Modeling of riparian vegetated buffer strip width and placement

Ecological Engineering 23 (2004) 327–339

Modeling of riparian vegetated buffer strip width and placement A case study in Shei Pa National Park, Taiwan Young-Fa Lina,b , Chao-Yuan Linc , Wen-Chieh Choud,∗ , Wen-Tzu Line , Jing-Shyan Tsaif , Cho-Fu Wub a Shei Pa National Park Headquarters, Miaoli County 364, Taiwan Institute of Construction Management, Chung Hua University, 707, Sec.2, Wu-Fu Rd., Hsinchu City 300, Taiwan c Department of Soil and Water Conservation, National Chung Hsing University, 250, Kuo-Kuang Road, Taichung City 402, Taiwan d Department of Civil Engineering, Chung Hua University, 707, Sec.2, Wu-Fu Rd., Hsinchu City 300, Taiwan Graduate Institute of Environmental Planning and Design, Ming Dao University, 369 Wen-Hua Rd., Peetow, Changhua County 523, Taiwan f Department of Landscape Architecture, Chung Hua University, 707, Sec.2, Wu-Fu Rd., Hsinchu City 300, Taiwan b

e

Received 19 February 2004; received in revised form 1 November 2004; accepted 9 November 2004

Abstract This study addressed the suitable width for riparian vegetated buffer strips (RVBS) using topographic analyses, attenuation curves, and an index model. The Chi Chia Wang Stream is susceptible to pollution because of highly saturated hydraulic conductivity and excessive fertilizer use in the nearby cultivated lands. The buffer strip widths calculated from a potassium attenuation curve in the vegetable plot were the widest due to easy potassium movement in the soil. A potassium safety soil depth of 8.81 m was calculated or estimated for the vegetable area. The buffer width derived from this safety depth is recommended for maximum agricultural nonpoint source pollution (ANSP) prevention. © 2004 Elsevier B.V. All rights reserved. Keywords: Agricultural nonpoint source pollution; Geographical information system; Attenuation curves; Index model

1. Introduction Vegetated buffer strips can offer pollutant buffering and riverbank stabilization. They can effectively reduce the nonpoint source pollution from agricultural lands by using appropriate streamside vegetation and area development. Pollutant buffering can control nonpoint ∗

Corresponding author. E-mail address: [email protected] (W.-C. Chou).

0925-8574/$ – see front matter © 2004 Elsevier B.V. All rights reserved. doi:10.1016/j.ecoleng.2004.11.006

source pollution, improve soil and water conservation, and enhance biodiversity. The regulations governing riparian vegetated buffer strips (RVBS) have not yet been established in Taiwan. To reduce or prevent river water pollution from agricultural nonpoint sources, it is better to establish standards for RVBS through experimental results and modeling. This study focused on the Wu Ling Farm area, neighboring the Chi Chia Wang Stream in Shei Pa National Park in Taiwan. Combining field investigation,

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topographic analyses, and chemical simulations, we sought the appropriate RVBS width to provide to policy-making authorities for watershed management and reduction of the agricultural impact on the downstream river and reservoir water quality. Since 1961, Wu Ling Farm development projects have utilized lands near the Chi Chia Wang Stream. The slope of these lands is steep, which speeds runoff. Agricultural development of these slopes for fruit trees and vegetables deeply impaired the habitat of land-locked salmon (Oncorhynchus masou formosanus). Because the profit for cultivation of fruit trees in this watershed has declined significantly, farmers have converted the orchards into vegetable fields. Vegetables are shortterm crops, normally with two to three harvests per year. Soil tillage, weed removal, and pesticide and fertilizer use on vegetables are more intensive than for orchards. Additionally, both rainfall and drought season irrigation accelerate fertilizer transport through the soil. RVBS are gaining recognition and playing more important roles in land conservation polices. In cooperation between Taiwan’s Council of Agriculture and the USDA, research on RVBS is one of the top priority topics. RVBS are natural or planted vegetation areas located between potential pollution sources and surface water bodies. Major purposes of this vegetation are reductions in overland runoff, sediments, nutrients, and pesticides. Din and Chen (1979) suggested that 10-m buffer strips would be enough for short-term effectiveness on undissolved pesticides (chlordimeform) in Taiwan. For soluble pesticides, the vegetated area should be expanded to 30 m. Sometimes 60 m of vegetation is needed for adequate results. Based on the Liu Kuei experimental forest, Hsia et al. (1990) suggested that for forest lands in Southern Taiwan, or similar areas, the buffer width required for the sediment delivery distance from road construction can be calculated as: F = 10 + 0.03s2 , where F is the buffer strip width in meters and s represents the slope in degrees. Numerous studies have indicated that vegetated buffer strips are one of the most effective management strategies, especially for nonpoint source pollution control (Dillaha et al., 1989; Leeds-Harrison et al., 1999; Dosskey, 2002; Qin, 2003). Several models have been developed to simulate the effectiveness of vegetated buffer strips. Williams and Nicks (1988) selected small-

scale plots around the United States and evaluated the soil erosion, sediment, and nutrient transportation control effects from vegetated buffer strips using Chemicals, Runoff and Erosion from Agricultural Management Systems (CREAMS). Lee et al. (1989) developed a mathematical model called GRAss-Phosphorus (GRAPH) to analyze runoff and phosphate transport in grass buffer strips under a single storm. Hayes and Dillaha (1992) proposed an effectiveness evaluation for grass buffer strips in controlling runoff and sediment resistance using the WEPP model (Laflen et al., 1991) and GRASSF model (Hayes and Hairston, 1983). Xiang (1996) combined a geographical information system (GIS) and pollutant detention equation to delineate vegetated buffers. To display the topographic attributes, the mathematical model combining application GIS technology is best in evaluating the width and placement of vegetated buffers. The effectiveness of vegetated buffer strips is dependent on the vegetation species, buffer strips width, placement, slope, and rainfall patterns. If the width of the vegetated buffer strips is not sufficient, it will not attain the desired effectiveness. Conversely, if the width is too great, it will cause agricultural land waste, preventing farmers’ interest in cooperating with environmental preservation efforts. For the above reasons, it is important to set a reasonable width range when implementing slope conservation plans. In the U.S., Section 319 of the Clear Water Act of 1987 requires the States to identify and submit best management practices (BMPs) for USEPA approval to help control nonpoint pollution sources. However, the improvement in water quality from buffer strips as a BMP method cannot be predicted. Ironically, farmers complain that inappropriate regulatory buffer strip widths decrease the amount of productive land, while environmental scientists argue that the width in the current regulations is not enough to control undesirable drainage water quality in farming watersheds (Puvis et al., 1989).

2. Methods 2.1. Study area Wu Ling Farm, altitude 1740–2100 m, area 7000 ha, annual mean temperature around 15 ◦ C, is located in the Chi Chia Wang Stream riverine area in Shei Pa

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The nutrient concentration in the 60–70 cm soil depth from nearby the forestland was used as the environmental background value. On-site measured attenuation curves of nutrients in the soil layer were used to predict safety depth by regression analyses and to provide the calculation of width and placement of vegetated buffer strips. (2) Prediction by index model The calculation processes by index model are: 1. Convection The convection of soil moisture is sometimes called Darcian flow. The solute convection quantity Jc is directly proportional to the solute concentration: Fig. 1. Study watershed in northern Taiwan.

National Park, Taiwan (Fig. 1). This farm is surrounded by giant woods, natural beautiful scenery, and one of the most popular summer resorts in Taiwan. The Chung Chin white peach, Fuji apple, summer vegetables, white crow, and land-locked salmon (O. masou formosanus) are the most famous representatives in this area. They are called the “Wu Ling Five Treasures”. The Chi Chia Wang stream meanders through the farmlands. The view is gorgeous. 2.2. Analysis procedures 2.2.1. Soil physiochemical analyses Soil samples were collected from different soil layers in representative land-use areas. After being airdried, crushed, and sieved with a #10 sieve, according to soil analysis methods (Klute, 1986; Page, 1982), the following analyzed properties were measured: pH, texture, conductivity, saturated hydraulic conductivity, available phosphorus, exchangeable cations (K, Na, Ca, Mg), extractable trace elements (Fe, Mn, Zn, Cu), and soluble anions (Cl− , NO2 − , NO3 − , SO4 2− ). 2.2.2. Methods for modeling vegetated buffer strips Pollutants’ attenuation curve measured on-site and calculated from index model were employed to predict the width of RVBS in this study. (1) Prediction by nutrient attenuation curves

Jc = qC

(1)

where Jc is the solute convection rate (M/TL2 ; M: mass, T: time, L: length), q the flux (L/T), and C the solute concentration (M/L3 ). v¯ =

q θ

(2)

where v¯ is the mean flow velocity (L/T), q the flux (L/T), and θ the volumetric wetness (Vwater /Vsoil ; V is the volume, describes the volume of water per unit volume of soil and is usually expressed as a percentage by volume). Combining (1) and (2), Eq. (3) can be obtained as Jc = v¯ θC

(3)

2. Diffusion According to Fick’s law, the diffusion rate is related to the concentration gradient: Jd = −D0

dC dx

(4)

where Jd is the diffusion rate (M/FL2 ), D0 the diffusion coefficient (L2 /T), and dC/dx the concentration gradient (M/L4 ). Because the aqueous phase only occupies part of the soil volume and soil pores are tortuous in shape, the effective diffusion coefficient will be less than diffusion coefficient. The equation can be written as Ds = D0 θξ

(5)

where Ds is the effective diffuse coefficient (L2 /T), θ the volumetric wetness, and ξ the tortuosity.

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Eq. (4) for an unsaturated soil aqueous phase can be written as
 dC Jd = −Ds (θ) (6) dx 3. Dispersion The hydrodynamic dispersion equation is similar to the diffusion equation. Dispersion coefficient has a linear relationship to the mean flow velocity: Dh = α¯v

(7)

where Dk is the dispersion coefficient (L2 /T), α the experimental parameter (L), v¯ the mean flow velocity (L/T).
 dC Jβ = −Dh (¯v) (8) dx where Jβ is the dispersion rate (M/FL2 ). 4. Transportation Transportation (J) of solute includes, convection (Jc ), diffusion (Jd ), and dispersion (Jβ ), three phenomena. Combining Eqs. (3), (6) and (8), Eq. (9) can be obtained as 

 ∂C ∂C J = v¯ θC − Ds (θ) + Dh (¯v) (9) ∂x ∂x Actually the diffusion and dispersion cannot be separated; therefore equation above can be written as
 ∂C J = v¯ θC − Dsh (θ, v¯ ) (10) ∂x where Dsh is the diffusion–dispersion coefficient. The flux and concentration will vary according to temporal or spatial differences that can be written as ∂(Cθ) ∂J =− ∂t ∂x

(11)

Combining Eqs. (10) and (11), Eq. (12) can be derived as
 ∂(Cθ) ∂(¯vθC) ∂ ∂C =− + Dsh (12) ∂t ∂x ∂x ∂x Normally, θ, v¯ , and Dsh can be considered as constants in a steady flow. ∂C Dsh ∂2 C ∂C = −¯v + ∂t ∂x θ ∂x2

(13)

5. Adsorption Eq. (13) indicates the movement of solute is related to convection, diffusion, and dispersion processes; the solute have no reaction with soil particles. If part of solute can be adsorbed by soil, it can be expressed as Dsh ∂2 C ∂C ρ ∂S ∂C = − v¯ − 2 ∂t θ ∂x ∂x θ ∂t

(14)

where S is the adsorbed solute (M/L3 ) and ρ the bulk density (M/L3 ). This process can be illustrated by the Freundlich isotherm adsorption equation: S = kCN

(15)

where k and N are Freundlich constants. A further differential equation can be obtained as ∂C ∂S = kNCN−1 ∂t ∂t

(16)

Substituting (16) into (14), Eq. (17) can be derived as 
Dsh ∂2 C ρkNCN−1 ∂C ∂C = 1+ − v¯ (17) θ ∂t θ ∂x2 ∂x 1+

ρkNCN−1 = Rf θ

(18)

where Rf is the retardation factor for Freundlich adsorption. Substituting Eq. (18) into (17), Eq. (19) can be given as ∂C Dsh ∂2 C v¯ ∂C = − ∂t θRf ∂x2 Rf ∂x

(19)

6. Index model establishment The index method employs attenuation and retardation as two indicators to simulate chemical transportation in soil. The assumed conditions are (1) in a soil column with uniform properties, (2) chemical concentration varied only by differences of soil depth, and (3) constant soil moisture, so the diffusion–dispersion function can be neglected. Eq. (19) can be written as λC = −

v¯ ∂C Rf ∂x

(20)

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where λ is the coefficient for the first-order degradation kinetics (1/T): ∂C λRf C =− v¯ ∂x After integral of Eq. (21),
 λRf C = exp − H C0 v¯

(21)

(22)

where C0 is the concentration in the surface soil, C the concentration reached the soil depth H, H the depth (L), λ the λ = ln 2/t1/2 , and t1/2 the half-time. 2.2.3. Geographical information manipulation Using by resolution 40 m × 40 m digital elevation model from the Center for Space and Remote Sensing Research, National Central University, aerial photos from the Agriculture and Forest Aviation Measurement Institution, terrain module in WinGrid system, and ArcView GIS software, after the regional topographic data processes, digital figure files of elevation (Fig. 2), slope (Fig. 3), aspect (Fig. 4) were created. Soil nutrient concentrations from nearby forestland as background values, nutrient attenuation curves, and index model were employed to estimate the required selfpurified depth, safety depth, in representative plots of different land-use patterns. Combining stream system, slope direction, and elevation data, using stream central line as datum, stream bank and channel bed difference

derived from the developed program in this study, if the difference value is less than flood elevation plus soil safety depth, it will need placement of buffer strips and prohibition of utilization. Fig. 5 shows the schematic diagram of riparian vegetated buffer strips concepts.

Fig. 2. Elevation distribution in the Chi Chia Wang Stream watershed.

Fig. 4. Aspect distribution in the Chi Chia Wang Stream watershed.

Fig. 3. Slope distribution in the Chi Chia Wang Stream watershed.

2.3. System architecture Successful watershed modeling for nonpoint source pollution control depends upon how the large input data volume is managed and manipulated. The function used

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Fig. 5. Schematic diagram of the riparian vegetative buffer strip placement.

to summarize and display the model results in a variety of forms and presentation styles that requires highly flexible data management. We developed the WinGrid (Lin and Lin, 2000) analysis software to generate and organize the input parameters required by the riparian buffer strip calculations. In the WinGrid system, the basic data storage unit can be represented as a single layer in a map that contains information about the location features. The spatial information consists of digital elevation data and derived safety depth. Elevation, slope, and aspect distributions were obtained from digital elevation model in the Terrain Module. Safety depth for specific nonpoint pollutant was derived from the attenuation curves and index model in the Riparian Buffer Strip Module (Fig. 6).

3. Results The saturated hydraulic conductivity of soil profile in Wu Ling Farm vegetable area shows a logarithmic decrease from soil surface to subsurface (Fig. 7). The saturated hydraulic conductivity of surface soil layer is extremely high (>4000 mm/h). The direction that the nutrients move is closely related to the moisture transportation, especially for soluble nutri-

ent salts. The groundwater pollution control for the Chi Chia Wang Stream from the Wu Ling Farm agricultural development emphasizes soluble nutrient salt control. Table 1 shows the high nutrient concentrations in the surface soil from the large quantity of fertilizers applied in the vegetable area. Except for magnesium, the other nutrients in the orchard show lower values because of the less frequent fertilizer application. The estimated safety soil depths for the analyzed nutrients are listed in Tables 2 and 3. Among the analyzed nutrients, potassium reached the highest safety soil depth at 8.81 m in the vegetable area. Calcium contained the highest safety soil depth at 6.26 m in the orchard area. Safety soil depth can be predicted by using the nutrient attenuation curves and calculated using the index model. Each salt’s λRf in the soil profile and its mean value (Table 4) can be estimated in the vegetation area from Table 1. Using each salt’s λRf mean value, the nutrient concentration (C) in a 60–70 cm from forest soil depth, nutrient concentration (C0 ) from the top soil, and the average hydraulic conductivity of each soil layer, the safety soil depth (Table 5) for each nutrient can be obtained. Comparing the attenuation curves and index model results, a deeper safety depth was obtained

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Fig. 6. Illustration of the WinGrid (Lin and Lin, 2000) system for processing riparian buffer strip modeling.

Fig. 7. Saturated hydraulic conductivity of soils sampled from the vegetable plot.

from the attenuation curve calculations. To ensure the pollution control effects, the suggested safety depth would be obtained from the attenuation curve simulations. Another study focused on 46 kinds of selected pesticides in the same area (Lin et al., 2002). The safety depth obtained from the index model was established successfully. Fig. 8 shows the Chi Chia Wang Stream riparian vegetated buffer strips placement in the vegetable area estimated by using the safety depth from the potassium attenuation curve in vegetable area with topographic analysis. When overlapping the vegetated buffer strip placement and aerial photos (Fig. 9), the darker area in the aerial photo is forestland. Fig. 9 shows that the width of vegetated buffer strip is not wide enough due to agricultural activity (lighter area) along the streamside. In areas with inadequate vegetated buffer strips, on-site

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Table 1 Nutrient distribution in the soil profile at the sampling-sties Sampling depth (cm)

pH

Vegetable

0–5 5–10 10–15 15–30 30–45 45–60 60–75 75–100

Orchard

Forestry

EC (mmho/cm)

Available P (ppm)

Exchangeable K (ppm)

Na (ppm)

Ca (ppm)

Mg (ppm)

Fe (ppm)

Mn (ppm)

Zn (ppm)

Cu (ppm)

Cl− (ppm)

NO2 − (ppm)

NO3 − (ppm)

SO4 2− (ppm)

7.20 7.39 7.43 7.57 6.67 6.78 6.41 5.99

22.38 13.86 10.97 4.92 2.47 0.99 1.43 0.85

991 510 420 411 435 169 44 33

5743 1048 949 739 872 783 717 552

14969 5705 4963 7928 2016 2115 1793 1670

8414 8414 8414 8414 2982 2194 1555 954

254 257 250 266 103 56 32 26

41 21 19 23 83 73 35 39

118 110 92 67 45 37 28 23

46 41 38 41 28 13 4 7

2.5 2.5 2.2 3.0 4.0 4.0 3.3 2.7

1179 1086 612 183 95 62 82 17

708 621 286 T T T T T

301 1374 2248 1550 584 275 373 130

4480 529 504 174 105 39 95 74

0–5 5–15 15–30 30–60 60–70

6.48 6.61 6.49 6.56 6.56

0.37 0.15 0.13 0.11 0.10

54 80 55 47 25

228 102 141 75 60

505 613 592 505 122

3145 1057 982 808 584

641 260 227 160 171

14 22 17 17 12

71 31 24 28 12

13 6 3 5 2

2.1 1.1 0.9 1.5 0.7

27 25 25 22 18

T T T T T

59 16 14 T T

23 23 13 18 12

0–5 5–15 15–30 30–60 60–70

6.10 5.51 5.07 4.96 4.79

0.17 0.15 0.11 0.17 0.21

8 6 9 10 7

186 149 141 142 156

733 483 429 603 537

158 163 170 165 198

243 224 243 252 281

15 13 15 15 16

17 13 17 14 19

3 3 2 2 3

1.1 1.1 1.5 1.1 1.3

32 24 46 41 34

T T T T T

T T T T T

T: trace amount < 0.01 ppm.

Extractable

Soluble

23 T 15 11 11

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Table 2 Safety depth for each nutrient in the vegetation area Item

Nutrient attenuation equations in soil layers of vegetable area

r

Concentration (ppm) of soil depth 60–70 cm from forest land

Safety depth (m) in vegetable area

EC

[EC] = 26.778 − 6.294 ln D

−0.981**

0.21 (mmho/cm)

0.68

Available

P

ln[P] = 6.835 − 0.038D

−0.951***

7.00

1.29

Exchangeable

K Na Ca Mg

ln[K] = 8.508 − 0.510 ln D ln[Na] = 10.143 − 0.613 ln D ln[Ca] = 9.288 − 0.028D ln[Mg] = 5.841 − 0.032D

−0.857** −0.918** −0.977*** −0.972***

156.00 537.00 198.00 281.00

8.81 5.40 6.26 1.43

Extractable

Fe Mn Zn Cu

ln[Fe] = 3.331 + 0.007D ln[Mn] = 4.743 − 0.020D [Zn] = 46.331 − 0.526D ln[Cu] = 0.747 + 0.1091 ln D

0.407 −0.983*** −0.956*** 0.597

15.80 18.80 2.80 1.33

–a 0.90 0.83 –a

Soluble

Cl NO2 NO3 SO4

ln[Cl] = 6.896 − 0.047D [NO2 ] = 914.828 − 299.419 ln D ln[NO3 ] = 7.177 − 0.023D ln[SO4 ] = 9.079 − 1.183 ln D

−0.949*** −0.925** −0.705** −0.954***

33.70 0.01 0.01 11.00

0.72 0.21 6.12 2.84

a ∗∗ ∗∗∗

Related coefficients too low to estimate. 1% significance level. 0.1% significance level.

0k + 300, 0k + 400, and 0k + 450 in this 600 m creek segment (Fig. 10). Vegetated buffer strips need to be placed between the A and B sections of the Chi Chia Wang Stream as illustrated in Fig. 11.

samples were collected and analyzed for every 50 m along river course from the forestland (S) to the front of the buffer strips (E). The obtained results are listed in Table 6. Pollution sites can be observed at 0k + 200, Table 3 Safety depth for each nutrient in the orchard Item

Nutrient attenuation equations in soil layers of vegetable area

r

Concentration (ppm) of soil depth 60–70 cm from forest land

Safety depth (m) in vegetable area

EC

ln[EC] = −0.769 − 0.393 ln D

−0.963**

0.21 (mmho/cm)

0.07

Available

P

ln[P] = 4.602 − 0.015D

−0.838

7.00

1.77

Exchangeable

K Na Ca Mg

[K] = 255.975 − 47.345 ln D [Na] = 644.745 − 6.334 ln D ln[Ca] = 8.347 − 0.475 ln D ln[Mg] = 6.732 − 0.421 ln D

−0.903* −0.765 −0.959** −0.969**

156.00 537.00 198.00 281.00

0.02 0.17 6.26 0.13

Extractable

Fe Mn Zn Cu

ln[Fe] = 2.910 − 0.005D [Mn] = 78.850 − 16.043 ln D [Zn] = 14.177 − 2.943 ln D [Cu] = 2.199 − 0.319 ln D

−0.501 −0.922* −0.922* −0.738

15.80 18.80 2.80 1.33

0.30 0.42 0.48 0.15

Soluble

Cl NO3 SO4

[Cl] = 27.296 − 0.142D ln[NO3 ] = 3.918 − 00072D [SO4 ] = 26.594 − 3.096 ln D

−0.964** −0.960** −0.773

33.70 0.01 11.00

0.00 0.54 1.54

∗ ∗∗

5% significance level. 1% significance level.

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Fig. 8. Riparian vegetated buffer strip (RVBS) layout along the Chi Chia Wang Stream.

Fig. 9. RVBS overlap placement with aerial photo (pixel size: 40 m × 40 m).

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Table 4 Variation of λRf for each nutrient in soil depth Soil depth (cm)

P

K

Na

Ca

Mg

Fe

Mn

Zn

Cu

Cl

NO2

NO3

SO4

2.5–7.5 7.5–12.5 12.5–22.5 22.5–37.5 37.5–52.5 52.5–67.5 67.5–87.5

49.99 12.57 0.523 −0.69 7.3 6.629 0.594

128.9 6.44 6.04 −2 0.83 0.434 0.54

73.07 9.05 −11.3 16.59 −0.37 0.814 0.147

0 0 0 12.57 2.37 1.696 1

−0.89 1.79 −1.49 11.5 4.71 2.76 0.429

50.68 6.49 −4.61 −15.5 0.992 3.62 −0.22

5.318 11.60 7.65 4.824 1.512 1.37 0.406

8.717 4.934 −7.49 −3.48 0 0.948 0.415

0 8.3 −7.49 −3.48 0 0.948 0.415

6.224 37.24 29.16 7.94 3.29 −1.37 3.25

9.87 50.22 247.7 0 0 0 0

−115 −32 8.98 11.83 5.82 −1.5 2.17

162 3.14 25.68 6.12 7.65 −4.38 0.516

Mean value

6.24

8.67

3.17

3.32

3.35

−0.74

−0.74

8.49

32.67

−4.23

6.54

0.84

14.51

Fig. 10. Area marked by the circle shows insufficient RVBS placement width (pixel size: 40 m × 40 m). Table 5 Safety depth derived from the attenuation curves and index model Item

Safety depth (m) Attenuation curve

Index model

Available

P

1.29

1.15

Exchangeable

K Na Ca Mg

8.81 5.40 6.26 1.43

0.60 0.74 1.71 0.00

Extractable

Fe Mn Zn Cu

– 0.90 0.83 –

1.62 0.79 1.24 0.00

Soluble

Cl NO2 NO3 SO4

0.72 0.21 6.12 2.84

0.61 0.54 0.00 0.60

Fig. 11. Required RVBS width placement between the A and B sections of the Chi Chia Wang Stream.

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Table 6 Chemical properties of the soil sampled from a gully with an insufficient RVBS placement width Sample

S(0k + 000) 0k + 050 0k + 100 0k + 150 0k + 200 0k + 250 0k + 300 0k + 350 0k + 400 0k + 450 0k + 500 0k + 550 E(0k + 600)

EC (s/cm)

100 100 86 105 373 155 385 189 290 317 183 99 118

pH

7.97 8.02 8.22 7.83 8.03 7.54 7.84 7.88 7.86 7.97 7.90 8.05 7.62

Available P (ppm)

11.63 9.29 15.25 11.30 171.30 10.43 29.34 14.30 232.70 225.40 13.89 10.68 7.25

Exchangeable

Extractable

K (ppm)

Na (ppm)

Ca (ppm)

Mg (ppm)

Fe (ppm)

Mn (ppm)

Zn (ppm)

Cu (ppm)

115 68 34 78 269 87 420 88 238 292 107 46 73

191 102 57 126 420 130 540 133 420 460 167 74 106

327 357 615 503 1205 352 923 744 1128 2100 641 452 515

83 83 108 97 115 71 226 138 163 132 90 83 84

113 93 75 82 109 88 98 105 157 61 216 98 111

34 37 44 42 76 29 81 58 118 149 57 36 48

3.54 3.46 3.64 4.85 13.71 4.14 9.43 5.71 13.71 34.29 4.66 4.44 4.93

1.54 1.32 1.54 1.43 3.08 1.21 2.31 1.65 5.60 3.08 2.53 1.43 1.65

4. Discussion and conclusions Sediment, nutrients, pesticides, and other nonpoint source pollutants from slope land agricultural activities are the major reasons for the worsening water quality. Vegetated buffer strips provide several functions such as pollutant buffering, stream bank stabilization, and conservation. Buffer strips can effectively prevent nonpoint pollution from slope land agriculture. The major soil pattern in Taiwan mountain areas is sandy or rocky soil mixed with shale or gravels exhibiting a high percentage of coarse pores. Agricultural nonpoint source pollution control and pollutant removal from infiltrated water from areas with high infiltration capacity occurs primarily through self-purification, i.e. soil adsorption, biological fixation, biodegradation, and chemical reactions. Maintaining proper soil depth in high infiltration areas is important to ensure soil self-purification and avoid groundwater pollution. Comparing the attenuation curves and index model analyses, a deeper safety depth was obtained from the attenuation curve calculations. To ensure the pollution control effects, the suggested safety depth is better obtained from the attenuation curve simulations. Among the analyzed nutrients, potassium exhibited the highest safety soil depth value of 8.81 m in the vegetable area from nutrient attenuation curve analyses. Because potassium exhibits the fastest mobility among the analyzed nutrients, calculating the Chi Chia Wang Stream riparian vegetated strip width using safety depth ob-

tained from the potassium attenuation rate is the best approach. The placement and width of riparian buffer strips designed for the potassium safety depth can effectively prevent other nutrient pollution. The obtained riparian vegetated buffer strip widths and placement can be provided to the Shei Pa National Park Headquarters as projects for best management practices. In addition to vegetated buffer strip placement, we should also provide grass ditches and agricultural detention tanks in the potential polluted agriculture areas in the watershed. Retaining sediment, nutrients, and other nonpoint pollutants in agricultural lands is in the spirit of sustainable agriculture. Prevention at the source is more important than protection downstream. This design concept should be utilized in planning vegetated buffer strip placement.

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