Climate and Hydrology

Department of Environment and Natural Resources (DENR)River Basin Control Office (RBCO) Mindanao Development Authority (MINDA) and the Office of Civil Defense (OCD)

Mindanao River Basin Integrated Management and Development Master Plan

FEASIBILITY STUDY OF AN INTEGRATED FLOOD CONTROL, RIVERBANK PROTECTION

AND REHABILITATION PROJECT FOR AMBAL-SIMUAY RIVER SYSTEM

CHAPTER 3CLIMATE AND HYDROLOGY

3.1 GENERAL CLIMATE

Rainfall in the Philippines is generally brought about by myriad rainfall-causing weather patterns inter alia, monsoon, the Inter-Tropical Convergence Zone (ITCZ) and tropical cyclones.

3.1.1 Monsoon

Monsoons are winds prevalently blowing from the northeast or southwest. The Northeast and Southwest monsoons trigger the onset and recession of the rainy season in the Philippines. The Southwest monsoon may begin as early as mid April and may last until as late as early November. The Northeast monsoon, on the other hand, prevails from November to March. During the period from May to October, when the Southwest monsoon and tropical cyclone seasons are dominant, the northern and central parts of the Mindanao River Basin receive heavier rainfall than the rest of the year potentially triggering flooding and landslides.

3.1.2 Inter-Tropical Convergence Zone

In the northern hemisphere, the trade winds move in a southwesterly direction, while in the southern hemisphere they take the northwesterly course. At a point where the trade winds converge, air is forced upward to the atmosphere, hence forming the Inter-Tropical Convergence Zone (ITCZ). The phenomenon is apparent with the formation of a band of clouds bringing about rain showers accompanied by occasional thunderstorms.

(source: Website of Data Discovery Hurricane Center)

Figure 3.1-1Seasonal Movement of ITCZ

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The ITCZ then moves north during the high-sun season of the Northern Hemisphere as shown in Figure 3.1-1. It begins moving back southward in August. It passes south of the Philippines by November and finally reaches the southernmost portions of the country in the months of January and February.

3.1.3 Tropical Cyclones

Tropical cyclones are characterized by a low pressure center where winds of varying intensities blow around the center. Tropical cyclones are classified according to maximum winds near the center, as follows:

Table 3.1-1Classification of Tropical Cyclones

Classification Maximum Wind SpeedTropical Depression (TD) Winds from 45 to 63 KPH (km/hour)Tropical Storm (TS) Winds from 63 to 117 KPHTyphoon (T) Winds of more than 117 KPH

On the average, about twenty (20) tropical cyclones bring damages to the Philippines, with voluminous rains and strong winds. The country figure however, as indicated in Figure 3.1-2 is in stark contrast with that of the MRB where an average of 0.1 tropical cyclones hit the northern part per year, while the central or southern parts only average at 1/50 years.

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Figure 3.1-2Average Number of Tropical Cyclones per Year

Figure 3.1-3 shows the track of major tropical cyclones which caused damages in the Mindanao Area. It shows that only limited tropical cyclones directly attacked the Mindanao Area. Cotabato City experienced serious flooding on June 2008 and May-June 2011. In June 2008, Typhoon Frank passed some 500 km away from Cotabato City, while in May-June 2011, Tropical Storm Chedeng passed much farther than Typhoon Frank as indicated in Figure 3.1-3. For both cases, the MRB received continuous rainfall by strengthening of wind flow of Southwest Monsoon on the MRB due to Tropical Cyclones.

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Figure 3.1-3Historical Tropical Typhoon Tracks, which Affected Mindanao

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3.2 MONTHLY VARIATION OF CLIMATIC PARAMETERS

3.2.1 Rainfall

(1) Data Availability

Figure 3.2-1 shows the location of rainfall gauges under PAGASA in and around the Mindanao River Basin while Table 3.2.1 lists the period of observation and type of rainfall gauge used in measuring rainfall.

Figure 3.2-1Location of Rainfall Station in and around the Mindanao River Basin

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Table 3.2-1List of PAGASA Stations in and around the Mindanao River Basin

Station ID Station Name Gauge Type Longitude Latitude Record Start Record End746 Cotabato City, Maguindanao Automatic 124.216667 7.166667 1986 Operating747 Lumbia Airport, Misamis Oriental Automatic 124.616667 8.433333 1977 Operating748 Cagayan de Oro, Misamis Oriental Automatic 124.633333 8.433333 1961 2010751 Malaybalay, Bukidnon Automatic 125.083333 8.150000 1961 Operating753 Davao City, Agusa del Sur Automatic 125.650000 7.116667 1961 Operating851 General Santos, South Cotabato Automatic 125.183333 6.116667 1961 Operating1008 Kisolon, Sumilao, Bukidnon Manual 124.883333 8.283333 1980 2007, 20101202 Kapatagan, Lanao del Norte Manual 123.766667 7.850000 1971 20001204 Parang, Maguindanao Manual 124.266667 7.383333 1972 20081205 Carmen, Tacuron, Sultan Kudarat Manual 124.616667 6.783333 1960 2004

(2) Annual and Monthly Variation

Annual and monthly variations of rainfall were computed for the stations ofMalaybalay (751), Cotabato City (745), Parang (1204), Davao (753), Carmen (1205) and General Santos (851).

Figure 3.2-2 shows the annual rainfall amount.

Figure 3.2-2Changes in Annual Rainfall in Stations Located In and around

The Mindanao River Basin

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0

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3000

3500

4000

4500 1950

1960

1970

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2000

2010

2020

(mm

/yea

r)

751 Malaybalay

2667 mm





Annual rainfall on the northwestern and western area of the basin reaches 2,500 mm to 3,000 mm, while it decreases on the southern area of the basin, wherein it is less than 1,000 m in Carmen and General Santos.

Figure 3.2-3Monthly Rainfall Distribution at Stations Located in and

Around Mindanao River Basin

Figure 3.2-3 indicates monthly variation of rainfall at the different rainfall stations. For Mindanao River Basin, there is no rainy and dry seasons, rainfalls throughout the year. In the northwestern and western area, rainfall increases from May to November compared to the remaining months.

3.2.2 Rainfall Analysis

a) Methodology

Thedesign storm hyetograph shall be computed using the procedures described in the “Manual on Flood Control Planning, 2003” by the DPWH (hereinafter referred as the Manual) enumerated below.

(1) Obtain a specific coefficient β and one-day rainfall from the figures attached in the Manual.

(2) Compute a parameter b of RIDF from β and obtain the RIDF

(3) Prepare hyetograph from the RIDF by the alternate block method.

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148

110 104 116

238

312 334

305 305 320

185

143

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300

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400

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

(mm

/mon

)

751 Malaybalay

89 88

133 149

277

329 323

244 259 282

219

132

0

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100

150

200

250

300

350

400

450


(mm

/mon

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746 Cotabato City

119 104 127

239

328

368 385 370

354 349

231

138

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150

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250

300

350

400

450


1204 Parang

(mm

/mon

)

132 113

97 135

180 191 161 175 178 173

135 110

0

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300

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400

450


(mm

/mon

)

753 Davao

35.1 42.9 32.8 55.1

91.8 121.8

103.3 106.5 92.8 104.0 66.5 55.2

0

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150

200

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400

450


(mm

/mon

)

1205 Carmen

79 63 49 54

75 108 100 87 87 99

81 73

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400

450


(mm

/mon

)

851 General Santos





(4) Obtain design hyetograph (basin mean) from the hyetograph computed above and area reduction factor described in the Manual.

3.2.3 Probable Hyetograph

a) Probable Hyetograph

The table below shows probable daily rainfall obtained from Attachment 4.5 Probable 1-Day Rainfall of the Manual.

Table 3.2-2Probable Daily Rainfall

b) Rainfall Intensity Duration Curve (RIDF)

A specific coefficient of β was read from Attachment 4.4 of the Manual. Substituting β (=10) to the following equation, parameter b was obtained as 0.72.

Using the parameter b (=0.72), RIDF becomes,

where, It = rainfall intensity for duration t (mm/hr), I24 = daily rainfall intensity (mm/hr), and t = hr.

The Ambal–Simuay basins has a catchment area of 759.16 km2. The corresponding reduction factor for this catchment area is 0.87. Basin mean rainfall was calculated using Horton’s Formula as defined below.

Where, r = basin mean rainfall (mm), ro=point rainfall (mm), and A=catchment area (km2)

Table 3.2-3 indicates the design hyetograph for the Ambal–Simuay River Basin from 2-yr to 100-yr return period and Figure 3.2-4 shows the design hyetograph for 25 year return period.

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Return PeriodDaily Rainfall

(mm)

Daily Rainfall Intensity(mm/hr)

2 120 5.005 150 6.25

10 160 6.6725 180 7.5050 200 8.33

100 225 9.38





Table 3.2-3Design Hyetograph for Ambal-Simuay Basin

2- yr 5-yr 10-yr 25-yr 50-yr 100-yr1 1.10 1.37 1.46 1.65 1.83 2.06

2 1.17 1.47 1.56 1.76 1.96 2.203 1.26 1.58 1.69 1.90 2.11 2.374 1.37 1.72 1.83 2.06 2.29 2.585 1.51 1.89 2.01 2.26 2.51 2.836 1.68 2.10 2.24 2.52 2.80 3.157 1.91 2.38 2.54 2.86 3.18 3.578 2.22 2.78 2.96 3.33 3.70 4.179 2.70 3.38 3.60 4.05 4.50 5.06

10 3.53 4.41 4.71 5.29 5.88 6.6211 5.44 6.79 7.25 8.15 9.06 10.1912 38.00 47.50 50.66 57.00 63.33 71.2513 7.99 9.99 10.66 11.99 13.32 14.9914 4.24 5.30 5.66 6.36 7.07 7.9515 3.05 3.81 4.07 4.57 5.08 5.7216 2.43 3.04 3.24 3.65 4.06 4.5617 2.05 2.56 2.73 3.07 3.42 3.8418 1.78 2.23 2.38 2.68 2.97 3.3519 1.59 1.99 2.12 2.38 2.65 2.9820 1.44 1.80 1.92 2.16 2.40 2.6921 1.32 1.65 1.75 1.97 2.19 2.4722 1.22 1.52 1.62 1.83 2.03 2.2823 1.13 1.42 1.51 1.70 1.89 2.1324 1.06 1.33 1.42 1.59 1.77 1.99

Return PeriodTime (hr)

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5

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30

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45

50

55

60

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Rai

nfal

l (m

m)

Duration (hr)

Figure 3.2-425–yr Design Hyetograph

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3.2.4 Runoff Analysis

a) Selection of Runoff Model

To do the runoff analysis and obtain the probable discharge, the HEC-HMS model tool is used in the analysis. This “HEC-HMS model” is composed of 1) runoff coefficient method for loss model, 2) SCS – Unit Hydrograph model for sub-basin model and 3) Muskingum-Cunge method for routine model of channels.

1) Explanation of Runoff Model

i) Runoff Coefficient Method

The runoff coefficient method is normally used in the Rational Formula as explained below,

where, Q = maximum flood discharge in m3/s, c = dimensionless runoff coefficient, i = rainfall intensity within time tc in mm/hr, A = catchment area in km2, and tc = time of concentration.

The runoff coefficient method is used as the loss model in the runoff model. Runoff coefficient varies with land use as shown in Table 3.2-4.

Table 3.2-4Runoff Coefficients of Different Land Use Category

Surface Characteristics Runoff Coefficient

Low (Urban Area) 0.50Middle (Urban Area) 0.65High (Urban Area) 0.80Factory 0.50Open Space 0.35Paddy 0.10FarmLand 0.30Mountain 0.80

ii) SCS Unit Hydrograph Method

This method developed by the Soil Conservation Service (SCS) currently known as Natural Resources Conservation Service is based on an analysis of a number of natural hydrographs. The SCS dimensionless hydrograph is a synthetic unit hydrograph in which the discharge is expressed by the ratio of discharge (Ur) to peak discharge (Up) and the time by the ratio of time (t) to the time of peak of unit hydrograph (Tp) (Figure 3.2-5). Based on the study of gauged rainfall and runoff for a large number of small rural watersheds, Up and Tp can be determined from the time of concentration (tc) of the basin and from Up and Tp, the unit hydrograph for the basin can be obtained.

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Figure 3.2-5SCS Unit Hydrograph

The SCS unit hydrograph method is used as the sub-basin model in the runoff model of the Ambal-Simuay basin. Using a triangular unit hydrograph, the peak discharge, Up and the time to peak, Tp can be computed using the following equations,

Where, is the effective rainfall durationor time-base of the hydrograph which is five times the tlag, and tlagis the basin lag or the time difference between the center mass of rainfall excess and the peak of the unit hydrograph. For the estimation of the SCS unit hydrograph lag for gauged headwater sub-watersheds, HEC-HMS is used. For the ungauged watersheds, unit hydrograph lag time can be related to the time of

concentration (tc) as tlag= 0.6 tc.

The equation used in computing the time of concentration is stated as,

tc= ti+ tf (refer to Figure 3.2-6)

Where, ti = inlet time or the time it takes for flow from the remotest point to the inlet point or farthest point of river channel, tf= flow time or the time it takes from the inlet point or farthest point of the river channel to the outlet point or point under consideration. Flow time can be computed using the following equation,

tf = L / V

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Where, L = length of river channel from its outlet point to its farthest point (m), and V = flow velocity (m/s). Flow velocity is computed from Kraven’ s formula as shown below.

Table 3.2-5Kraven’s Formula for Flow Velocity

Riverbed Gradient (ib)

ib> 1/100(Steep slope)

I/100 >ib> 1/200ib< 1/200

(Mild slope)

Flow Velocity (m/s) 3.5 3.0 2.1

Figure 3.2-6Inlet and Outlet Points of the Rational Formula

Inlet time (min) is computed by finding the inlet point which should have a catchment area less than or equal to 2 km2. The equation is,

Rational Formula states that if rainfall intensity, i, begins instantaneously and continues indefinitely, the rate of runoff will increase until the time of concentration (tc), when all of the watershed is contributing to the flow at the outlet or point under consideration. The time of concentration is the time that the runoff is established. It is the time required for water to flow from the remotest part of the drainage area to the outlet point. In this way, the design discharge can be determined not by the instantaneous rainfall but by the accumulated rainfall which considers the infiltration capacity of the soil in the watershed area.

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ii) Muskingum–Cunge Method

This method is used for routine model of channels for the Ambal-Simuay River. Muskingum-Cunge method is used to describe the transformation of storm rainfall into runoff over a watershed to produce a flow hydrograph for the watershed outlet. This hydrograph becomes the input at the upstream end of a river system and route it to the downstream.

Muskingum-Cunge method is based on the continuity equation and the diffusion form of the momentum equation in solving the unsteady flood flow. Outflow at time t, (Ot) is computed as follows:

Ot = C1It-1 + C2It+ C3Ot-1 + C4 (qL x)

where: qL= lateral inflow, and C1 to C4 = coefficients which are functions of wave celerity (C) and discharge (Q).

For the computation of the probable discharge, HEC- HMS is used

3.2.5 Modeling and Basin Division

For the application of the Runoff Coefficient – SCS Unit Hydrograph –Muskingum–Cunge method, delineation of sub-basins and channels is necessary. The following figures (Figure 3.2-7 and Figure 3.2-8) show the basin division and runoff model of the Ambal-Simuay River Basin for runoff analysis and planning.

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Figure 3.2-7Ambal–Simuay

Sub-basins

Figure 3.2-8Runoff Model

for Ambal-Simuay Basin

a) Runoff

Coefficient

Since runoff coefficient varies with land use, data from Table 3.2-4 were used considering the actual land use category of the river basin. The land use category of the river basin can be summarized into land covers with their respective equivalent runoff coefficients. With these, the runoff coefficients for each sub-basin were determined as shown below (Table 3.2-6).

Table 3.2-6Runoff Coefficients of the Ambal-Simuay Basin

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MountainOpen Space/

Grassland/Shrubs/ Mangrove

Farmland

f (0.8) f (0.35) f (0.3)AS 01 60.65 29.36 7.58 0.61AS 02 19.86 18.70 59.39 0.40AS 03 11.09 40.76 59.39 0.37AS 04 30.66 12.02 55.24 0.45AS 05 3.34 17.17 77.37 0.32AS 06 0.00 0.00 97.30 0.29AS 07 0.00 0.00 97.80 0.29AS 08 0.00 4.82 92.85 0.30

Land Cover (%)

Sub-basinRunoff

Coefficient

b) Parameters for SCS Unit Hydrograph Method

In order to determine the probable discharge hydrograph of the downstream end of the river basin, parameters that are tabulated in Table 3.2-6 were determined, in which catchment area (CA), length of basin (L) and elevation difference (ΔH) are measured using the USGS GDEM Version 2 map of the area.

Table 3.2-7Parameters for the Channels of the Ambal-Simuay Basin

Ca L ΔH tf ti tc tlag

(km2) (km) (m) (min) (min) (min) (min)AS01 169.53 31.35 1493.00 21.00 0.05 149.3 30.00 179.3 107.6AS02 104.82 21.45 1130.00 18.98 0.05 102.1 30.00 132.1 79.3AS03 71.86 23.43 1109.00 21.13 0.05 111.6 30.00 141.6 85.0AS04 117.07 23.81 929.00 25.63 0.04 113.4 30.00 143.4 86.0AS05 96.01 15.80 121.95 129.55 0.01 87.8 30.00 117.8 70.7AS06 21.79 10.50 98.00 107.16 0.01 58.3 30.00 88.3 53.0AS07 48.79 10.52 20.00 526.04 0.00 83.5 30.00 113.5 68.1AS08 129.29 18.44 24.00 768.24 0.00 146.3 30.00 176.3 105.8

Subbasin Slope1/Slope

In the table above, tfand ti are flow time and inlet time, respectively. Depending on the riverbed slope, the flow velocity used in Kraven’s Formula was based in Table 3.2-5.

c) Parameters for Muskingum–Cunge Method

Table 3.2-8 summarizes the features of the river channels, which are required for the Muskingum–Cunge method. These were measured from the USGS GDEM Version 2 maps.

Table 3.2-8Parameters for the Channels of Ambal-Simuay Rivers

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ChannelLength ΔH

1/slopeB

(km) (m) (m)

RIV01 15.80 122 130 388.31RIV02 10.50 98 107 412.43RIV03 10.52 20 526 449.43RIV04 18.44 24 768 510.80

3.2.6 Probable Discharge

Based on the the design hyetograph (see Table 3.2-3), the peak rainfall occurs at the 12th hour a 24-hr rainfall for the 2-yr, 5-yr, 10-yr, 25-yr, 50-yr and 100- yr period. The discharge hydrograph shown below (Figure 3.2-9) indicates that the peak discharge at the downstream end of the Ambal-Simuay river stretch occurs five hours after the occurrence of excess rainfall for a 2-yr, 5-yr, 10-yr and 25-yr return period rainfall; and four hours for a 100-yr return period rainfall.

Following the discharge hydrograph, the 25-year design discharge is 1,609.7 m3/s.

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Dis

char

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ate,

m3/s

Time, hours

2yr

5yr

10yr

25yr

50yr

100yr

Figure 3.2-9Probable Discharge at the Downstream end of the Ambal-Simuay River

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3.3 ESTIMATION OF SEDIMENT YIELD

The continuous aggradation of silt materials in the study decreased the flow capacity of the Ambal-Simuay River. The following analysis shall provide an estimate of sediment loss and yield within the Ambal-Simuay Sub-basin.

3.3.1 Major Sediment Sources and Mechanism

The sediment yield was estimated through the following procedures(shown in Figure 3.3-1): 1) estimation of gross sediment sources by erosion, 2) prediction of sediment delivery ratio using the empirical relationship with drainage area, and 3) estimation of sediment yield using obtained sediment delivery ratio.

Figure 3.3-1Flowchart of Soil Sedimentation Yields

3.3.2 Sediment Source by Surface Erosion

a) Estimation of Sediment Source

Sediment source due to surface erosion was estimated using the Universal Soil Loss Equation (USLE), which was chiefly developed to predict the long-term average soil loss.

(Eq. 3.3-1)

where, ESE : Mean annual sediment source or soil loss by surface

erosion including landslide (ton/ha/year)R : Rainfall erosion factorK : Soil erodibility factorL : Slope length factorS : Slope steepness factorC : Cover or crop management factorP : Supporting practice factor

These parameters for the USLE equation calculation were determined from various sources, and processed chiefly by Geographic Information System (GIS) technology.

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Estimation of Gross sediment sources by erosion, EG

Sediment source by surface erosion and landslide, ESE

Prediction of sediment delivery ratio, SDR, through drainage area, A

Estimation of sediment yield, SY





The following describes the determination of the USLE parameters.

1) Rainfall Erosion Factor (R)

The rainfall erosion factor, R, was calculated by the following equation, which was modified by Utomo et al. (1983).

R =0.32(Y/100)2.5

where,

R: Rainfall erosion factor

Y: Yearly rainfall (mm)

Rainfall data gathered from rainfall stations within the Mindanao river basin were used to determine the yearly rainfall intensity for each sub-basin. From these data, an isohyetal map was generated to determine the annual rainfall intensity in each sub-basin.

Table 3.3-1Rainfall Erosion Factor

Yearly rainfall intensity (mm) Rainfall erosion Index

Range Mean value R

<1500 1500 279

1,500-2,000 1,750 410

2,000-2,500 2,250 768

2,500-3,000 2,750 1,269

3,000-3,500 3,250 1,927

Figure 3.3-3 shows the distribution of the rainfall erosion factor used in the estimation of sediment loss.

2) Soil Erodibility Factor (K)

The soil erodibility factor, K, is a measure of the susceptibility of soil particles to detachment and transport by rainfall and runoff. It is generally obtained using the Nomograph for soil erodibility estimation after Wishmeir et al. (1971). However, due to lack of data, the following were used for the estimation of K.

i) Soil types distributed in the study area were quoted from the soil map at the scale of 1:150,000, prepared by Department of Environment and Natural Resources (DENR) and the soil map shape file from the Bureau of Soils and Water Management - Department of Agriculture;

ii) K value of each soil type was cited from previous measurement done by Taniyama et al. (1998) and Stone (2000), as shown in the following table.

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Table 3.3-2Estimated K Values

Soil Type K Value1

Mountain Soil (Undiffirentiated) 0.19 Silt Loam 0.38 Sandy Loam 0.13 Sandy 0.02 River Wash 0.30 Clay Loam 0.30 Clay 0.22

Source: 1Taniyama et al. (1998) and Stone (2000)

3) Slope Factor (LS)

Slope factor (LS), which reflects the topographical (length-steepness) factor, was estimated by the following equations:

, and

where,

L: Slope length factor

S: Slope steepness factor

l: slope length of collapse area or target cell (m)

θ: Gradient (degree) of specific slope such as collapse or target cell

L is the horizontal length of slope measured. It is the point of origin where water will begin flowing down the slope to the point where concentrated flow begins, such as where water flows into a ditch, channel, or deposition occurs and water disperse.

L and S were obtained from digital elevation data produced by digitizing elevation contour on the 1:50,000 topographical map.

4) Cover or Crop Management Factor (C)

Cover or crop management factor (C) represents the ratio of the soil loss eroded from land (slope area) that is cropped under specific conditions to that which is eroded from clean-tilled fallow under identical slope and rainfall.

The land use map of the study area was obtained to determine the C value for each sub-basin.

5) Supporting practice factor (P)

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The support practice factor (P) reflects the effects of countermeasures that will reduce the amount and rate of the water runoff and thus reduce the amount of erosion. No soil conservation works have been implemented in the study area, therefore, P factor was selected as P=1.0.

6) Estimated sediment source by surface erosion

Using the formula of sediment source estimation (eq. 3.3-1), the estimated soil loss by surface erosion and landslide at present condition can be determined.

From the computed ESE, the severity of soil erosion in an area may be classified from the five categories shown in Table 3.3-3.

Table 3.3-3Classification Criteria of Soil Surface Erosion

ClassSoil Erosion

ClassSoil loss rate

(ton/ha/y)

1 Very Low 0 – 7

2 Low 8 – 12

3 Moderate 13 – 25

4 High 26 – 37

5 Very High > 37

3.3.3 Estimation of Sediment Delivery Ratio

The sediment delivery ratio is a function of the drainage area, the distance that the soil particles travel towards the stream and the sediment size of the source particles. That is, the larger the drainage area and longer the distance that the particles have to travel, the lesser the sediments that reach the stream. Also, sediments of finer materials are lighter and thus easier to transport. Thus, an area with that is mostly composed of light sediments have high delivery ratio.

Because of data availability in relation to the study area, the followingequations were used in the analysis to predict SDR in the study area.These equations relate the SDR and the drainage area.

a) Rainfall-runoff and SDR (Boyce, 1975)

SDR = (A)-0.2 (Eq. 3.3-2)

where,A = drainage area in square miles.

b) Drainage area and SDR(Renfro,1975)

log (SDR) = 1.7935 - 0.14191 log (A) (Eq. 3.3-3)

where, A = drainage area in km2

c) Particle size and SDR (USDA, 1979)

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Source: Manual on Vulnerability Assessment of Watershed, DENR 2011





SDR = 0.51A-0.11 (Eq. 3.3-4)

where, A = drainage area in square miles

In relation to the study area, silt materials are transported from the upstream of the river basin causing aggradation along the Ambal-Simuay river system. To determine the sediment yield of the Ambal-Simuay sub-basin, the computed mean SDRof each sub-basin shall be used in the computation. (See Table 3.3-4)

Table 3.3-4Ambal-Simuay Basin Sediment Delivery Ratio

Sub-basin Name

Total Area(km2)

SDR1 SDR2 SDR3 Mean

AS01 169.53 0.43 0.25 0.32 0.33

AS02 104.82 0.48 0.26 0.34 0.36

AS03 71.86 0.51 0.28 0.35 0.38

AS04 117.07 0.47 0.26 0.34 0.35

AS05 96.01 0.49 0.27 0.34 0.37

AS06 21.79 0.65 0.32 0.40 0.46

AS07 48.79 0.56 0.29 0.37 0.41

AS08 129.29 0.46 0.26 0.33 0.35

Total 759.16 0.32 0.21 0.27 0.27

SDR1 to SDR3 values are derived from equations 3.3-2 to 3.3-4. As mentioned the mean value shall be used in the calculation of sediment yield.

3.3.4 Estimation of Sediment Yield

Sediment yield is the amount of material eroded from the land surface transported to the river system. Using the computed values of SDR, the sediment yield was calculated using the relationship

a) Sediment Loss Parameters

Each of the parameters defined in the USLE equation was derived using a GIS approach method as cited earlier. The procedures done in the estimation and the resulting grid files are presented below for every parameter.

1) Rainfall Erosion Factor, R

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2800 2900 3000 3100

2500 2750 3000 3100

2200 2500 2750 3000

2100 2200 2500 2500

Isohyets Polyline Isohyets Grid

Figure 3.3-2Diagram of Rainfall Erosion Factor Computation

Using the annual isohyet polyline (developed from rainfall data from PAGASA within and near the study area), a 30 x 30 meter rainfall grid was developed with the use of ArcGIS. An illustration of this procedure is shown in Figure 3.3-2. Shown in Figure 3.3-3 is the product of this function and this illustrates the distribution of the rainfall erosion factor within the river basin.

Figure 3.3-3Rainfall Erosion Factor for Each Sub-basin

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2) Soil Erodibility Factor, K

0.186 0.186 0.186 0.186

0.186 0.186 0.186 0.387

0.19 0.19 0.387 0.387

0.19 0.19 0.387 0.387

Soil Map (Polygon) Soil Map (Grid)

Mountainous Soil = 0.186

Sand = 0.19

Clay = 0.387

Figure 3.3-4Diagram of Soil Erodibility Factor Computation

The soil map from the Department of Agriculture – Bureau of Soils and Water Management was used in creating the raster file needed for the soil erodibility factor calculation (Figure 3.3-4).Values used for K are shown in Figure 3.3-5. Also indicated is the distribution of each soil type within the catchment area.

Figure 3.3-5Soil Erodibility Factor for Each Sub-basin

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3) Slope Factor, LS

For the LS factor computation, a 30 x 30 meter digital elevation model named “ELEVATION” was used. The following are the steps of procedure inside ArcGIS 9.3:

1. Convert the ELEVATION grid to Slope grid by using Spatial Analyst Surface Analysis Slope. Chose the “Degree” option in the dialog box and name the output as SLOPE.

2. Calculate the Flow Accumulation using Raster Calculator by encoding the following script:

FLOWACC = FlowAccumulation(FlowDirection([ELEVATION]))

3. Finally, calculate the Length-Slope factor by encoding the following script inside the Raster Calculator:

LS = Pow([FLOWACC]*30, 0.6) * Pow(Sin([SLOPE]*0.01745/0.09, 1.3)

The multiplier 30 of the [FLOWACC] is the cell size of the grid

The above procedure is illustrated in Figure 3.3-6 and the resulting LS grid is as shown in Figure 3.3-7.

Figure 3.3-6Diagram of LS Factor Computation

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Figure 3.3-7Slope Factor for Each Sub-basin

4) Cover or Crop Management Factor, C

The calculation of this parameter required the use of the land cover map extracted from the NAMRIA data. A crop management grid, as shown in the figure below, was also created using GIS to generate the corresponding land cover management factor in the river basin to be used in the sediment analysis (see Figure 3.3-8).

0.02 0.02 0.06 0.06

0.02 0.02 0.06 0.06

0.02 0.80 0.80 0.06

0.80 0.80 0.80 0.80

Cropping Management (Polygon)

Cropping Management (Grid)

Forest = 0.02

Rice field = .80

Grass land = 0.06

Figure 3.3-8Diagram of Cover or Crop Management Factor Computation

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Figure 3.3-9Cover or Crop Management Factor for Each Sub-basin

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2800 2900 3000 3100 0.186 0.186 0.186 0.186 3.84 3.53 0.91 0.83 0.02 0.02 0.06 0.06 40.0 38.1 30.5 28.6

2500 2750 3000 3100 X 0.186 0.186 0.186 0.387 X 4.19 3.67 0.87 0.38 X 0.02 0.02 0.06 0.06 = 39.0 37.5 29.1 27.3

2200 2500 2750 3000 0.19 0.19 0.387 0.387 0.89 0.02 0.01 0.36 0.02 0.8 0.8 0.06 7.4 6.7 8.5 25.2

2100 2200 2500 2500 0.19 0.19 0.387 0.387 0.02 0.01 0.01 0.01 0.8 0.8 0.8 0.8 5.8 3.0 9.0 10.3

Rainfall Erosion Factor (R) Soil Erodibility Factor (K) Length-Slope Factor (LS) Crop Management Factor (C) Soil Loss (tons/ha/yr)





b) Estimated Soil Loss, EG and Sediment Yield, SY

Figure 3.3-10Diagram of USLE Parameters Raster Calculation

As illustrated above (Figure 3.3-10), soil loss was computed using the grid file of each of the parameters of USLE. Using the sub-basin polygon and the computed soil loss grid, the soil loss per sub basin was evaluated using zonal statistics in ArcGIS as illustrated in Figure 3.3-11.

Figure 3.3-11Diagram of Soil Loss Computation for Each Sub-basin

Figure 3.3-12 shows the result of the procedure described above. This illustrates the distribution of soil loss in Ambal-Simuay river basin.

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AS01

AS02

AS03

AS0128.50AS0237.80

AS038.30

Zonal Statistics(Median)

Sub-Basin Boundary(Polygon)

Soil Loss(Grid)

Sub-basin Thematic Map of Soil Loss

(ton/ha/yr)(Polygon)





Figure 3.3-12Computed Soil Loss Using USLE

Summarized below is the computed sediment loss and sediment yield in each sub-basin.

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Table 3.3-5Estimated Soil Loss and Sediment Yield

DA

(sq.km) ton/ha/year mm/year m3/year ton/ha/year mm/year m

3/year

AMB01 65.79 0.36 0.03 1,691.7 0.39 0.14 0.01 654.8AMB12 22.89 0.51 0.04 833.8 0.46 0.23 0.02 380.2AMB13 16.50 10.01 0.72 11,796.8 0.48 4.80 0.34 5,661.5AMB14 11.67 10.01 0.72 8,345.8 0.51 5.07 0.36 4,228.8AMB15 14.26 10.01 0.72 10,197.2 0.49 4.91 0.35 5,006.9AMB16 11.93 7.72 0.55 6,577.2 0.50 3.90 0.28 3,321.3AMB22 21.16 10.01 0.72 15,132.7 0.46 4.62 0.33 6,985.0

Subtotal 164.20 48.63 3.47 570,370.7 0.47 22.84 1.63 267,848.8

AMB02 29.77 0.85 0.06 1,807.6 0.44 0.37 0.03 791.1AMB03 27.46 3.09 0.22 6,060.7 0.44 1.37 0.10 2,686.2AMB10 11.36 27.89 1.99 22,635.8 0.51 14.19 1.01 11,518.2AMB11 22.72 8.67 0.62 14,072.5 0.46 3.96 0.28 6,423.9AMB17 10.16 21.46 1.53 15,580.8 0.52 11.11 0.79 8,068.5

Subtotal 101.48 61.96 4.43 449,132.6 0.47 29.30 2.09 212,355.6

AMB04 25.10 4.29 0.31 7,692.1 0.45 1.93 0.14 3,457.2Subtotal 25.10 4.29 0.31 7,692.1 0.45 1.93 0.14 3,457.2

AMB05 46.76 2.90 0.21 9,685.0 0.41 1.18 0.08 3,952.0AMB06 31.50 0.43 0.03 967.5 0.43 0.19 0.01 419.8AMB07 22.69 0.40 0.03 648.4 0.46 0.18 0.01 296.0AMB08 19.52 3.09 0.22 4,308.8 0.47 1.44 0.10 2,014.1AMB09 21.84 0.47 0.03 733.2 0.46 0.22 0.02 336.8AMB18 5.01 7.15 0.51 2,559.1 0.58 4.14 0.30 1,482.0

Subtotal 147.32 14.44 1.03 151,953.2 0.47 6.75 0.48 71,022.2

AMB19 16.50 7.12 0.51 8,391.1 0.48 3.42 0.24 4,027.0AMB21 27.56 8.87 0.63 17,461.0 0.44 3.93 0.28 7,734.5AMB23 23.25 50.07 3.58 83,143.3 0.45 22.78 1.63 37,819.0AMB24 10.54 7.15 0.51 5,384.3 0.51 3.68 0.26 2,772.3AMB26 24.24 50.07 3.58 86,705.6 0.45 22.63 1.62 39,182.1

Subtotal 102.09 123.28 8.81 898,999.6 0.47 57.81 4.13 421,544.2

AMB25 10.41 35.76 2.55 26,598.3 0.52 18.45 1.32 13,721.7Subtotal 10.41 35.76 2.55 26,598.3 0.52 18.45 1.32 13,721.7

AMB20 21.79 6.83 0.49 10,628.2 0.46 3.14 0.22 4,883.7AMB27 17.78 21.46 1.53 27,256.2 0.47 10.18 0.73 12,928.3

Subtotal 39.57 28.29 2.02 79,953.4 0.47 13.21 0.94 37,331.2

AMB28 31.00 7.15 0.51 15,834.5 0.43 3.11 0.22 6,886.6AMB29 14.98 5.87 0.42 6,279.9 0.49 2.86 0.20 3,059.9AMB30 19.07 5.85 0.42 7,969.7 0.47 2.74 0.20 3,739.0AMB31 32.71 7.41 0.53 17,311.3 0.43 3.20 0.23 7,466.6AMB32 13.84 7.75 0.55 7,662.8 0.49 3.82 0.27 3,780.1AMB33 37.94 7.93 0.57 21,487.6 0.42 3.34 0.24 9,056.9AMB34 5.14 5.85 0.42 2,149.5 0.58 3.37 0.24 1,239.7AMB35 8.68 3.33 0.24 2,064.2 0.53 1.77 0.13 1,095.9AMB36 5.61 0.00 0.00 0.0 0.57 0.00 0.00 0.0

Subtotal 168.98 51.14 3.65 617,245.4 0.49 25.08 1.79 302,713.8

TOTAL 759.16 8.99 0.64 487,654.44 0.48 4.19 0.30 227,077.64

AS01

AS04

AS05

AS06

AS07

AS08

Sub-basin CodeSoil Loss Mean

SDR

Sediment Yield

AS02

AS03

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Figure 3.3-13Severity of Soil Loss for Each Sub-basin

The figure above shows the distribution and extent of soil loss in the river basin. From this figure, it shows that sub-basins AS01, AS02, and AS05 have high soil loss rates that may reach up to a range of 26 to 37 tons/ha/year. These sub-basins lie mainly on the municipality of Buldon, Maguindanao. On the upper part of the basin, where vegetation is thick, severity is generally “low”. On the lower part of the basin, soil erosion can be classified from “low” to “medium”. This

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mapwillgive hint on which particular areas need soil conservation and management practices in order to reduce further soil loss in the basin.

Figure 3.3-14Accumulative Sediment Yield per Sub-basin

Figure 3.3-14 illustrates the distribution of sediment yield for each sub-basin. This takes into account the accumulation and transport of soil loss from upstream to downstream sub-basins. The downstream part of the basin, where all

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sediments from the upstream are deposited, has a sediment yield of 227,078 cubic meters in one year. This will correspond to a depth of 0.20 meters of sediments considering an average river width of 150 meters extending to a river length of 8 kilometres. This amount of sediment is mainly deposited on the municipality of Sultan Kudarat, Maguindanao and on the City of Cotabato as projected in the figure.

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