ESTIMATION OF SEDIMENT YIELD IN BARAT DAYA PULAU … filemenggunakan Sistem Maklumat Geografi (GIS)...

Sumayyah Aimi, International Journal of Environment, Society and Space, 2017, 5(1), 44-59

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ESTIMATION OF SEDIMENT YIELD IN BARAT DAYA PULAU PINANG,

MALAYSIA

Sumayyah Aimi Mohd Najib1*

1Department of Geography & Environment, Faculty of Human Sciences,

Universiti Pendidikan Sultan Idris, 35900 Tanjong Malim, Perak.

Abstract: Sediment yield estimation in rivers at the regional or local scale is very important especially in terms

of managing the water resources in the catchment area. The sediment yield is usually calculated either from

direct measurement of sediment concentration in rivers or from sediment transport equation at a particular outlet

point in the catchment. A total of 19 rivers were selected as sampling sites located at the Barat Daya District of

Pulau Pinang. The Universal Soil Loss Equation (USLE) was used to estimate the sediment yield in the study

area by integrating with the Geographic Information System (GIS) to generate maps of the USLE factors, which

are rainfall erosivity (R), soil erodibility (K), slope length and steepness (LS), crop management (C), and

conservation practice (P) factors. A sediment rating curves of the study area was developed to verify the

accuracy as well as comparison to the sediment yield estimated by USLE. The results show good correlation

between the sediment yield estimated by USLE and observed data (r2 is 0.62). The sediment yield estimated in

the year 1974 was 1300 ton/km2/year, 1984 was 1921 ton/km2/year, 2004 was 1919 ton/km2/year and 2012 was

2336 ton/km2/year. Based on the land use analysis, agricultural activity was dominant in the Barat Daya area

and contributes much of the sediment into the river system.

Keywords: estimation sediment yield, USLE, Barat Daya Pulau Pinang, Malaysia

Abstrak: Anggaran hasilan sedimen dalam sungai pada skala serantau atau tempatan adalah sangat penting

terutama dari segi menguruskan sumber air di dalam kawasan tadahan. Hasilan sedimen biasanya dikira sama

ada dari pengukuran langsung kepekatan sedimen di dalam sungai atau dari persamaan pengangkutan sedimen

di tempat keluar khususnya di kawasan tadahan. Sebanyak 19 sungai telah dipilih sebagai kawasan persampelan

yang terletak di Daerah Barat Daya Pulau Pinang. Persamaan Kehilangan Tanih Sejagat (USLE) telah

digunakan untuk menganggarkan hasilan sedimen di kawasan kajian dengan mengintegrasikannya

menggunakan Sistem Maklumat Geografi (GIS) untuk menjana peta faktor USLE, iaitu faktor erosiviti hujan

(R), erodibiliti tanih (K) , panjang cerun dan kecuraman (LS), pengurusan tanaman (C), dan amalan

pemuliharaan (P). Kaedah keluk sedimen kawasan kajian telah dibangunkan untuk mengesahkan ketepatan dan

juga perbandingan dengan hasilan sedimen yang dianggarkan oleh USLE. Hasil kajian menunjukkan korelasi

yang baik antara hasilan sedimen yang dianggarkan oleh USLE dan data yang dicerap (r2 adalah 0.62). Hasil

sedimen dianggarkan pada tahun 1974 adalah 1300 tan/km2/tahun, 1984 adalah 1921 tan/km2/tahun, tahun 2004

adalah 1919 tan/km2/tahun dan tahun 2012 adalah 2336 tan/km2/tahun. Berdasarkan analisis guna tanah, aktiviti

pertanian adalah dominan di kawasan Barat Daya dan dianggap sumbangan terbesar bagi nilai sedimen ke dalam

sistem sungai.

Kata kunci: Anggaran hasilan sedimen, USLE, Barat Daya Pulau Pinang, Malaysia

* E-mail: [email protected]


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INTRODUCTION

Soil erosion is a worldwide problem because of its economic and environmental impacts. Many

human-induced activities, such as mining, construction, and agricultural activities disturb land

surface, resulting in accelerated erosion (Lim et al., 2005). Over the past 40 years, 30 per cent of the

world’s arable land has become unproductive. Erosion also reduces the ability of the soil to store

water and support plant growth, thereby reducing its ability to support biodiversity (Lal, 1990).

Sediment yield is the amount of sediment load passing the outlet of a catchment and is the net result

of erosion and deposition processes within a basin. It can be expressed in absolute terms (t yr−1) or per

unit area (t km−2 yr−1) (Jain, Mishra and Shah, 2010). The amount of sediment yield generated within

a catchment is a function of a number of anthropogenic and physical factors including farming,

mining, construction, slope, basin area and rainfall intensity. Information on sediment yield of a river

basin is an important requirement for water resources development and management (Akrasi, 2011),

because high sediment loads affect water quality, water supply, flood control, reservoir lifespan,

irrigation, navigation, fishing, tourism, hydro-power generation, river channel morphology and

stability (Schwartz and Greenbaum, 2009). During the last decades, many different models have been

proposed to describe and predict soil erosion by water and associated sediment yield, varying

considerably in their objectives, time and spatial scale involved, as well as in their conceptual basis

(De Vente and Poesen, 2005). Hence, the main objective of this study is to estimate sediment yield

using USLE and Rating Curve Method and to estimate past sediment yield for the year of 1974, 1984,

2004 and 2012, for 19 catchments in the Barat Daya District, Pulau Pinang, Malaysia.

LITERATURE REVIEW

The soil erosion involves the processes of detachment, transportation and deposition (Brady & Weil,

1999). Sediment which is separated from the soil surface was due to impact of raindrop and shear

forces of flowing water. Then, the sediment will be transported down the slope of the hill particularly

by the flowing water, although there are a small number caused by splashing rain (Walling, 1988).

Soil erosion is one of the most serious and challenging environmental issues related to land

management all over the world. It is a complex natural process altered by anthropogenic activities

such as clearing of lands, agricultural practices, surface mining, construction and urbanization. It is

reported that seventy five billion metric tons of soil are removed from land annually by wind and

water erosion (Pimentel, 1995).

Water erosion which accelerated by anthropogenic activities is the key process that has been taking

place in humid and tropical regions. The detached sediments from hillslopes by sheet, rill and gully

erosion are exported to the river systems, a significant portion of that is silted into many inland

reservoirs, and the rest is being transferred to the ocean. Many studies have revealed that sediment

loads in the rivers of Asia have been rapidly increasing as a result of increased rate of inland soil

erosion. Increasing pressure on land and fragile ecosystems by rapidly growing population is the main

reason for accelerated soil erosion Transportation of suspended sediments in rivers is very complex,

non-linear, dynamic and widely scattered due to the influence of physical processes involved and

variability in space and time. Therefore, studying the behaviour of suspended sediment transportation

in rivers is also an elusive task in hydrology (Diyabalanage et al., 2017). Similarly, estimation of

suspended sediment loads in rivers is a major concern since information on sediment loads of rivers

can be used for many studies such as evaluation of contaminant transport, reservoir sedimentation,

environmental impact assessment, sediment transport to oceans, channel and harbour siltation, soil

erosion and ecological impacts (Horowitz, 2003; Syvitski, et al., 2000).


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Soil Erosion Processes

In the tropics, the process of soil erosion occur more actively than in temperate climates and it has

already brought pressure and contribute to environmental problems (Yang et al., 2003). Asia is

reported the highest erosion rates with the loss of sediment by an annual average of about 166

tons/km2 compared to 47.43 and 93 tons/km

2 for Africa, Europe and South America (El-Swaify et al.,

1982; Doughlas, 1994). Soil erosion is a two-phase process consisting of removal of particles from the

mass of soil and transported by agents that cause erosion such as running water (Morgan, 1986). Both

phases are closely related to the hydrological cycle and both are influenced by various factors, then it

also varies through space and time (Thornes, 1980).

The detachment of soil particles from the soil surface is from rainfall intensity and flow of surface

runoff. Strong local shear stress in the ground by the impact of raindrops, leading to fragmentation of

interstitial holding soil particles (Loch & Silburn, 1996). When rainfall exceeds the infiltration rate of

the soil surface and storage capacity began to slip, surface runoff occurs, which consists of a very thin

layer of water (Bridges & Oldeman, 1999). Surface flow causes shear stress on the surface where if it

exceeds the strength of the soil cohesion, it will give a decision on the removal of sediment (Meritt et

al., 2003). The number particles of soil eroded and transported is controlled by splashing water

capacity and water flow, thus bringing the particles of soil sliding down the slope (Lal, 1990).

Therefore, the deposition of eroded soil particles may occur depending on the surface area of the

organization, capacity and transport flows in the event of a reduction in water velocity, suspended

sediment deposition may occur. Four main processes of erosion control is erosivity (erosion),

erodibility of soil, the protection of plants and soil surface gradient (Foster, 1981; Morgan, 1986).

MATERIALS AND METHODOLOGIES

Study area

Barat Daya District of Pulau Pinang was selected as the study area, which consists of 19 river

catchments. Figure 2 and Figure 3 show the location and land uses of each catchment, and the

morphological characteristic of the respective catchment is shown in Table 1. The list of the selected

rivers are the Upstream and Downstream of Relau River, Upstream and Downstream of Ara River,

and Bayan Lepas River. The area is categorized as an built up area of development.

The second area is the area that includes rivers in the western part of the study area, namely Sg. Pulau

Betong, Sg. Nipah, Sg. Buaya and Sungai Burung. The rivers in this area represents an area with

diverse land use and the land use is predominantly agricultural. The third area is for the rivers in the

middle of the overall study area known as Sg. Kuala Jalan Baru, Sg. Titi Teras, Sg. Pak Long, Sg. Air

Puteh, and Sg. Rusa. The upper part was Sg. Pinang, Sungai Titi Kerawang and Upstream and

Downstream of Teluk Bahang river where land use is more to agriculture and forests.

Barat Daya area was selected as research area because of the lack of data in terms of sediment and

discharge, and also the diversity of land use compared to the Timur Laut which is dominated by build-

up land use. In addition, there is no comprehensive sediment study of the Barat Daya area. According

to the Malaysian Meteorological Department data, the temperature of the northern part of Penang

ranges between 29◦C and 32◦C and the mean relative humidity between 65 per cent and 70 per cent.

The highest temperature is during April to June while the relative humidity is lowest in June, July and

September. Rainfall on Penang Island averages between 2000 and 3000 mm per annum

respectively.Figure 1 were the rain gauged data for the stations of Bayan Lepas and School of

Physics, USM for 2012. The highest annual rainfall recorded in September, which is 384.66 mm (rain

gauged school of physics) while at Bayan Lepas was 376.9 mm.


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Figure 1: Monthly Rainfall data for 2012

Figure 2 : Location of sampling stations

Figure 3: Land use for 2012


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Sediment rating curves

The framework for the estimation of sediment yield is shown in Figure 4. The samplings were carried

out every fortnight to develop the sediment rating curves for the river catchments. The rating curve

will then be used to estimate the sediment yield during the ungauged period. The sediment rating

curves were developed based on Ismail (1995). It was based on the relationship between the rainfall

and sediment loading. Two rating curves were developed, one for rainfall amount that is less than

20mm, and the other is for rainfall of more than 20mm. The sediment rating curve equation as shown

in Table 2 were used to obtain the sediment yield for each river catchment. Based on the estimation of

sediment yield from the sediment rating curve, the relationship of the obtained sediment yield are

used to verify the accuracy of sediment yield estimated from the USLE.

Rivers/ Sampling location River

Length

(km)

Catchment

area (km2)

Drainage

density

(km/km2)

River

Order

Latitude Longitude

1. Relau River Upstream(RU) 10.05 2.53 3.97 3 N 5◦ 20.94” E◦ 100 16.32’’

2. Relau River Downstream (RH) 46.24 11.55 4 5 N 5◦ 19.27” E◦ 100 16.88’’

3. Ara River Upstream (AU) 15.25 4.93 3.09 3 N 5◦ 19.48” E◦ 100 15.86’’

4. Ara River Downstream (AM) 17 5.1 3.33 3 N 5◦ 19.34” E◦ 100 16.33’’

5. Bayan Lepas River (BL) 9 2.35 3.83 3 N 5◦ 17.8” E◦ 100 15.6’’

6. Teluk Kumbar River (TK) 7.92 2.72 2.91 3 N 5◦ 17.5” E◦ 100 13.8’’

7. Pulau Betong River (PB) 15.39 5.36 2.87 4 N 5◦ 18.42” E◦ 100 12.17’’

8. Nipah River (SN) 3.07 0.92 3.34 2 N 5◦ 19.9” E◦ 100 12.38’’

9. Burung River (BR) 30.54 10 3.05 4 N 5◦ 20.68” E◦ 100 12.49’’

10. Kuala JalanBaru River (KJB) 63.21 16.14 3.92 5 N 5◦ 21.12” E◦ 100 12.55’’

11. Buaya River (BY) 22.78 7.65 2.98 3 N 5◦ 20.23” E◦ 100 13.24’’

12. TitiTeras River (TT) 26.78 7.12 3.76 4 N 5◦ 21.21” E◦ 100 13.77’’

13. Pak Long River (PL) 4.55 1.1 4.14 3 N 5◦ 21.63” E◦ 100 13.32’’

14. Air Puteh River (AP) 10.98 3.05 3.6 3 N 5◦ 21.75” E◦ 100 13.22’’

15. Rusa River (RS) 12.29 2.98 4.12 3 N 5◦ 23.18” E◦ 100 12.75’’

16. Pinang River (SP) 43.37 8.84 4.91 4 N 5◦ 23.94” E◦ 100 12.7’’

17. TitiKerawang River (TTK) 28.79 6.71 4.29 4 N 5◦ 24.2” E◦ 100 13.35’’

18. Teluk Bahang River (TBU) 4.37 0.98 4.46 2 N 5◦ 25.43” E◦ 100 13.21’’

19. Teluk Bahang River (TBD) 50.19 11.96 4.20 4 N 5◦ 27.25” E◦ 100 12.81’’

Table 1: Location and morphological characteristic of catchments in the study area


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Figure 4 : The framework for the estimation of sediment yield

Rivers catchment Rainfall relation < 20 mm Rainfall relation ≥ 20 mm

Fortnight sampling

Relationship between rainfall vs sediment load

The measurement of ungauged sediment load based on this calculation

Relationship between USLE model USLE

and rainfall-sediment load

Calculate of past sediment yield from 1974 -

2012


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Table 2: The calculation of Rating Curve

The sediment yields estimation from Universal Soil Loss Equation (USLE)

For estimation of sediment yield, USLE method is also used to compare the gauging data. GIS is an

efficient tool to integrate various datasets and assess any dynamic system such as soil loss/ soil

erosion and there have been many studies of soil loss by various methods (Adinarayana, Rao,

Krishna, Venkatachalam and Suri, 1999; Lee; 2004; Millward and Mersey, 2001). The USLE model

integrated with GIS could be used to calculate soil erosion at any point in catchment experiencing net

erosion. It’s easy and simple approach, efficient method of soil loss assessment and universally

accepted method for monitoring soil loss. The universal soil loss equation is an empirical model

developed by Wischmeir and Smith (1978) to estimate soil erosion. Figure 5 and Figure 6 show the

steps for obtaining the soil loss map from USLE. Mathematically, the equation is denoted as:

A (tons/ha/year) = R x K x LS x C x P (1)

R2 Regression Regression R

2

Relau River Upstream(RU) 0.53 y =10.97x-21.7 y = 15.03x-268.2 0.83

Relau River Downstream (RH) 0.72 y =27.18x+54.48 y = 288.2x-7745 0.87

Ara River Upstream (AU) 0.86 y =22.39x+ 50.68 y = 18.25x-390.3 0.76

Ara River Downstream (AM) 0.83 y =11.20x+37.18 y = 257.2x-6017 0.98

Bayan Lepas River (BL) 0.52 y = 33.49x+ 78.69 y = 23.5x-126.6 0.56

Teluk Kumbar River (TK) 0.58 y = 10.01x+98.62 y = 121.8x-3774 0.70

Pulau Betong River (PB) 0.75 y = 5.38x+30.85 y = 5.26x-47.83 0.85

Nipah River (SN) 0.80 y = 78.97x-130.7 y = 0.73x-15.56 0.80

Burung River (BR) 0.97 y = 40.81x+50.12 y = 42.07x-1170 0.78

Kuala JalanBaru River (KJB) 0.93 y = 59.05x+293.3 y = 236.6x-6046 0.78

Buaya River (BY) 0.86 y = 63.61x+54.72 y = 49.92x-1076 0.88

TitiTeras River (TT) 0.57 y = 7.46x+23.12 y = 117.3x-3152 0.95

Pak Long River (PL) 0.93 y = 33.1x+29.31 y = 7.14x-173.9 0.78

Air Puteh River (AP) 0.7 y = 4.35x+22.35 y = 4.33x-14.96 0.59

Rusa River (RS) 0.78 y = 3.5x+19.67 y = 44.18x-876.2 0.99

Pinang River (SP) 0.77 y = 41.22x+129 y = 0.43x+77.82 0.89

Titi Kerawang River (TTK) 0.77 y = 31.66x+29.47 y = 8.35x-114 0.69

Teluk Bahang River (TBU) 0.5 y = 18.72x+23.70 y = 20.33x-432.7 0.98

Teluk Bahang River (TBD) 0.64 y = 10.57x+42.95 y= 23.20x-480.3 0.98


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Figure 5 : The layering of USLE factor Figure 6: Soil erosion map (2012)

Rainfall-runoff erosive factor (R)

Rainfall data was obtained from the Bayan Lepas weather station belonging to Malaysian

Meteorological Services Department. The monthly average rainfall data of 38 years (1974–2012) was

used to calculate the R factor. The annual aerial precipitation, P (mm) were calculated using Thiessen

polygon average method (Thiessen and Alter,1911) (Fig 1). Average or mean total rainfall for all

stations in the catchment can be calculated with the following formula:

P = Error! Reference source not found. …………………….….(2)

Where :

P1 = Area of polygon

TA = Total Area

Wischmeier and Smith (1978) suggested that maximum intensity (I30) value of 75 mm/h for tropical

regions because studies have shown that a decrease in the size of raindrops erosive when the intensity

exceeds the threshold value. For Penang station the I30 is 100 mm at once in 5 years. Among the

methods that can be used to get rain index based on such equations 3, 4 and 5 by Morgan, (1995);

Foster et. al, (1981) and Roose (1975)

1) R = 9.28 * P - 8838 (metric units) .................................................................................. (3)

2) R = 0.276 * P * I30 (metric units) ................................................................................... (4)

3) R = 0.5 * P * 1.75 (metric units) ...................................................................................... (5)


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Where,P = average annual precipitation (mm); I30 = The intensity of the rain for 30 minutes

Soil erodibility factor (K)

Soil erodibility is a soil resistance against the process of disassembly and transport of soil. It is an

important index to measure the tendency of soils to water erosion, and an important parameter to

predict soil erosion (Renard et al., 1997). Soil factors (K) shows the effect of the soil, the nature and

the characteristics of the soil profile as soil texture, stability aggregate, shear stress, infiltration

capacity, organic and chemical content in soil loss. An attempt to formulate index soil erodibility was

also based on the properties of soils as determined in the laboratory or the field, and the reaction of

soil against rain (Weischmeier et al., 1971; Weischmeier & Smith, 1978; Tew, 1999; Shirazi &

Boersma, 1984 ; Singh & Phadke, 2006). Equation 6 (Tew, 1999) was found to give an estimate of the

most complete of the K factor for a series of soil in Malaysia, and is therefore recommended for the

calculation of the K factor in the guidelines Drainage and Irrigation Department. Soil series in the

study area catchment are shown in Table 3 .

The equation for the K is as follows:

K = [1.0x10−4 (12 −OM)M1.14 + 4.5(s − 3)+ 8.0(p − 2)]/100 …………………………………(6)

Where :

K – Soil Erodibility Factor (ton/ha)(ha.hr/MJ.mm)

M – (% silt +% very fine sand) x (100 – % clay)

OM – % of organic matter

S – soil structure code

P – permeability code

Table 3: K factor for different soil series in study area

Soil series K value

Beriah-Tanah Liat 0.051

Chengai 0.057

Holyrood-Lunas 0.035

Keranji 0.051

Redua-Rusila 0.02

Renggam-Bukit Temiang 0.029

Renggam-Jerangau 0.038

Sedu-ParitBotak-Linau 0.045

Selangor-Kangkung 0.053

Sogomana-Setiawan-Manik 0.045

Tanah curam 0.066

Tanah Bandar 0.066

Telemung-Akob-LanarTempatan 0.051


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Slope-length and steepness factor (LS)

The effect of topography on soil erosion is accounted for by the LS factor, which combines the effects

of a slope length factor (L) and a slope steepness factor (S). Wishmeier and Smith (1978) defined

slope length as the distance from the point of origin of overland flow to the point where the slope

decreases enough that deposition begins or the point where runoff becomes concentrated in a defined

channel. Slope steepness reflects the influence of slope gradient on soil erosion (Wischmeier & Smith,

1965). It is known that the amount of runoff increases due to the continuous accumulation down the

slope as the slope length (L factor) increases; the velocity of runoff increases as the slope steepness (S

factor) increases. The development of TIN (30m x 30m resolution) produced using ESRI ArcGIS

software, and this topographic factors, L and S factor values were then derived from the TIN and

combined to a single LS factor. The LS factor was calculated based on equation from Wischmeier &

Smith (1978) (eqs. 7,8)

S = 0.065 + 0.045+0.0065S2…………………………………………………………………. (7)

LS = (0.065 +0.045S+0.0065 S2S) X Error! Reference source not

found.……………………………………………..….(8)

Where:

L = Slope length in meter; S = Slope angle in %;

m = 0.2 if S < 1,

m = 0.3 if 1≤ S<3,

m = 0.4 if 3≤ S<5,

m = 0.5 if 5≤ S<12

m = 0.6 if S ≥12%

Calculations for the LS is using map calculator in a raster analysis that is based on equations 9 below:

Pow ([FlowAcc) x 30/22.1,0.6) x Pow(Sin[Slope]) x 0.01745/0.09,1.3)…………………………(9)

where:

30 = resolution

0.6 = faktor m

0.09 = 9% or 5.16 slope gradient according standart plot USLE

Land cover and management factor (C)

The vegetation covers factor (C) represents the ratio of soil loss under a given vegetation cover as

opposed to that bare soil. The effectiveness of a plant cover for reducing erosion depends on the

height and continuity of the tree canopy as well as the density of the ground cover and the root

growth. The vegetation cover intercepts raindrops and dissipates its kinetic energy before it reaches

the ground surface. In the current study, C values for land use in the study area entered into the table

attributes for each basin and the value of C is based on Table 4 for each fraction of the land use. In

Malaysia, the values of C based on the land use is determined by the Department of Agriculture

(DOA).

Conversation practice factor (P)

The P factor depends on the conservation measure applied to the study area. In Malaysia the most

common conservation practice is contour terracing in rubber and oil palm plantations. In this study,

the P value is 1, assuming no conservation practices were adopted.


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Table 4 : C value for each land uses in study area

To get the past sediment yield data, regression methods were used to assess the usability of USLE

model and the observed data. After getting the value of all the basin area, regression analysis were

used to see the relationship and reliability between the model and the observed data (eq 10; Figure 7).

The calculation for obtaining the past sediment for 1974, 1984 and 2004 are using the following

equation:

y = 5.002 x + 8.015 ………………………..................................................................................... (10)

where y = the estimated value of the observations

x = the forecast from USLE

Land use C factor

Built-up area 0.15

Forest 0.003

Orchard 0.35

Rubber 0.25

coconut/ Oil palm 0.2

Paddy 0.45

Scrub/ Others 0.03

Quarry 1

Water body 0.1


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Figure 7: Regression analysis between gauging data and USLE


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RESULTS AND DISCUSSIONS

The most common way of combining intermittent concentration data with continuous discharge data

uses a rating curve to predict unmeasured concentrations from the discharge at the time (Ndomba et

al., 2008). A suspended sediment rating curve or transport curve is usually presented in one of two

basic forms, either as a suspended sediment concentration/streamflow or as a suspended sediment

discharge/streamflow relationship (Walling, 1977). In most cases, rating curves are constructed from

instantaneous observations of discharge and either sediment concentration or load, but several specific

variants have been proposed (Walling, 1977). Colby (1956) has classified rating relationships,

according to temporal resolution of the data, into instantaneous, daily, monthly, annual and flood

period curves and, according to particle size criteria, into clay-silt ratings and sand-sized ratings.

Other researchers have subdivided instantaneous data according to stage and season, constructing

separate rating relationships for rising and falling stages (Loughran, 1976) and for various times of the

year (Hall, 1967) as reported in Walling (1977). From this rating curve method, an estimation of

sediment yield was obtained.

Table 3 and Figure 6 shows the result between the sediment yield estimated by USLE and the

observed data (r2 is 0.62) and the equations were used to estimate past sediment yield. The estimated

sediment yield from 1974-2012 shows an increasing trend (Figure 8). The highest sediment yields for

gauging data was 221.94 ton/km2/year recorded at Sungai Rusa catchment while the lowest sediment

yields was 10.99 recorded at Sg. Nipah. The average amount of sediment yields for all 19 catchments

were estimated at 163.72 ton/km2/year and 195.28 ton/km2/year (19.3 per cent) for 1974 and 1984,

respectively. Then it increased slightly (0.5 per cent) to an average of 196.18 ton/km2/year in 2004

and a larger increase was noticeable (10.8 per cent) to 217.43 ton/km2/year in 2012. USLE estimated

the soil loss at 110.18 ton/km2/year and 116.89 ton/km2/year for 1974 and 1984 respectively, then also

increase slightly to 117.87 ton/km2/year in 2004 then 122.44 ton/km2/year in 2012.

Figure 8: The trend of past sediment yield from 1974-2012


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Verification data were use to see the reliability of model USLE with observed data than use to

estimate the past sediment for the 1974, 1984 and 2004 for 19 river basins in the study area. Negative

values indicate underestimate data, while a positive value is grossly over-estimated the value of the

model and the observations. USLE data before correction mainly large value, while after the

correction value has been reduced to 40%. This is shows that corrected USLE based on the regression

is almost with observation data and can be used to estimate past sediment yield. While Figure 9 shows

the relationship between the observed data and the corrected USLE, which shows that the limits of

the accuracy of data is still within 95% accuracy and can be adopted. Relationship of regression also

showed a significant 62% while there are seven points outside the limits of confidence affecting the

regression. Underestimate value of the river basin is for the Sg Teluk Bahang Hulu, Sg. Bayan Lepas

and Sg. Teluk Kumbar, while for the overestimate is for the Sg Pulau Betong, Sg. Burung, Sg. Buaya

and Sg. Pinang.

Figure 9: Confidence level between gauging data and USLE

The highest number is from areas of urban and agricultural land use. Human activities contribute

significantly to the process of collection, storage and resultant sediment occurring in the drainage

basin in the world. Relatively, the increase in population, industrial growth and urbanization of

irregular pressure on existing water resources, which are scarce, did not only increase the usage but

also the deteriorate the quality of available resources (Antrop, 2004). The process of dynamic change,

especially population growth and urban areas around the world, affect natural and human systems at

all geographical scales (Herold et al., 2005). The relationship between land use and soil erosion/

sediment yield has attract the interest of many researchers, and many writers have used simulation and

laboratory experiment to determine the relationship between land use and soil erosion, which

indirectly help the process of remediation of soil (Long et al, 2006). Suspended sediment

concentration primarily identified as the main cause disturbance to the river system. Indirectly, the


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concentration of suspended sediment transport often acts as an agent to carry nutrients, trace metals,

semi-volatile organic compounds, pesticides and thereby affect the physical, chemical and biological

properties of aquatic ecosystems (Bledsoe & Watson, 2001). At present, the land has become one of

the limited resources in Penang due to its hilly topography and the limited flat land for agricultural

use, settlements and urbanisation. Sediment availability in the study area is related to the land use and

agriculture was the most dominant activities. In Penang Island, natural elements particularly weather

elements are highly erosive (Goh and Hui, 2006). Geomorphological processes such as rain splash

erosion and surface runoff erosion have been shown to be extremely high in wet equatorial areas

(Pradhan, Chaudari, Adinarayana and Buchroithner, 2012; Ismail, 1995).

CONCLUSION

The rating curve method was used because of insufficient hydrological and sediment data in Barat

Daya District. This study has successfully estimated the past sediment yield for 19 ungauged

catchment in the Barat Daya District of Pulau Pinang, using existing conceptual methods and GIS.

This method can be used for the identification of sediment source areas and the prediction of sediment

yield from an ungauged catchments. This study is significant because it indirectly gives the obtained

input data and the methods that can be applied to the river basin of the river without observations and

suitability of the Universal Soil Loss Equation (USLE). With the advent of such studies, people are

not directly seeing the process of urbanization in rural development which is very useful to achieve

sustainable rural development. In this regard, the need to study the relationship between land use

changes that occur and how these changes also affect the sediment in addition to income can generate

input and guidance as well as useful data for reference in the future.

Acknowledgement

I would like to thank Universiti Sains Malaysia for supporting this study under the Post-Graduate

Research Grant (RU-PRGS), grant number 1001/PHUMANITI/846039.


59

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