panel at 8.0 m

Dune

Figure 4.4: Beach profile and arrangement of instrumentation at Mambrui.

The wind speed measurements were made using a micro-cup rotation anemometer placed at a height of 6.5 m in order to obtain a wind climate of the beach at Malindi Bay. A windvane was placed at the same height above the surface to measure wind direction. Occasionally the threshold wind speeds were determined by simultaneously recording instantaneous readings from the anemometer when the sand particles begin to move (see results of measurement of wind parameters at Mambrui). A rotronic at a height of 7.77 m was used to measure air temperature and humidity; a second rotronic placed at a height of 1.78 m operated only for the first three months; then it ceased due to malfunction. Solar radiation was measured using a solarimeter placed at the top of the mast, but this also ceased after four months due to malfunction (the hemispherical glass dome covering the detector cracked, perhaps due to the high tropical temperatures). Table 4.3 shows the monthly average values of wind speed, air temperature and humidity. For comparison of sand transport with each wind direction and wind power, vector analysis of winds stronger than 6 m s"1 was done to obtain the resultant effective wind vector during the period when the trap was set up. Once a function of sand transport in terms of wind power was established, it was then possible to compute vectors of wind data for the whole year and evaluate the onshore component of the resultant transport vector with respect to the shoreline. In addition, wind roses were drawn by means of the computer program WINDROOS (van Boxel, 1990).

Measurement of sand movement using traps

The type of vertical trap used (Figure 4.5) is described in Arens & van der Lee (1993). It consists of a catching part and a storage compartment; when the storage area is full (the limit is approximately 600 g or 4 X 10"4 m3) the data is considered spurious. The height and diameter of

84

the traps was 25 cm and 10 cm respectively, consisting of 6 collectors placed at 5 cm interval (the first collector is flush with the surface).

The decision on trap height was based on the observation after initial trials with a higher sand trap, that in all measurements, zero amount of sand catch was realized at a height of 30 cm. Arens & van der Lee (1993) found from wind tunnel tests that the trap efficiency was about 15% based on an effective collector diameter of 10 cm, which in subsequent calculations is assumed to be constant throughout the wind speed range. This value was used in this study and assumed to apply to all sediment grain sizes constituting the saltation as well as the creep load under all field conditions. Mohamed (1995) showed that the efficiency of the "de Ploey-type" of vertical sand catchers was virtually height independent.

The traps were positioned on the berm zone, just above the high tide mark, between the Sabaki river and Mambrui; sand transport was measured only on the dates shown on Table 4.4a and Table 4.4b. These experiments were performed at least once a month to gain insight into whether seasonal variations in field conditions would affect sand transport rates. Although the exposure time was usually 24-30 hours, the effective duration (t) was considered to be that when the wind velocity exceeded the threshold of sand movement, i.e. 6 m s'1 at z = 6.5 m (further justification for application of this value is presented in the discussion).

The exposure time of the traps during the first four months of field measurements appeared adequate; the amount of sand caught in the storage area never exceeded the 600 g limit. In the period June-August, under peak conditions of strong southerly winds, this exposure time appeared to be too long, but then changing this time frame would have meant catching very little sand in the upper collectors for subsequent grain size analysis. As a result the lowest trap caught far too much sand most of which was blown off. In such cases, exclusive sand transport by saltation on the ground is assumed and predicted by extrapolating the log-linear profile through the data of the upper collectors downwards.

10 cm

I

\ h

I P 5 cm

20 cm

10 cm

9 cm

10 cm

I I

cm

Figure 4.5: Cross section of Arens' omnidirectional sand trap.

FLORISJ1C COMPOSITION AND VEGETATION ECOLOGY 85

Analysis of grain size distribution

Sediments trapped at different heights were used to determine the vertical grain size distribution. Three sand catching experiments were selected on the basis of:

1. sufficient material (more than 20 g) for sieve analysis, at least in the lower three collectors, 2. same position of trap, in this case the berm zone at location SBK/7, 3. close dates, in this case July-August, so that field conditions can be assumed to be constant, 4. wind speeds, selected to represent low (6-8 m s"1), moderate (8-9 m s'1) and high wind speeds

(above 10 m s"1); wind speeds were measured at a height of 6.5 m above the ground.

Large samples (larger than 100 g) were split. The samples were then washed repeatedly with distilled water to remove soluble salts, oven dried at 60°C, and sieved at quarter-phi intervals for 10 minutes using a Retscht mechanical shaker and a nest of 15 sieves. The mass frequency data was subsequently processed using a PC grain size package GAPP (Fay, 1989) which calculates both moment and graphical statistical parameters (Folk & Ward, 1957). The grain size distribution data was then correlated with wind speeds and height of traps above the surface to investigate the mechanism of sand transport from the beach into the dunefield.

RESULTS

Measurement of wind parameters at Mambrui

Figure 4.6 shows the average hourly distribution of wind speeds and direction for February and July 1993. The curves show fluctuations resulting from the influence of land and sea breeze, with the largest daily changes noticeable in February. In February and July, minimum wind speeds, respectively 3.9-4.2 m s'1 and 7.3-7.7 m s\ were recorded in the morning between 0800-1000 hours and wind directions were respectively from 111-157° and 191-196°. The effect of sea breeze is noticed towards midday, reaching a peak during the afternoon/early evening. Wind speeds were 6.5-7.9 m s'1 between 1500-2100 hours in February and above 8.0 m s"1 after 1200 hours in July and the wind directions were respectively 81-89° and 185-187° (wind speeds were 4.5-5.8 m s"1 and directions were 54-77° between 1000-1500 hours in February). Figure 4.6 suggests some influence of land breeze which develops at night, reflected by a decrease in wind speed from midnight till about 1000 hours (both in February and July); wind directions got predominantly more easterly for February and veered slightly to the 178-179° range for July.

The general wind climate obtained at Mambrui is presented as a wind rose diagram in Figure 4.7 using a computer package developed by van Boxel (1990). The diagram was plotted from mean hourly wind data consisting of 24 compass directions, each covering 15 degrees, and 12 wind speed classes. Each circle represents 5% of the total frequency of wind directions per year while the widths of the roses depict wind speeds; the lengths of segments represent wind speed frequency (% hours) that prevailed from a given direction. For example, the total percentage wind blowing from the southeast was 10.9% and that blowing from the south was 23.1%, and the highest wind speeds (above 12 m s~') occurred in the sector ranging from 165-180°.

86

o o *0

TJ

c

-1 1 1 1 — i 1 1 1 1 1 1 1 1 1 1 1 1 1 1 r

O 300 600 900 1200 1500 1800 2100 240Ó" Hour

FEB. WSP FEB. WDR — JULY WSP - — JULY WDR

Figure 4.6: Average hourly distribution of wind speeds and direction showing the effects of land and sea breeze for (a) February 1993 and (b) July 1993.

Figure 4.7: Wind rose for Malindi Bay area showing the annual distribution of wind velocity and direction from data collected at Mambrui during 1993.


Figures 4.3a and 4.3b illustrate that the annual wind direction distribution was predominantly bimodal; the two populations represent the southerly and northeasterly winds during the "southeast" and "northeast" monsoon periods. Although the terms southeast and northeast monsoon are widely (and loosely) used in the meteorological descriptions of trade winds for the East African coast and simultaneously to denote the two main seasons, they are not exactly synchronous with the observed wind directions. As the wind direction distribution logged in at Malindi during 1993 closely agreed with those from Mambrui, the difference between our results reported here and the above descriptions cannot be blamed on an error in the orientation of the vane. Moreover, the orientation of the vane was checked frequently; it did not change throughout the duration of field measurements. In January and February 1993, the wind blew consistently from the northeast-east. In the second week of March, the character of the wind began to change as the direction veered more toward easterlies, until in early April, the wind began to blow out of the southeast-southwest quadrants. It remained fairly consistent from that direction until November when a gradual transition back to northeast-east began. Approximately 83.4% of the wind blew from southeast-southwest quadrants, whereas only 15.8% blew from the northeast quadrant; Figures 4.3a and 4.3b illustrate the dominance of the southeast-southwest winds during 1993.

The average wind speeds for the northeastly and southerly winds are 6.1 m s'1 and 7.1 m s" respectively. The season associated with the southerly winds occasionally experienced strong wind conditions when wind speeds exceeded 12 m s'. The average of the hourly wind speeds above 6 m s"1 for the whole year is 7.9 m s"1. From these wind speed measurements it can be seen that the southerly winds were stronger, and the duration of these higher wind speeds was longer. Northeastern winds were usually of low velocities and prevailed for a duration of less than three months. Wind data for 1993 shows that the maximum monthly average wind speed occurred in July (Figure 4.2). It was established that 69.5% of the measured wind speeds for 1 year were above threshold conditions (sand moving winds) of which 61.8% of the sand moving winds blew from the southern sector, whereas 8.7% blew from the northern sector. The distribution frequency (Table 4.5a in a subsequent section; Figure 4.7; Figure 4.8) also shows that the wind speeds ranging from 6 to 12 m s"1 were essentially the most effective winds in terms of aeolian sand movement at Malindi Bay, totalling 6084 hours in 1993, whereas winds stronger than 12 m s~' prevailed for only 6 hours in 1993.

25 -] ~ I

20 - -y.\.

i_ J. \ s y

,* h

| , , , , 1 ^ - m— 4. 6 8 10 12 14-

Wind speed ms-1

-= I u

Figure 4.8: Frequency distribution of the number of hours in each wind speed class, for 1 year.

Assessment of sand transport using sand traps

Table 4.4a shows the amounts of sand caught at various heights below 30 cm and the periods during which the trap measurements were taken. These results demonstrate a consistent exponential decrease of sediment caught with height above the surface; this was a manifestation of sand particle behaviour during transport in the saltation mode. This relation as presented by van Dijk (1990) can be expressed as the following log-linear equation:

lnq. = lnqü-az (4.1)

where, q,, = amount of catch (g) at height z (m) q0 = amount of catch (g) at ground level z = height above the surface (mid-height between successive collectors) a = gradient of sediment profile

Sand transport by saltation (Table 4.4a) at the surface is calculated by extrapolating data of the upper collectors downwards, leaving out the actual catch of the lowest tray. Exception is experiment no. 20 for which the actual data in the lower two trays are not considered because both were too full; therefore they were both predicted. Table 4.4b shows that the intercept on the In q axis (In q0 at the effective catching height of the lowest collector) ranged from -0.33 to 8.91 and averaged 5.07. For the 32 traps, the regression analysis gave a fairly good correlation between lno^ and z, ranging between 0.89 and 1.00 (Table 4.4b). The slopes of the sediment curves, a, ranged from 5.33 to 61.41 and averaged 24.93 (Table 4.4b).

Table 4.4a shows the weights (expressed in grammes) caught during the 32 sand trapping experiments set at least once a month (except May and November) for the period January-December 1993. The data shows that the highest total amount of sand caught in a trap column was 69 g/hour (experiment no. 20, representing a transport rate over a 25 hour effective period) in August when the average wind speed exceeded 10 ms'1. The lowest amount caught was 0.61 g/hour (experiment no. 9, representing a transport rate over a 12 hour effective period) in April when the mean wind speed was less than 7 ms"1.

Data obtained from the lowest bucket (Table 4.4a, column 4) of these two extreme results indicate that a high of 36 g/hour and a low of 0.48 g/hour were respectively caught. It is obvious that in experiment no. 20 the trap was exposed for far too much time during which the storage capacity of the lowest two collectors was exceeded and much of the sand was probably blown off. Thus at the surface, for experiment no. 20 (column 5), a transport rate of 95 g/hour is obtained by entering the effective height of the lowest collector in the regression equation. For the period January 1993 to December 1993 the average amount of sand caught in all the traps was 29 g/hour although the amounts caught varied considerably over this period. A slightly higher average of 39 g/hour is obtained if the catch in the lowest collector is substituted with the more reliable saltation data in cases where the storage capacity is exceeded and/or blowout is evident. However, both values are too low because creep data are missing for days with strong winds when creep is supposed to be highest. At low to moderate wind speeds it is still probable that the edge of the lowest collector deflects some creeping grains. This is more so during strong winds when the amount of grains moving close to the surface tends to increase (as is indicated by the correlation in Figure 4.9).


70

6 0 -

. 5 0 -

N 40

o 30 H

2 0 -

10-

Equation of regr. line: c< = 16.02u - 98.77

r-vabe = 0.65 for 30 cases is significanf*af p < 0.05

JL •

6.5 7.0 7.5 8.0 8.5 Avg wind speed ms-1/6.5 m above surface

9.0

Figure 4.9: The relation between wind velocity and the slope of logarithmic vertical sediment distribution.

Differences in sediment curves are noteworthy. A few examples of such differences are illustrated in Figure 4.10.

1. In most cases (unmarked experiments including 14 and 31 in Table 4.4a; Figure 4.10), the actual amount caught in the lowest collector showed higher values than the estimated saltation and there was no indication of blowout. Probably transport by creep becomes more important or the catching efficiency is higher in the lowest collector (highest % in experiment no. 25) because of lower wind speeds near the surface. However, no quantitative account can be made on creep because this method was inadequate.

2. In Table 4.4a, all dates marked + showing more than 600 g in the lowest collector are the dates when the sediment transport rate at the beach was very high and the exposure time was too long. As a result, the storage capacity of the lowest collector was exceeded and loss by blowout was noticeable. Usually, loss by blowout was evident on days when the mean wind speed exceeded 7.5 m s"1. Blowout reduces the possibility of an accurate calculation of the sediment caught in the lowest collector.

3. Although experiments 14 and 31 (Figure 4.10) show that the lowest collector caught too much sand, there is no indication of blowout. This is possibly due to trap defects resulting from the fabrication technique, which may result in a slightly larger collector and/or the observed topographic changes during sand transport, but an adequate explanation cannot yet be given.

4. The dates marked * in Table 4.4a are the dates showing overestimation of transport by saltation than the actual amount caught in the lowest collector, because there is not enough material left in the lowest collector when extrapolating saltation downwards from the upper trays. This can be attributed to blowout and/or overexposure.

90

Table 4.4a: Data of sediment (caught at various heights) traps placed on the berm zone along the Malindi Bay shoreline. Results of wind parameters are also included.

Experiment No

1

2

3

4

5

6

7

cS

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

Code

129301-1

129302-1

099303-1

129303-1

099303-2

099303-3

079303-1

079304-1

099304-1

129304-1

129306-1

129307-1

099307-1

079307-1

079308-1

129308-1

099308-1

079308-2

099308-2

099308-3

079308-3

099309-1

129309-1

079309-1

099309-2

129309-2

079309-2

129309-3

079310-1

129310-1

099310-1

129312-1

Date

31/01/93

15/02/93

03/03/93

03/03/93

13/03/93

30/03/93

30/03/93*

14/04/93

14/04/93

21/04/93

03/06/93*

09/07/93

09/07/93

0.025 actual

523.22

539.55

358.20

361.00

64.43

232.96

371.15

81.99

5.76

559.86

535.17

290.39

545.54

09/07/93+ 687.82

05/08/93

05/08/93

05/08/93

13/08/93*

415.54

435.25

259.86

+741.22

13/08/93+ 675.84

24/08/93 *+901.48

24/08/93*

06/09/93

06/09/93*

06/09/93

15/09/93

15/09/93

15/09/93

27/09/93

04/10/93

04/10/93

+890.50

290.37

446.97

467.13

427.56

517.15

503.03

430.41

446.16

574.00

04/10/93+ 680.50

15/12/93 317.47

height z 0.025

(m)and w 0.025

actual/est. est.

523.22

539.55

358.20

361.00

64.43

232.96

1295.95

81.99

5.76

559.86

1293.04

343.78

545.54

533.52

415.54

435.25

259.86

1874.79

579.98

2383.92

3249.48

290.37

459.21

467.13

427.56

517.15

503.03

430.41

446.16

574.00

303.54

317.47

103.54

112.25

63.01

23.84

2.63

5.42

1295.95

3.22

0.63

171.06

1293.04

139.70

255.19

533.52

167.59

38.53

91.49

1874.79

579.98

2383.92

3249.48

36.51

459.21

42.02

5.44

66.22

112.56

40.41

45.80

27.91

303.54

10.65

eight ca 0.075 actual

55.03

27.67

32.02

5.23

2.20

4.17

332.54

1.71

0.49

45.43

65.45

29.05

148.57

294.54

53.87

30.55

27.78

315.99

123.57

627.84

504.33

32.01

204.94

35.48

4.38

44.67

30.56

15.70

16.57

17.41

96.41

6.66

*ght (g) 0.125 saltation

6.37

6.65

3.04

0.09

0.28

0.91

43.71

1.17

0.37

9.48

1.79

2.43

21.51

28.73

5.87

2.83

3.86

36.98

11.23

149.67

124.40

2.34

14.58

3.15

1.78

6.35

7.38

6.28

5.47

4.40

21.07

6.15

0.175 load

1.85

1.82

0.72

0.07

0.07

0.40

15.62

0.58

0.28

2.88

0.14

0.79

8.30

8.42

1.26

0.38

1.23

3.37

1.45

22.73

26.60

0.53

3.04

0.68

1.73

1.20

1.20

2.83

3.19

1.76

8.55

3.18

0.225

0.64

0.58

0.24

0.00

0.12

0.33

9.39

0.49

0.22

1.57

0.00

0.00

7.68

8.46

0.44

0.79

0.47

0.73

0.35

13.19

15.17

0.63

0.74

0.63

0.94

1.22

0.35

0.96

1.77

1.26

5.36

0.00

total q, 0.275 saltation

0.43

0.10

0.16

0.00

0.04

0.19

0.47

0.18

0.17

0.14

0.00

0.00

1.71

1.48

0.10

0.24

0.07

0.12

0.06

only

167.86

149.07

99.19

29.23

5.34

11.42

1697.68

7.35

2.16

230.56

1360.42

171.97

442.96

875.15

229.13

73.32

124.90

2231.98

716.64

1.54 3143.68

0.38 3920.36

0.30

0.50

0.43

0.77

0.58

0.12

0.40

0.25

0.59

0.50

0.00

72.32

683.01

82.39

15.04

120.24

152.17

66.58

73.05

53.33

435.43

26.64

total q, catch actual/est

587.54

576.37

394.38

366.39

67.14

238.96

1697.68

86.12

7.29

619.36

1360.42

376.05

733.31

875.15

477.08

470.04

293.27

2231.98

716.64

3143.68

3920.36

326.18

683.01

507.50

437.16

571.17

542.64

456.58

473.41

599.42

435.43

333.46

effective duration hours (t)

9

12

16

16

8

11

11

12

12

21

17

24

24

24

23

23

23

25

25

25

25

29

29

29

28

28

28

21

22

22

22

8

Wind speed

at 6.5 m

7.81

7.28

8.10

8.10

7.06

7.46

7.46

6.64

6.64

7.57

8.12

7.79

7.79

7.79

7.76

7.76

7.76

8.61

8.61

10.14

10.14

8.06

8.06

8.06

7.73

7.73

7.73

8.03

7.42

7.42

7.42

7.07

Averages 455.55 646.05 423.10 101.03 16.89 3.96 2.33 038 547.68 770.64 2038 7.85

+ = Lowest collectors, and lower two in Exp. no. 20, were too full (with more than 600 g) to assure adequate trapping of sand. * = Estimated saltation load was more man the actual catch at the lowest collector due to excessive blowout

In such cases, the estimated values are adopted in subsequent calculations (column 5, 12 and 13).


Table 4.4b: Cumulative frequency % of amounts of sand trapped on the berm zone along the Malindl Bay shoreline. Results of curve fitting and transport rates are also presented

Equation 4.1

Experiment No. Code

1 129301-1 2 129302-1 3 099303-1 4 129303-1 5 099303-2 6 099303-3 7 079303-1 8 079304-1 9 099304-1 10 129304-1 11 129306-1 12 129307-1 13 099307-1 14 079307-1 15 079308-1 16 129308-1 17 099308-1 18 079308-2 19 099308-2 20 099308-3 21 079308-3 22 099309-1 23 129309-1 24 079309-1 25 099309-2 26 129309-2 27 079309-2 28 129309-3 29 079310-1 30 129310-1 31 099310-1 32 129312-1

Averages

Date

31/01/93 15/02/93 03/03/93 03/03/93 13/03/93 30/03/93 30/03/93 14/04/93 14/04/93 21/04/93 03/06/93 09/07/93 09/07/93 09/07/93 05/08/93 05/08/93 05/08/93 13/08/93 13/08/93 24/08/93 24/08/93 06/09/93 06/09/93 06/09/93 15/09/93 15/09/93 15/09/93 27/09/93 04/10/93 04/10/93 04/10/93 15/12/93

Cumulative perc. 5 cm

61.68 75.30 63.52 81.56 49.25 47.47 76.34 43.78 29.17 74.19 95.05 81.23 57.61 60.96 73.14 52.55 73.25 84.00 80.93 75.83 82.89 50.48 67.23 51.00 36.18 55.07 73.97 60.69 62.70 52.33 69.71 39.97

63.72

10 cm

94.47 93.86 95.81 99.45 90.45 83.98 95.92 67.11 51.86 93.90 99.86 98.13 91.15 94.62 96.65 94.22 95.49 98.15 98.17 94.05 95.75 94.75 97.24 94.07 65.30 92.22 94.05 84.27 85.38 84.98 91.85 64.97

89.75

if sand transported below: 15 cm

98.26 98.32 98.87 99.76 95.69 91.95 98.50 83.08 68.98 98.01 99.99 99.54 96.01 97.90 99.21 98.08 98.58 99.81 99.74 98.81 98.92 97.98 99.37 97.89 77.13 97.51 98.90 93.71 92.87 93.23 96.69 88.06

95.36

20 cm

99.36 99.54 99.60

100.00 97.00 95.45 99.42 90.98 81.95 99.26

100.00 100.00 97.88 98.86 99.76 98.60 99.57 99.96 99.94 99.53 99.60 98.71 99.82 98.71 88.63 98.50 99.69 97.96 97.23 96.53 98.65

100.00

97.84

25 cm

99.74 99.93 99.84

100.00 99.25 98.34 99.97 97.60 92.13 99.94

100.00 100.00 99.61 99.83 99.96 99.67 99.94 99.99 99.99 99.95 99.99 99.59 99.93 99.48 94.88 99.52 99.92 99.40 99.66 98.89 99.89

100.00

99.28

R-val

0.97 1.00 0.96 0.89 0.90 0.95 0.97 0.98 0.99 0.98 1.00 0.98 0.96 0.95 0.99 0.89 0.99 1.00 0.99 0.99 0.96 0.90 0.97 0.91 0.96 0.94 1.00 1.00 0.97 0.97 0.97 0.91

0.96

lnq„

5.24 5.38 4.80 4.25 1.41 2.05 7.90 1.44

-0.33 5.81 8.70 5.84 6.04 6.87 5.88 4.20 5.22 8.52 7.30 8.49 8.91 4.13 6.88 4.26 1.90 4.71 5.43 4.16 4.30 3.73 6.31 2.55 5.07

Slope a

24.00 26.37 26.27 43.14 17.72 14.38 29.32 10.86 5.27

26.72 61.41 36.02 19.92 23.62 30.34 21.94 28.15 39.35 37.48 28.54 32.95 21.30 30.02 20.87 8.23

20.68 28.26 18.44 19.03 16.04 23.78

7.39

24.93

Saltation kgm-'h-'

1.24 0.83 0.41 0.12 0.04 0.07

10.29 0.04 0.01 0.73 5.33 0.48 1.23 2.43 0.66 0.21 0.36 5.95 1.91 8.38

10.45 0.17 1.57 0.19 0.04 0.29 0.36 0.21 0.22 0.16 1.32 0.22

1.75

5. Overestimation of the amount caught in the lowest collector was noted in three unusual cases indicated by experiments 7 (Figure 4.10), 11 and slightly in 23 (also shows the least percentage difference). This occurrence was expected when the storage capacity of the lowest collector was exceeded. In this case also, the anomaly could partly be attributed to trap defects and changes in sand level, but a suitable reason is not yet possible to give.

92

1 0 0 0 3

o L.

'o

T)

a. CL D

100 =

0.00 0.05 0.10 0.15 0.20 trapping height (m above surface)

0.25 0.30

- » - E x p . 7

— » - Exp. 20

~ B ~ Exp. 14 -A:- Exp. 18

•••X- Exp. 25 - * - E x p . 31

Figure 4.10: Sediment curves for experiments 7, 14, 18, 20, 25 and 31 (Source: Table 4.4a).

Local factors such as terrain configuration, debris, heavy mineral concentrations (possibly also size and shape of grains) and measurement errors have led to notable differences in the amounts of sand caught. To reduce these effects somewhat, the mean catch for the sites for each duration was calculated (Table 4.4c) for comparison with wind conditions (Table 4.4d). One notes from the seventh column of Table 4.4d that the movement of sand is mainly to the southwest during January to March, when the northeast winds were dominant, and to the north during April to October, when the southerly winds were dominant.

The last five columns of Table 4.4d show the resultant wind vectors (RjX) for each period of trap exposure, each calculated from the x* power of hourly wind speeds above threshold (where x = 1, 2, 3, 4 and 5) and direction, and using the same methods as in Equation 4.3 and Tables 4.5a, 4.5b.


Table 4.4c: Average catch in g per trap calculated from table 4.4a for the effective duration of measurement.

Date

31/01/93 15/02/93 03/03/93 13/03/93 30/03/93 14/04/93 21/04/93 03/06/93 09/07/93 05/08/93 13/08/93 24/08/93 06/09/93 15/09/93 27/09/93 04/10/93 15/12/93

lowest actual

523.22 539.55 359.60 64.43

302.06 43.88

559.86 535.17 507.92 370.22 708.53 895.99 401.49 482.5S 430.41 566.89 317.47

lowest act/est

523.22 539.55 359.60

64.43 764.46

43.88 559.86

1293.04 474.28 370.22

1227.39 2816.70

405.57 482.58 430.41 441.23 317.47

lowest est

103.54 112.25 43.43

2.63 650.69

1.92 171.06

1293.04 309.47 99.20

1227.39 2816.70

179.25 61.41 40.41

125.75 10.65

height 0.075

55.03 27.67 18.63 2.20

168.36 1.10

45.43 65.45

157.39 37.40

219.78 538.49 90.81 26.54 15.70 43.46

6.66

and average 0.125

6.37 6.65 1.56 0.28

22.31 0.77 9.48 1.79

17.56 4.19

24.10 137.04

6.69 5.17 6.28

10.31 6.15

weight caught 0.175

1.85 1.82 0.40 0.07 8.01 0.43 2.88 0.14 5.84 0.96 2.41

24.66 1.42 1.38 2.83 4.50 3.18

0.225

0 61 0.58 0.12 0.12 4.86 0.35 1.57 0.00 5.38 0.57 0.54

14.18 0.67 0.84 0.96 2.80 0.00

(g) 0.275

0.43 0.10 0.08 0.04 0.33 0.17 0.14 0.00 1.06 0.14 0.09 0.96 0.41 0.49 0.40 0.45 0.00

Is (g)

167.86 149.07 64.21

5.34 854.55

4.75 230.56

1360.42 496.69 142.45

1474.31 3532.02

279.24 95.82 66.58

187.27 26.64

qt (8)

587.54 576.37 380.38 67,14

968.32 46.71

619.36 1360.42 661.51 413.46

1474.31 3532.02

505.56 516.99 456.58 502.75 333.46

qs kg m"'h

1.24 0.83 0.27 0.04 5.18 0.03 0.73 5.33 1.38 0.41 3.93 9.42 0.64 0.23 0.21 0.57 0.22

qt I kgm-lh-I

4.35 3.20 1.58 0.56 5.87 0.26 1.97 5.33 1.84 1.20 3.93 9.42 1.16 1.23 1.45 1.52 2.78

Mrs t

9 12 16 8 11 12 21 17 24 23 25 25 29 28 21 22 8

Wind speed

7.81 7.28

8.1 7.06 7.46 6.64 7.57 8.12 7.79 7,76 8.61

10.14 8.06 7.73 8.03 7.42 7.07

q s= total saltation, average of actual amounts in the upper collectors plus estimated saltation in all lowest collectors qt = total transport, average of actual amounts in the upper collectors plus estimated saltation only in lowest collectors

considered less reliable (marked + and * in Table 4.4a)

Table 4.4d: Effective wind parameters for the same periods when sand was trapped.

Date

31/01/93 15/02/93 03/03/93 13/03/93 30/03/93 14/04/93 21/04/93 03/06/93 09/07/93 05/08/93 13/08/93 24/08/93 06/09/93 15/09/93 27/09/93 04/10/93 15/12/93

"max ras"'

8.34 8.44 9.58 7.96 8.06 8.91 8.64 9.60 9.45 9.06

10.11 10.91 9.37 9.07 9.29 8.59 7.82

umin ms"'

6.63 6.23 6.21 6.25 6.27 6.01 6.27 7.24 6.13 6.43 6.70 8.26 7.07 6.46 6.47 6.09 6.43

"avg ms"'

7.81 7.28 8.10 7.06 7.46 6.64 7.57 8.12 7.79 7.76 8.61

10.14 8.06 7.73 8.03 7.42 7.07

YVDRmax in°

91.1 94.7 84.1

108.8 110.3 190.1 188.8 192.6 186.6 188.3 191.2 196.7 186.1 184.3 184.1 180.5 104.7

\VDRm i n

in° 46.4 49.1 42.6 96.8 53.2

133.9 177.8 174.1 154.5 168.3 163.7 164.7 174.4 168.1 160.9 158.2 55.6

WDR a v g

in°

83.1 81.3 66.9

102.3 83.3

152.5 184.2 179.6 169.1 177.7 175.7 184.6 179.7 178.5 172.6 168.3 77.5

WDR r a n g e

in° 44.7 45.6 41.5 12.0 57.1 56.2 11.0 18.5 32.1 20.0 27.4 32.0 11.7 16.2 23.2 22.3 49.1

WDRst(j Resultantwindvector

in°

13.3 14.1 12.2 4.1

20.9 18.4 2.9 4.7

10.4 6.0 6.1 8.4 2.8 3.9 7.6 5.8

17.7

R j i

68.3 84.9

126.7 56.4 76.8 75.2

158.8 137.5 184.0 177.5 214.0 250.8 233.4 216.0 167.1 162.4 53.9

V 537 626

1039 401 577 503

1210 1125 1454 1393 1855 2553 1889 1688 1354 1215 383

«iJ

4245 4667 8616 2871 4351 3415 9276 9270

11639 11050 16182 26074 15352 13330 11077 9169 2737

nf 33793 35128 72202 20701 32969 23655 71517 77041 94452 88586

142086 267132 125339 106348 91372 69688 19631

«J5

270536 266822 610884 150266 250854 167669 554368 645683 776783 717479

1255216 2744848

533261 141448 759563 533261 141448

Grain size composition of trapped sand

The grain size distributions of the sand trapped at different heights and wind speeds (at 6.5 m height) were examined. Three experiments set at location SBK/7 during the period July-August 1993 were selected (see methods). Percentages for each size class are plotted against mid-class values (Figure 4.1 la-c).

94

a. 90

80 H

Experiment no. 14, 0 9 / 0 7 / 9 3 , u = 7.79 m/s

1.50 1.75 2.00 2.25 2.50 2.75 3.00 3.25 3.50 3.75 4.00 Phi grain size (mid-class values)

0.025 m 0.075 m 0.125 m

Mean 1.890 Sorting 0.190

2.040 0.300

2.220 0.480

b.

fc$

90

80

70

SO

50H

'5 40

30

20

10

0 1

Exper iment no. 18, 13 /08 /93 , u = 8.61 m/s

(.-'

i

A / V /

1

1

V

\ \ \ \ \ \ \

^ \

•. S

— T 1 1 ' 1

,50 1.75 2.00 2.25 2.50 2.75 3.00 3.25 Phi grain size (mid-class values)

3.50 3.75 4.0C

0.025 m 0.075 m - — 0.125 m


1.930 0.210

2.290 0.300

FLOR1STIC COMPOSITION AND VEGETATION ECOLOGY

c. 80

70

SO

50 t?

x 40

30

20

10

Experiment no. 21 , 2 4 / 0 8 / 9 3 , u = 10.14 m/s

50 1.75 2.00 2.25 2.50 2.75 3.00 3.25 Phi groin size (mid-class values)

3.50 3.75 4.00

0.025 m 0.075 m - - - -0 .125 m


1.960 0.220

2.030 0.310

Figure 4.11: Frequency distribution of grain sizes for sand transported at below 15 cm height for (a) experiment no. 14 at 7.79 m S1, (b) no. 18 at 8.61 m s1 and (c) no. 21 at 10.14 ms1.

a. Experiment no. 14, 09/07/93, u = 7.79 m s'

The traps caught only a few grains coarser than 1.75cp. The grain size distributions of the samples were double peaked, with dominant peaks in the 1.75-2.000 class, and modest peaks in the 2.50-2.750 class.

Figure 4.11a shows that the percentage of the big grains (coarser than 2.00o) decreased with height, particularly between 10 and 15 cm. Consequently, the percentage of small grains (smaller than 2.OO0) increased with increase in height, so that above 10 cm they represented 27% of the catch.

Thus near the surface, there was a relative concentration of coarser sands, but at higher levels of sand transport, smaller grains dominated. Sand trapped in the lower two collectors practically show similar grain size distributions.

Comparison of sorting figures for different heights indicates that material became less well sorted with height at the lower 15 cm; from 0.19 0 at 0-5 cm to 0.48 0 at 10-15 cm above the surface, and reflects the selective character of aeolian transport.

96

b. Experiment no. 18,13/08/93, u = 8.61 m s1

Figure 4.11b shows that the modal size class was 1.75-2.000 in the lower 10 cm, above which the peak shifts to the class range 2.00-2.25o. Sand samples from the three collectors show a modest peak in the fraction 2.50-2.750.

In the lower 10 cm, all classes show distributions which are almost identical. Above this height, there is relative enrichment of smaller grains. Thus the mean grain size decreases.

The results of grain size distribution analysis indicate that at moderate wind speeds, sorting was constant in the lower 10 cm, then decreased above this height.

c. Experiment no. 21, 24/08/93, u = 10.14 m s'

The distribution of the sample caught near the surface was dominated by the 2.00-2.25o class, which constituted an average 36% of the total weight of sand caught. With increase in height the class 1.75-2.000 became more important and represent 72% of the total sediment catch at above 5 cm.

Consequently, the percentage of grains smaller than 2.00o decreased with increase in transport height. There was more similarity in the grain size distributions of the sand caught in the two collectors above the 5 cm height.

The transported sand near the surface (in the lower 5 cm) generally showed worse sorting than the sand caught in the upper collectors.

DISCUSSION

Distribution of sand in vertical trap columns

Contribution of creep and saltation fluxes

The sand surface of the dunefield at Malindi Bay was generally characterized by wind ripples which was a manifestation of the regular lengths of the leaps of the grains, mainly moving by small bounds, and the large size of the bed-load transport.

On average, 64% of sand consisting of a mixture of creep and saltation was transported below 5 cm height, although it might range from 29-95% depending on the force of the wind; about 99% of aeolian transport took place below 25 cm (Table 4.4b). From linear regression of log (weight) and height in preceding sections (Table 4.4a; Table 4.4b), it is evident that in about 80% of these studies, the predicted near-surface transport was underestimated by between 53% and 99%.

FLOR1ST1C COMPOSITION AND VEGETATION ECOLOGY 97

It is possible that:

1. the percentage creep is too high because the 15% efficiency does not apply to creep under our field conditions, and

2. the catching efficiency is higher in the lowest collector because of low wind speeds near the surface (Arens & van der Lee, 1993).

It has also been established by Rasmussen & Mikkelsen (1989) that the amount of sand moving close to, and on the surface was notably higher than was predicted by the log-linear extrapolation of the vertical sand distribution profile to the bed. This is one of a few studies where the importance of the creeping grain population has been given prominence. According to the definition of Anderson & Willets (1991), reptating and creeping grains constitute a great proportion of grains travelling close to, and in the sand bed. However, their conclusion that these modes contribute relatively little to the transport rate because of their short jump lengths is both intriguing and controversial.

During the study of sand movement in the northwestern Gezira (Sudan), Mohamed (1995) concluded that creeping sand did not show a convincing pattern of reproducibility under both wind tunnel and field results, even after the many modifications he made to his instrument. Mohamed (1995) also found that the creeping load increased as the saltating load decreased-verifying as we have stated below the limitations of Bagnold's (1954) classical notion that the movement of creeping grains is controlled by saltating grains.

Bagnold (1954), in his investigations of aeolian sand transport estimated that transport rates by saltation and creep were respectively -75% and -25% of the total load. He hypothesized that the creeping particles consistently obtain their forward momentum from the impact of saltating grains. It has also been suggested by Visher (1969), that saltation and creep populations respectively form 97-99% and 0-2% of the total transported sand in an aeolian environment. The trap data and visual observations suggest that the creep load constituted an important component of the total transport rates but the proportions were variable.

These results lead us to question the justification of assuming a consistent relationship between creep and saltation (1:3 according to Bagnold, 1954) or some other arbitrary ratios as many authors have previously done (Draga, 1983; van Dijk, 1990), as the properties of the saltation system on which the figures are based are still far from being well understood. According to Sorensen (1991) and Arens (1994b) the high concentration of grains close to the surface (in this study not exclusive of high wind speeds) results in decreased efficiency of collision, which probably invalidates the conventional concepts of aeolian sand transport mechanisms. The construction of traps used for these experiments did not permit accurate measurement of the creep load because of inherent problems associated with the trap fabrication, notably due to

1. obstruction by the edge of the lowest collector, 2. surface irregularities, 3. loss by blowout, and 4. catch creeping as well as reptating and saltating grains moving close to the surface.

As a result less creep is caught during days with strong winds when creep is supposed to be highest.

98

The high variability in the near surface transport even for similar wind conditions probably also reflected the importance of textural characteristics of surface sediment and presence of macroscopic bedforms and obstacles present on the berm zone. More field investigations are therefore necessary to separate saltating sand from creeping sand probably by using measuring systems that do not obstruct the wind flow, probably based on laser technology (Nickling & Ecclestone, 1981) to determine conclusively the patterns of sand transport. Mohamed (1995) has developed a simple theoretical method for separation of saltating sand from creeping sand based on the geometry of the trap opening and the way in which each mechanism of sand transport contributes to what is caught in the trap.

From these results, it became clear that the question of the main driving force behind the movement of surface creep needed further verification. Based on field observations (but more empirical data is needed for verification), the controlling factors in surface creep transport appeared to be direct wind stress, local speeding due to topographic irregularities, gusts, density of grains, bed slopes and of course, the saltation impacts. In future research, we propose that reliable measurement of these parameters should be made in order to develop a sensible physical justification for our empirical findings and give a more comprehensive physical account of grain activity close to, and in, the bed. A good starting point would probably be based on observations of Howard et al. (1978) who found that the creep motion deviated from the direction of wind stress and was perpendicular to ripple crests, in contrast to the movement of saltating grains which nearly paralleled the near-surface wind.

Vertical sand transport distribution

This study has found that for the Malindi Bay dunefield, the maximum height of sand transport on the berm zone appears to be 30 cm. The maximum wind speed for all the periods when traps were set was 11 m s"1 (Table A Ad). Field measurements of the height of sand transport on flat terrains in the dunes and the berm zone by Rutin (1983) and van Dijk (1990) respectively established that the maximum heights were 60 cm and 25 cm. It is apparent that Rutin's (1983) experiments were carried out under high wind speed conditions, ranging up to 24 m s', while maximum wind speeds of 12 m s"1 were recorded in the case of van Dijk's (1990) research.

A significant correlation (Figure 4.9, r = 0.65, p < 0.05) was found between the values of the gradient of sediment profile, a (data was achieved by regression referred to in preceding sections, Equation 4.1) and the average wind speeds which can be described by the following regression equation:

a = 16.02u-98.77 (4.2)

Extrapolating the regression line to the ordinate u = 6.16 m s"1 (Figure 4.9; Equation 4.2), it was found that the a-axis intercept was zero. This corroborates independent field evidence that at approximately u = 6 m s~', the amount of sediment transport tended to zero, and therefore this wind speed is considered to be the threshold for grain entrainment. In addition, the highest a values tended to be clustered at wind speeds above 8 ms'1.

Van Dijk (1990) and Arens & van der Lee (1993) found that at Schiermonnikoog on the Dutch coast the value of a decreased with increase in wind velocity, so that, according to their data.


relatively more sand was transported at greater heights at higher wind speeds. In the wind tunnel experiments, Arens & van der Lee (1993) also observed that for higher wind speeds, the amount of grains moving close to the surface diminishes; as a result particles moving in the creep mode also decreased in importance. In the field experiments performed at Malindi Bay, the gradient a of the least-squares regression for the vertical sand distribution profile increases at higher wind speeds (Figure 4.9). This data and independent visual observations indicate that during windy conditions, more of the transported sand moved close to the surface and the amount of creep was considerable. As expected, a significant correlation with a moderate r value of 0.54 was found between the percentage of sand saltating below 5 cm (Table 4.4b) and the average wind speeds.

The reasons for the scatter in Figure 4.9 are probably fluctuations in sand entrainment at the source of sediment supply, surface roughness elements which cause local accelerations, and trap defects (as is mentioned in a preceding section).

Grain size distribution related to height and wind speed

In this section, vertical variations in grain size distributions of saltating sand and the grain bed (surface material) are analyzed. The grain size distributions and derived parameters were apparently height and wind speed dependent. The narrow range of particle sizes (more than 95% between 0.1 and 0.4 mm) implies that changes of size with height were subtle. The size frequency distribution spectrum was bimodal, perhaps reflecting distinctive properties of the saltation mechanics and/or textural (shape, mineral density, etc.) segregation.

De Ploey (1980) reported from the Kalmthout dune station in Belgium that the relative amount of fine particles decreases with height during strong winds (above 10 m s"1). Bagnold (1960) and Sharp (1964) observed in wind tunnel studies that the transport height for coarser grains is higher than for smaller grains at high wind velocities. This pattern agrees with what is observed in Figure 4.1 lc. The decreasing percentage of the fine fraction with height during strong winds has also been mentioned by Draga (1983) in Westerland on the German North Sea coast and Arens (1994a) along the Dutch coast. Van Dijk (1990) noted along the Dutch coast that as the percentage of the size fractions smaller than 3.00cp increased, the size fraction 1.00-2.00cp also increased with height.

Figure 4.11a-c show that if wind speed conditions are considered for comparison, then the selective wind processes during grain transport could partly be described. The grain size distribution of moving sand is also a function of the textural composition of the grain bed (surface material) including grain shape and mineral density, but these have not been determined for trap samples. According to Sindowski (1956), sand moving winds can be classified into.three main classes; weak winds range from 6-8 m s', moderate winds range from 8-10 m s'1 and strong winds are those above 10 ms'1. The relationship between grain size selection and wind conditions was reported by Sindowski (1956) on Norderney dunes, who found that at moderate wind speeds (defined as wind speeds ranging 8-10 m s"1) the percentage of the fraction smaller than 2.500 increased considerably with transport height.

Applying the simplified criteria for wind speed ranges to the present sieve data, it was found that at wind speeds between 6-8 m s'1, the percentage of small grains increased slightly with height in the lower 10 cm and increased more drastically above this height. At 8-10 m s"1, the grain size distribution remained practically unchanged in the lower 10 cm but manifestly showed an

100

increase in small grains above this height. Above 10 ms"1 the percentage of big grains increased with height especially above 10 cm; the sand trapped at higher levels is better sorted and shows relative deficiency of the finer fraction. Presumably, the catching efficiencies of traps vary with grain size, height above the surface and wind speed (Arens & van der Lee, 1993). In addition, the effect during strong winds would imply that the bigger grains had perhaps gathered sufficient momentum to execute higher saltation paths or the fine grains were preferentially blown out.

The effect at high wind speeds would probably also be explained if we invoke De Ploey's (1980) suggestion of the "optimum saltation fraction" where the coarser sands are really saltating whereas the finest fractions are just floating in the dense basal flow (a high concentration of grains moving close to the surface) which is skimming over the surface. He speculated that this phenomenon could be associated with some selective transport mechanism by which the saltating grains are the most rapidly evacuated and separated from the basal flow. Observations of the effect of such aerodynamic processes on grains during strong winds was also reported by Arens (1994a). Rice (1991) has stated that aerodynamic processes and collision processes account for different sediment transport mechanisms at different wind speeds. As sorting characteristics appear to vary over the range of wind speeds studied, analysis of grain size distribution may contribute to a better understanding of the transport mechanisms, and thus to a better estimate of potential sand transport rate.

Sand transport analysis

Relationship between sand transport and wind variables

Correlations between sand transport and wind energy indices are presented. The correlation coefficients were subjected to the t test for significance; the term significant is used with reference to 90% confidence limit (Jungerius et al, 1981). Correlation coefficients between the total amount of sand transport (both qs and q„ columns 10 and 11 in Table 4.4d) and the five indices of windiness for the same period regarded here as the resultant wind vectors which are based on the first up to fifth power of wind velocity showed an increase from 0.49 and 0.52 for the first power, to 0.64 and 0.67 for the second power, to 0.74 and 0.77 for the third power, to 0.81 and 0.84 for the fourth power and, 0.90 and 0.91 for the fifth power.

A further correlation performed between the resultant vector based on the sixth power of wind speed and the total sand caught showed a slight decrease in the correlation coefficient to 0.88 and 0.90 (n = 17 in all the above cases). The regression coefficients r are presented as r for (o^ versus RjX) and r for (q, versus RjX). It appears therefore from these results, that the amount of sand transport was correlated most highly with the fifth power of wind speed above threshold. However, van Boxel (pers. commun., 1999), contended that the accuracy of the trap data probably cannot allow the distinction between the third power and fifth power of wind speed.

However, even for the best correlation there is still much scatter in the data. Arens (1994a) found that even within a short period of time like 10 minutes, sand transport was not continuous even at very high wind speeds and concluded that the predictive power of time-averaged wind speeds on sand transport will consequently be reduced.


Winds blowing from directions of 102° to 153° (transition period from northeast to southeast monsoon) brought in the least amount of sand whereas the highest amounts of sand emanated from 168° to 185° (southeast monsoon). The amounts of sand blown from typical northeast directions were of moderate quantities.

Vector analysis of wind data

Testing of conventional transport equations under our field conditions to predict potential sand transport are far from representative due mainly to possible errors in the calculated values of the wind shear velocity u,; u» is a fundamental component in the conventional transport equations of Kawamura (1951), Bagnold (1954), Hsu (1971), Lettau & Lettau (1977) and White (1979) which were tested. Furthermore, these formulae are based on controlled wind tunnel conditions.

In the preceding section, it has been determined that sand transport was most highly correlated with the fifth power of wind speeds above threshold. This relationship was used to calculate the resultant vector of effective winds for the whole year as shown in Table 4.5a and Table 4.5b for different wind speed classes, annual frequency of occurrence and direction for the period January 1993 to December 1993. The following formula has been used for the computation of wind vectors:

Rj-2,ZjZ,'f, («)

where, Rj = wind vector _j = wind speed, _j > threshold speed of 6 m s"1

fjj = frequency of occurrence of wind speed _j at wind direction j (hours a year)

The results of vector analysis also included the annual scalar sum of vectors, resultant wind direction and the index of directional variability (see footnotes below Table 4.5b). The calculations were performed for 24 wind directions, categorized into class intervals of 15°. The sand transport directional variability was 0.87, which implied that the resultant vector and scalar summation of wind components could be considered mutually equivalent in magnitude and direction for Malindi Bay. Using the definition of Fryberger & Dean (1979) the wind regime was classified as high energy with the scalar sum exceeding 4000 (in this case, 4000 x 8760/100 since our frequencies are in %) VU (VU= Vestor Units).

To illustrate the dominant direction of sand transport, results of vector analysis are presented in Figure 4.12 and Figure 4.13, respectively to show the resultant vector for each wind speed class and the mean vector for various wind speed classes. Figure 4.12 shows that the direction of vectors for wind speed class designated as a, b, c, d and e are southeast while f is several degrees west of south. Wind speed class 8-9 m s"1 compares most closely with the direction of the resultant vector of wind for the whole period of 1993. Tables 4.5a and 4.5b illustrate that the winds coming from compass direction of 195° potentially moved most of the sand (41.6%), followed by 180° (33.9%). On the other hand, the combined effect of two wind speed classes, 8-9 m s"1 and 9-10 m s~' moved 43.0% of the sand. Further analysis indicated that wind speeds ranging between 6 m s"' and 12 m s'1 contributed most to the movement of sand from the beach

102

into the dunefield. The offshore-directed component (between 210° and 030°), from land to sea, was insignificant at less than 1% since the corresponding wind speeds were mostly below the threshold value of 6 m s'.

It may also be noted here that there was a good agreement between the orientation of the long axes of hummocky dunes observed from inspection of aerial photographs (Chapter 3), and the predominant transport vectors (Figure 4.12 and Figure 4.13), the range of dune orientation being between 167-188° and the mean wind vector for the whole year being 171.2°. When the annual sand rose diagram (Figure 4.13) is inspected further, it is noted that the majority of the sand was transported in a northerly direction, the direction of the major winds.

Scaling factor = 4.9 X 1 0

1—9 = vector length

a = resultant vector wind speed

b = resultant vector wind speed

c = resultant vector wind speed

d = resultant vector wind speed

e = resultant vector wind speed

f = resultant vector wind speed

resultant vector units for wind

and 1 3 - 1 4 m s - 1 are 0.2 and

Figure 4.12: Resultant vectors for each wind speed class.


Figure 4.13: Sand rose diagram indicating the dominant wind transport directions at the Malindi Bay area showing narrow unimodal characteristics from the south; Arrow indicates the resultant wind vector, aligned at 38.1 ° west from the shoreline.

104

Table 4.5a: Computation of vector unit total from different wind directions at Malindi-Mambrui area

Wind direction (underlined)VVind speed class (m s'1) Up. limit

DIR 15° u5

Freq. in hrs Vector units

DIR 30° u5

Freq. in hrs Vector units DIR 45° 11s

Freq. in.hrs Vector units DIR6()° u5

Freq. in.hrs Vector units D1R 75° u5

Freq. in.hrs Vector units DIR 90° u5

Freq. in.hrs Vector units

DIR 105° u' Freq. in.hrs Vector units

DIR 120 o u5

Freq. in.hrs Vector units DM 135° u' Freq. in.hrs Vector units

DIR 150° u' Freq. in.hrs Vector units DIR 165° uJ



DIR 195° u;




6-7

11602.9 2

23205.8

11602.9 10

116029.1

11602.9 32

371293.0

11602.9 79

916629.6

11602.9 99

1148687.7

11602.9 106

1229908.1

11602.9 127

1473569.1

11602.9 76

881820.9

11602.9 117

1357540.0

11602.9 134

1554789.4

11602.9 200

2320581.3

11602.9 296

3434460.3

11602.9 271

3144387.6

11602.9 68

788997.6

11602.9 6

69617.4

7-8

23730.5 0

0.0

23730.5 7

166113.3

23730.5 5

118652.3

23730.5 18

427148.4

23730.5 80

1898437.5

23730.5 80

1898437.5

23730.5 56

1328906.3

23730.5 24

569531.3

23730.5 32

759375.0

23730.5 51

1210253.9

23730.5 307

7285253.9

23730.5 705

16729980.5

23730.5 445

10560058.6

23730.5 61

1447558.6

23730.5 0

0.0

8-9

41821.2 0

0.0

41821.2 0

0.0

41821.2 0

0.0

41821.2 11

460033.1

41821.2 33

1380099.4

41821.2 77

3220232.0

41821.2 33

1380099.4

41821.2 2

83642.4

41821.2 0

0.0

41821.2 6

250927.2

41821.2 152

6356821.5

41821.2 511

21370630.3

41821.2 498

20826954.7

41821.2 31

1296457.0

41821.2 0

0.0

9-10

73390.4 0

o o

73390.4 0

0.0

73390.4 0

0.0

73390.4 13

954075.2

73390.4 18

1321027.2

73390.4 26

1908150.5

73390.4 2

146780.8

73390.4 0

0.0

73390.4 0

0.0

73390.4 0

0.0

73390.4 56

4109862.5

73390.4 194

14237738.0

73390.4 372

27301229.6

73390.4 14

1027465.6

73390.4 0

0.0

10-11

121665.3 0

0.0

121665.3 0

0.0

I2I665.3 0

0.0

121665.3 7

851657.0

121665.3 6

729991.7

121665.3 0

0 0

121665.3 0

0.0

121665.3 0

0.0

121665.3 0

0.0

121665.3 0

0.0

121665.3 23

2798301.7

121665.3 133

16181483.6

121665.3 178

21656421.7

121665.3 9

1094987.6

121665.3 0

0.0

11-12

184243.5 0

0.0

184243.5 0

0.0

184243.5 0

0.0

184243.5 0

0.0

184243.5 0

0.0

184243.5 0

0.0

184243.5 0

0.0

184243.5 0

0.0

184243.5 0

0.0

184243.5 1

184243.5

184243.5 3

552730.6

184243.5 64

11791585.1

184243.5 106

19529812.9

184243.5 10

1842435.2

184243.5 0

0.0

12-13

293162.5 0

0.0

293162.5 0

0.0

293162.5 0

0.0

293162.5 0

0.0

293162.5 0

0.0

293162.5 0

0.0

293162.5 0

0.0

293162.5 0

0.0

293162.5 0

0.0

293162.5 0

0.0

293162.5 0

0.0

293162.5 1

293162.5

293162.5 2

586325.0

293162.5 0

0.0

293162.5 0

0.0

Sum of 13-14

416158.0 0

0.0

416158.0 0

0.0

416158.0 0

0.0

416158.0 0

0.0

416158.0 0

0.0

416158.0 0

0.0

416158.0 0

0.0

416158.0 0

0.0

416158.0 0

0.0

416158.0 0

0.0

416158.0 0

0.0

416158.0 2

832315.9

416158.0 1

416158.0

416158.0 0

0.0

416158.0 0

0.0

vector units

23205.8

282142.3

489945.3

3609543.4

6478243.6

8256728.0

4329355.6

1534994.5

2116915.0

3200214.0

23423551.4

84871356.2

1040213481

7497901.7

69617.4


C O N T . TABLE 4. ,5A

DIR 240°

u5 11602.9 23730.5

Freq. in.hrs 0 0 Vector units 0.0 0.0

DIR 255°

u5 11602.9 23730.5 Freq. in.hrs 0 0

Vector units 0.0 0.0

DIR 270 °

u5 11602.9 23730.5

Freq. in.hrs 0 0


DIR 285°

u5 11602.9 23730.5


DIR 300°

uJ 11602.9 23730.5


DIR 315°

u5 11602.9 23730.5 Freq. in.hrs 0 0


DIR 330°

u5 11602.9 23730.5


DIR 345°

us 11602.9 23730.5 Freq. in.hrs 0 0


DIR 360°

u5 11602.9 23730.5

Freq. in.hrs 0 0

Vector units (10 0.0

Hrs per speed 1623 1871 1354 695 356 184 3 3 6089

Vectorlengthl8831516.8 44399707.0 56625897.0 51006329.6 43312843.3 33900807.3 879487.5 1248473.9 250205062.5

106

41821.2 0

0.0

418212 0

0.0

41821.2 0

0.0

41821.2 0

0.0

41821.2 0

0.0

41821.2 0

0.0

41821.2 0

0.0

41821.2 0

0.0

41821.2 0

0.0

73390.4

0

0.0

73390.4

0

0.0

73390.4

0

0.0

73390.4

0

0.0

73390.4

0

0.0

73390.4

0

0.0

73390.4

0

0.0

73390.4

0

0.0

73390.4

0

0.0

121665.3

0

0.0

121665.3

0

0.0

121665.3 0

0.0

121665.3

0

0.0

121665.3

0

0.0

121665.3 0

0.0

121665.3

0

0.0

121665.3 0

0.0

121665.3

0

0.0

184243.5

0

0.0

184243.5 0

U.0

184243.5

0

0.0

184243.5 0

0.0

184243.5

0

0.0

184243.5 0

0.0

184243.5

0

0.0

184243.5 0

0.0

184243.5 0

0.0

293162.5

0

0.0

293162.5

0

0.0

293162.5

0

0.0

293162.5

0

0.0

293162.5

0

0.0

293162.5

0 0.0

293162.5

0 0.0

293162.5

0 0.0

293162.5

0

0.0

416158.0

0

0.0

416158.0

0

0.0

416158.0 0

0.0

416158.0

0

0.0

416158.0

0 0.0

416158.0 0

0.0

416158.0

0

0.0

416158.0 0

0.0

416158.0

0

0.0

0.0

0.0

o.o

0.0

0.0

0.0

0.0

0.0

0.0

Table 4.5b: Computation of resultant sand movement magnitude and direction using vector analysis, Malindi Bay, during the period of January-December 1993.

Wind direction Up. limit Class-mid

15 30 45 60 75 90 105 120 135 150 165 180 195 210 225 240 255 270 285 300 315 330 345 360

7.5 22.5 375 52.5 67.5 82.5 9 7 5

112.5 127.5 142 5 1575 172.5 187.5 202.5 217.5 232.5 247.5 262.5 277.5 292.5 307.5 322.5 337.5 3525

Vector sum

Angle to Perpend.

Bi

112.5 97.5 82.5 67.5 52.5 37.5 22.5

7.5 352.5 337.5 322.5 307.5 292.5 277.5 262.5 247.5 232.5 217.5 202.5 1875 172.5 157.5 142.5 127.5

cos Bj

-0.4 -0.1 0.1 0.4 0.6 0.8 0.9 1.0 1.0 0 9

0.8 0.6 0.4 0.1

-0.1 -0.4 -0.6 -0.8 -0.9 -1.0 - 1 0 -0.9 -0.8 -0.6

Angle to Parallel

<*i 202.5 187.5 172.5 157.5 142.5 127.5 112.5 97.5 82.5 67 5 5 2 7 37.5 22.5

7.5 352.0 337.5 322.5 307.5 2925 277.5 262.5 247.5 232.5 217.5

cosaj

-0.9 -10 -1 0 -0.9 -0 8 -0 6 -04 -0.1 0.1 0 4 0.6 0.8 0.9 1.0 1.0 0 9 0.8 0 6 0.4 0.1

-0.1 -0.4 -0.6 -0.8

Scalar units

23205.8 282142.3 489945.3

3609543.4 6478243.6 8256728.0 4329355.6 1534994.5 2116915.0 3200214.0

23423551.4 84871356.2

104021348.1 7497901.7

69617.4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Scaling factor

1.0 10 1.0 1.0 1.0 1 0 1.0 l 0 1 0 1 0 1 0 1.0 1.0 1.0 1.0 1.0 10 1 0 1.0 1.0 1.0 1.0 1.0 1.0

Vector unit

23205.8 282142.3 489945.3

3609543.4 6478243.6 8256728.0 4329355.6 1534994.5 2116915.0 3200214.0

23423551.4 84871356.2

104021348.1 7497901.7

69617 4 0 0 0 0 0 0 0 0

0.0 0.0 0.0 0.0 0.0

250205062.5

Rp Wind

Perpend. component

-8880.5 -36827.0 639507

1381312.5 3943704.8 65505027 3999803.0 1521862 4 2098804.5 2956612.2

18583152.8 51666408.1 39807246.5

978672.6 -9086.9

0.0 0.0 0 0

0.0 0.0 0.0 0.0 0.0 0.0

133497238.5

R| Wind

parallel component

-21439 4 -279728.6 -485753.8

-33347833 -5139536.2 -5026377.5 -1656772.6

-2003570 276312.9

1224668.9 14194400.5 67332973.9 96103194 4

7433756 1 68939.9

0.0 0.0 0,0 0.0 0.0 0.0 0.0 0.0 0.0

170489498.2

Summary of computation of effective wind vectors from Table 4.5b 1) R| = summation of values in Rl column = 170489498.2 2) Rp = summation of values in Rp column = 133497238.5 3) R|2 = 29066668986252000 4) Rp2 =17821512683626000 5) R]2 + Rp2 = 46888181669878000 6) R|/Rp= 1.2771013101273

7) Angle to shore perpendicular = tan"' (R|/Rp) = 51 9o 8) Rp/Ri = 0.78302323556489

9) Angle to shore parallel = tan"' (Rp/Rl) = 38.1° 10) Scalar sum = summation of values in column 9 = 250205062.5

11) Resultant vector of wind from 171.9» = 216536790.6 12) Wind directional variability = 0.87 Where. R| = Shore parallel component Rp = Shore perpendicular component Bj = angle between the direction (anticlockwise) of the shore-perpendicular

transect and the direction in which the wind is blowing cq = angle between the direction (anticlockwise) of the mean shoreline

and the direction in which the wind is blowing

Evaluation of landward component

Regression analysis based on results of the present field study indicated a distinct relationship linking the wind force and the quantity of sand caught in the traps. Thus an empirical expression describing the correlation between the transport rates and the magnitude of effective wind vectors calculated from the fifth power of wind speeds using the averaged data in Tables 4.4c (column 10) and 4.4d (column 14) was derived on this basis:

q, = 0.0013 (Wind vector) - 25 7.665 (4.4)


The wind speeds are the mean hourly (of u > 6 m s"1) wind speeds measured during the trap exposure.

Application of this formula to the value for the onshore component of the resultant transport vector directed perpendicular to the shoreline (see summary at the bottom of Table 4.5b) resulted in an estimated shore normal sand input into the dunefield of about 1.2 x 108 kg a"1 or 7.6 mWa"1 (mean specific bulk weight of aeolian sand = 1580 kg m"3), the shore length being some 10 km. In this calculation, the trap diameter was taken to be 0.1 m and a trap efficiency of 15% was assumed for the whole trap column, wind speeds and grain sizes. As this figure is based on estimated saltation values for the lowest collector and the actual catch in the upper collectors, it is likely to be lower because creep has not been taken into consideration.

Verification and limiting factors during transport by wind

From wind vector analysis it can be seen that the direction of this sand movement was mostly to the north (Figures 4.12 and 4.13) under the influence of predominantly high velocity winds from the south and to a lesser extent from the northeast (Figure 4.7). During 61.8% of the period when the southerly winds of 6 m s~' or greater were blowing, the highest amounts of sand was trapped in the sand traps (Table 4.4a, April-October) as was shown through vector analysis (Table 4.4d). In the field, personal observations in the northern (of Sabaki river) sector indicated larger dune forms, crescentic dunes ranged in height up to 12m and sand sheets climbed up to 50 m above sea level; in the southern sector the height of crescentic dunes was less than 2 m and the sand sheets hardly climbed to a height of 10 m above sea level.

In addition, inspection of aerial photographs for the years ranging between 1954-1994 showed that besides the effect of progradation (seaward movement of both the shoreline and dune zone throughout Malindi Bay), the dunefield transgression landward was manifested (at the scale of aerial photographs) in a limited part of the northern sector, estimated at an average rate of 4.7 m a"' (Chapter 3). Therefore in terms of scale, based on the above geomorphological evidence, it was also apparent that the direction of sand transport was mostly to the north.

The present results demonstrated the importance of wind direction on sand transport (Svasek & Terwindt, 1974; Kroon & Hoekstra (1990); Arens (1994b). The least amount of sand was blown off the beach when wind directions averaged between 102° to 153° (Table 4.4a and Table 4.4b), directions which also corresponded to minimum fetch (beach width), considering the orientation of the beach (shoreline) at 030°. According to the definition of wind direction ranges from shore normal/perpendicular by Arens (1994b), these winds would be considered onshore in contrast to the typical northeast and southerly winds which were on average oblique to the shore and were associated with moderate to high transport rates.

Keeping in view the highly variable field conditions such as moisture content, salt crust, sediment texture, beach width (including the effect of tides) and slope, wind gustiness and local accelerations, in addition to trap defects, there is evidence that aeolian sand transport rate is correlated with the fifth power of wind speed.

Jungerius (1984) and Arens (1994b) noted the importance of gusts during sand transport, having observed that most of the transport takes place during gusts of much higher wind speed than the

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average. Jungerius (1984) further argued that the short duration of wind gusts renders Bagnold-type expressions inapplicable during field investigations since they are based on steady-state wind conditions, and therefore overestimate sand transport. Arens (1994b) suggested that the actual values of the threshold speed could be improved if measurements are taken at a time scale of seconds; however Arens' investigation of sand transport by wind was based on beaches in a humid climate where variations in the threshold speed were considerable over short periods of time.

Because of the disturbance caused to the airflow, a more appropriate trap such as that of Leatherman (1978) may be more efficient and thus trap more sand, but 100% efficiency in trapping all the moving sand is unlikely to be achieved (Illenburger & Rust, 1986; Arens, 1994a) due to the intrinsic defects of the conventional vertical traps, spatial variability in the field and sampling errors. To circumvent these problems, Horikawa el al. (1984) suggested the use of a trench trap, having observed that the entire quantity of blown sand could be trapped by a trench of more than a few metres wide. They dug a trench measuring 8 m wide, 1 m deep and 50 m long. The main drawback of this method is lack of possibility to measure the respective amounts of the creeping sand and the saltating sand.

CONCLUSIONS

More than 120 thousand tonnes of sand was blown from the beach into the dunefield during 1993. A variable proportion of the sand grains were transported below 5 cm height; on average about 64%. Application of vector analysis and observation of surface geomorphology of the dunes indicate that sand movement was mostly directed northward. However, when the described wind climate is compared with long term wind data for Malindi area, and also considering the distribution of the sand dunes, 1993 seems to be anomalous.

The amount of trapped sand decreases exponentially with height. At low wind speeds, this decrease is more gradual than at high wind speeds. There is evidence that the creep mode of sand transport is possibly much higher than the figures published by Bagnold (1954) and Visher (1969). The results of this study seem to suggest that no real relation exists between the amount of creeping material and saltating material.

From the present experiments, it appears that at low to moderate wind speeds, the amount of small grains generally increased with height above the surface (Figure 4.1 la-b). At wind speeds above 10 m s"1 (Figure 4.11c), the percentage of coarser grains increased at higher levels especially between 10 and 15 cm above the bed; this effect during strong winds implies that the big grains had perhaps gathered sufficient momentum to execute higher saltation paths or the fine grains were preferentially blown out. The changes in sorting with height for weak winds apparently point to selective wind transport processes, sand became less well sorted upward, but at higher wind speeds the variations of sorting values with height became increasingly more complex, probably as a result of change in the transport mechanisms.

The cubic form of wind speed used in expressing sand transport rates employed in Bagnold-type equations proved to be less predictive. The average amount of sand transport per trap per duration was found to be best correlated with an exponential value of 5, greater than the third power. The relationship between the total saltation and the wind vector based on this fifth power


of wind speeds is amply shown by the application of a simple linear regression formula in the form qt = 0.0013(Wind vector) - 257.665 as the coefficient of correlation was found to be 0.90, p < 0.05 and n = 17. The results of this study would improve if quantitative information on creep, surface conditions, frequency /magnitude of gust and more efficient traps are given attention in future field research. A better knowledge of these factors would contribute to a better understanding of the sand transport system, and thus to a better estimate of aeolian sand transport potential.

For a dynamic coastal management and dune conservation purposes, it is important to note that most of the sand movement occurs during the prevailing southerly winds. Therefore, any planting of vegetation for stabilization of mobile sand must be done during the northeast monsoon, at the onset of the short rains.

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Documents

UvA-DARE (Digital Academic Repository) Geomorphology … · Mombasa, Kenya. Sand transport rates, wind speed and direction were measured on the berm zone adjacent to the beach