a g r i c u l t u r a l w a t e r m a n a g e m e n t 9 5 ( 2 0 0 8 ) 1 1 8 9 – 1 2 0 0

Applicability of targeting vegetative filter strips to abate fecalbacteria and sediment yield using SWAT

P.B. Parajuli a,*, K.R. Mankin b, P.L. Barnes b

aDepartment of Agronomy, Kansas State University, Manhattan, KS 66506, United StatesbDepartment of Biological and Agricultural Engineering, Kansas State University, Manhattan, KS 66506, United States

a r t i c l e i n f o

Article history:

Received 15 December 2007

Accepted 12 May 2008

Published on line 2 July 2008

Keywords:

Non-point source pollution

Best management practice

Targeted approach

Random approach

Watershed

a b s t r a c t

Quantifying and evaluating effects of best management practices (BMPs) on water quality is

necessary to maximize the effectiveness of BMPs for minimizing pollutants. Watershed-

scale evaluation of effects of BMP implementation on fecal bacteria and sediment yield can

be estimated using a watershed water quality model, and strategies for identifying critical

areas in a watershed can be pollutant specific. The soil and water assessment tool (SWAT)

model was used in the Upper Wakarusa watershed (950 km2) in northeast Kansas to explore

effectiveness of vegetative filter strip (VFS) lengths applied at the edge of fields to reduce

non-point source pollution. The Upper Wakarusa watershed is a high priority total max-

imum daily load (TMDL) designation watershed for fecal bacteria in Kansas. This study

characterizes fecal bacteria sources (human, livestock, and wildlife) and targets VFS to abate

sediment and fecal bacteria pollution from the Upper Wakarusa watershed.

Objectives of this study were to test the effectiveness of VFS lengths (0, 10, 15 and 20 m)

for removing overland process sediment and fecal bacteria concentration, rank sub-water-

sheds after determining sediment and fecal bacteria contribution of each sub-watershed,

and demonstrate the SWAT model’s ability to evaluate effectiveness of VFS application to

abate sediment and fecal bacteria using targeted and random approaches to select 10, 25 and

50% of the sub-watersheds. The 15-m VFS reasonably reduced fecal bacteria concentration

in the watershed. The greatest difference between the target and random approaches to

fecal bacteria reduction was at 50% VFS adoption; the target approach removed about 60% of

fecal bacteria, and the random approach removed about 42%. For sediment yield, the

greatest reduction was at 25% VFS adoption; the target approach removed about 63% of

sediment yield, and the random approach removed about 33%. A targeted watershed

modeling approach using SWAT was effective at reducing both fecal bacteria concentration

and sediment yield.

# 2008 Elsevier B.V. All rights reserved.

avai lab le at www.sc iencedi rec t .com

journal homepage: www.e lsev ier .com/ locate /agwat

1. Introduction

Among the five leading pollutants in U.S. water bodies,

mercury is ranked first followed by pathogens and sediment

(USEPA, 2006). In Kansas, pathogens are ranked third (15.44%)

* Corresponding author at: 2011D Throckmorton Plant Sciences CenteTel.: +1 785 532 2766; fax: +1 785 532 6094.

E-mail address: parajpb@ksu.edu (P.B. Parajuli).

0378-3774/$ – see front matter # 2008 Elsevier B.V. All rights reservedoi:10.1016/j.agwat.2008.05.006

among causes of water quality impairment (USEPA, 2006).

Non-point source (NPS) pollution from agricultural lands

contributes to water quality degradation. Developing total

maximum daily loads (TMDLs) require quantifying pollutant

contribution from each source and determining pollutant

r, Kansas State University, Manhattan, KS 66506, United States.

d.

a g r i c u l t u r a l w a t e r m a n a g e m e n t 9 5 ( 2 0 0 8 ) 1 1 8 9 – 1 2 0 01190

reduction from each source required to meet applicable water

quality standards. Implementing best management practices

(BMPs) can sufficiently eliminate or reduce NPS pollution to

meet water quality criteria without disturbing environmental

quality (Novotny and Olem, 1994). Vegetative filter strip (VFS)

is a popular BMP for removing sediment and fecal bacteria and

decreasing pollutant loads from manured fields and pastures

(Guber et al., 2007; Inamdar et al., 2001; Park et al., 1994; SWCS,

2001; Vennix and Northcott, 2002).

Targeting, a term for implementing stricter pollution

control in areas where it will be most cost-effective (Veith

et al., 2001) often reduces costs compared with first-come,

first-served approaches. Several studies have developed

targeting procedures that enable watershed-specific evalua-

tion of NPS pollution control, and targeting methods incor-

porating a spatially distributed hydrologic/water quality

models have been demonstrated. The BMPs effectiveness

have been researched using many available modeling tools

(Moore et al., 1992; Niu et al., 2001; Zreig, 2001; Veith et al., 2001;

Vennix and Northcott, 2002; Bracmort et al., 2004), which can

be inexpensive and less time consuming as compared to

watershed scale field evaluation (Benham et al., 2006; Corwin

and Vaughan, 1997; Dickinson et al., 1990; Heatwole et al.,

1987; Tim et al., 1992). The location of the BMP targeting can be

varied due to pollutant variation. For example, targeting for

sediment and fecal bacteria reduction may or may not be in

the same hydrologic response units (HRUs) or sub-watersheds.

Various studies, conducted in the past, determined that the

VFSeffectiveness is influenced byfactors including lengthofthe

VFS, type of vegetation, slope of strip, sediment size distribution

in the runoff, and flow concentration. Many studies consider

strip length the most important parameter affecting sediment

removal efficiency (Munoz-Carpena et al., 1999; Helmers et al.,

2006). Some studies concluded that increasing flow length

beyond 10 m does not increase VFS efficiency by large margins

(Lee et al., 2003; Zreig et al., 2004). However, Gharabhagi et al.

(2001) indicated that smaller sediments take longer to separate

out and require a longer filter. They concluded that the first 5 m

of VFS play a significant role in removing suspended solids and

aggregates larger than 40 mm. Fecal coliform bacteria (FCB) are

facultative, anaerobic, gram negative, non-spore forming, rod-

shaped bacteria smaller than 0.45 mm (Wang, 2003).

Various modeling tools have been developed to evaluate

efficiency of VFS to reduce pathogens during the 1980s (Moore

et al., 1988; Overcash et al., 1983; Springer et al., 1983; Walker

et al., 1990). Still, a wide range of opinions exists on VFS

efficiency in regard to pathogens and/or indicator organisms

(Pachepsky et al., 2006). Targeting and prioritizing the areas for

implementation of BMPs rather than random or general aerial

application is the key to the cost-effective water quality

improvement (Horan and Ribaudo, 1999; Kerr et al., 2007).

Recent watershed water quality physically based and

spatially distributed models can account for physical and

spatial processes. The soil and water assessment tool (SWAT)

water quality model has been applied, calibrated, and

validated for one or more pollutant parameters such as

runoff, sediment yield, and nutrient losses from watersheds at

different geographic locations, conditions, and management

practices (Saleh et al., 1999; Spruill et al., 2000; Santhi et al.,

2001; Kirsch et al., 2002; Van Liew et al., 2003; White et al., 2004;

Qi and Grunwald, 2005; White and Chaubey, 2005; Wang et al.,

2006; Jha et al., 2007; Gassman et al., 2007). Parajuli et al. (2007)

reasonably calibrated and verified the SWAT model using 3

years (2004–2006) of measured daily flow and sediment data.

The model, verified at the Upper Wakarusa watershed in

Kansas, reasonably predicted total fecal bacteria concentra-

tion (coefficient of determination from 0.37 to 0.52 and Nash

Sutcliffe efficiency index from 0.24 to 0.38). Tripathi et al.

(2003) used SWAT to identify and prioritize critical areas on the

basis of average annual sediment yield and nutrient losses.

Mankin et al. (2005) and Tuppad and Mankin (2005) found that

the targeted watershed modeling approach using the SWAT

model was effective at reducing sediment loads from the

Kanopolis watershed in Kansas. However, these studies did

not consider targeting pathogens from agricultural water-

sheds. The SWAT model has not been used for targeting to

reduce fecal bacteria concentrations in agricultural water-

sheds. The SWAT model placed VFS along the edges of HRUs.

This study used the SWAT model to evaluate effectiveness

of different VFS lengths for targeting conservation programs to

reduce NPS pollution. Overall objectives were to characterize

fecal bacteria sources and target BMPs to abate sediment and

fecal bacteria pollution from the Upper Wakarusa watershed.

This study had three specific objectives: (i) test the effective-

ness of various filter strip lengths (0, 10, 15 and 20 m) for

removing overland process sediment and FCB concentration,

(ii) rank sub-watersheds after determining the sediment and

FCB contribution (base condition) of each sub-watershed by

overland process, and (iii) evaluate the effectiveness of VFS

application using both target and random approaches to select

10, 25 and 50% of the sub-watersheds.

2. Material and methods

2.1. Upper Wakarusa watershed

The Upper Wakarusa watershed (Fig. 1) is located in Douglas,

Shawnee, Osage and Wabaunsee counties in Kansas and

consists of 950 km2 with an average elevation of 304 m. The

watershed has three major land uses: grassland (57%),

cropland (28%), and woodland (9%). Silty-clay textured soils

are the predominant soil type in this watershed. The SWAT

model was verified at the Upper Wakarusa watershed.

2.2. Stream monitoring

Stream flow and bacteria data were collected at various water

quality sampling points of the watershed to validate model

results (Fig. 1). Grab samples (about 250 mL) were collected

from the midpoint of the flowing stream at each water quality

sampling point. Samples were placed immediately into an ice

chest and transferred to a laboratory refrigerator within 2–4 h

of collection. Bacteria enumeration procedures were started

within 24 h. The serial dilution method (Clesceri et al., 1998)

was applied to enumerate FCB colonies. Bacterial samples

typically required four serial dilutions to obtain reasonable

bacteria colony counts.

Flow was calculated at the time of sample collection using

Manning’s equation, as outlined by Ward and Elliot (1995).

Fig. 1 – Location map of the Upper Wakarusa watershed in northeast Kansas.

a g r i c u l t u r a l w a t e r m a n a g e m e n t 9 5 ( 2 0 0 8 ) 1 1 8 9 – 1 2 0 0 1191

Flow depth, cross-sectional area, and channel slope were

measured, and the channel roughness factor was estimated

based on channel roughness characteristics, and degree of

meandering (Cowan, 1956). The calculated flow was validated

based on ratio of the watershed area using data from the

United States Geological Survey (USGS) Richland gage station.

Calculated flow data showed very good correlation (>90%)

with weighted area flow data. This study used data collected

from January 2004 to April 2006.

2.3. Fecal bacterial source characterization

2.3.1. LivestockManure from grazing areas, feeding operations, and winter

feeding areas were major bacterial sources in this study.

Livestock population at the county and watershed level was

estimated using agricultural census/GIS layers data (USDA,

2006). The county animal census population was equally

distributed on a total land-area basis to determine the fraction

of total livestock in the study watershed. The USDA data were

compared with data from the Kansas Department of Agri-

culture (KDA, 2004a). The number of animal units (AUs) in

feedlots within the watershed were estimated using active

feedlot data (both federally permitted feedlots >1000 AUs and

state registered feedlots >300 AUs) from the Kansas Depart-

ment of Health and Environment (KDHE) (M. Jepson, personal

communication, 2005). Permitted and registered livestock

were subtracted from the total number of AUs in the

watershed to estimate the net grazed livestock population.

The field reported stocking rate of 3 ha per cow and calf pair

(KDA, 2004b) was used as the baseline value, but this value

could vary due to pasture management activities, animal

growth, and animal sales. Animal stocking rates in the

pastureland also were validated using county-wide livestock

population data (KDA, 2004a). Animals in pasturelands could

be brought from feedlots, barnyards, and leasing agreements

for grazing during the warm season (generally April to

September). However, stocking rate of the animals in pasture-

land was assumed to be constant throughout the grazing

season and decrease during the winter.

The Upper Wakarusa watershed was estimated to be

populated with 27,122 beef AUs in the pastureland (based on

stocking rate), 2982 beef AUs in the feedlots, and 627 swine

AUs and 10,849 beef AUs in the winter feeding areas (40% of

27,122), which was used in this study to represent the current

scenario of the watershed. Manure production by beef cattle

was estimated based on standard production rates (ASAE,

2000) of 26.4 kg of wet manure per day per 1000-kg AU. Actual

manure production by each AU can vary depending on dietary

habit of the animal, reflected in a reported standard deviation

of 17 kg per day for manure estimation (ASAE, 2000). FCB

concentration in manure was estimated based on ASAE (2000),

which reported a concentration of 13 � 1010 colony forming

units (cfu) per day per AU (wet-weight-basis) from the beef

manure with a standard deviation of 12 � 1010 cfu per day per

AU. Bacteria concentration was converted into model-input

units of cfu per gram of dry-weight manure using a standard

mean manure moisture content of 86% (ASAE, 2000).

Pastureland was simulated under two major grass-type

management conditions, which represent typical field condi-

tions: grazed (80%, typically native prairie) and non-grazed

(20%, typically smooth brome and tall fescue). Native prairie

grass typically is not fertilized, but tall fescue is fertilized with

70–15–0 (NPK) (W. Boyer, personal communication, 2006). It was

a g r i c u l t u r a l w a t e r m a n a g e m e n t 9 5 ( 2 0 0 8 ) 1 1 8 9 – 1 2 0 01192

estimated that about 1.81 kg per ha per day dry weight of

manure is applied in the pasturelands due to grazing operation

during the growing season and cattle are grazed for 153 days in

the pastureland (W. Boyer, personal communication, 2006). It is

possible that actual animal density varies in the watershed

every day due to animalgrowth and the pattern of incoming and

outgoing animals. It was estimated that about 20% of the air-dry

biomass is trampledevery day and about 341 kgof air-dry forage

is required for an AU for 30 days (Paul and Watson, 1994). In this

watershed, grazing starts about a month earlier in tall fescue

grasslands than in native prairie grass (W. Boyer, personal

communication, 2006). The entire native prairie is grazed, but

only 80% of the tall fescue is grazed; the remaining tall fescue is

used for haying or the Conservation Reserve Program (CRP).

About 3.7 Mg per ha of hay is harvested annually from the un-

grazed area, but biomass is not removed from the CRP land (W.

Boyer, personal communication, 2006). Because cattle do not

graze pastureland from October to March, no biomass uptake

from the pastureland occurred, with no grass trampling and no

manure deposition on the soil during this period.

All source loads due to livestock in confined animal feedlots

were modeled to be land-applied as grazing operations in

pasturelands of the sub-watershed where the active permitted

feedlots were located. Winter feeding areas were modeled

assuming that all livestock were confined within 40% of the

grazed land of the watershed based on observed animal

congregating behavior (W. Boyer, personal communication,

2006). Animals in feedlots and winter-feeding areas contrib-

uted fecal bacteria for 212 days during the dormant season of

the year (generally October to March). It was estimated that

about 4.52 kg per ha per day dry weight of cattle manure (2.5

times greater than regular pastureland operation) was applied

in the respective pasturelands of the sub-watersheds due to

winter-feeding operations.

2.3.2. HumanDigital orthophoto quarter quadrangles of the watershed from

2002 were digitized depending on the physical context, roads,

and type of houses to represent each septic system in the

watershed. Each rural house was assumed to have one septic

system, resulting in a total of 2137 septic systems in the Upper

Wakarusa watershed. About 40% of the estimated septic

systems (n = 855) were assumed failing in the watershed

(KDHE, 2000). Each septic system was assumed to be used by

three persons in the household that can contribute about

0.32 m3 of sewage effluent per household per day (USEPA,

2001). Failing septic systems in the watershed were modeled

using two techniques: effluent was either land-applied in the

non-grazing grassland areas or input as a direct-daily point

load to the outlet of the each sub-basin. The FCB concentration

in failing septic system effluent was taken as 6.3 � 106 cfu per

100 mL (Overcash and Davidson, 1980).

2.3.3. WildlifeNo comprehensive wildlife inventory was available for the

Upper Wakarusa watershed. Wildlife population density was

estimated based on information received from the Kansas

Department of Wildlife and Parks (KDWP). The 2002 summer

road-kill indices survey data (M. Peek, personal communica-

tion, 2005) for Kansas were used to estimate small mammal

populations in the watershed. Information included data for

various wild animal species: raccoon, opossum, striped skunk,

coyote, badger, bobcat, red fox, gray fox, swift fox, beaver,

mink, muskrat, river otter, spotted skunk, weasel, armadillo,

woodchuck, and porcupine. The population of raccoon,

opossum, striped skunk, and coyote constituted about 81%

of the total surveyed small mammals in Kansas (Timm et al.,

2007). Population of the predominate large mammal (white-

tailed deer) in the watershed was estimated based on expert

opinion from the KDWP big-game coordinator (L. Fox, personal

communication, 2006). Similar data were collected for the

predominate indigenous avian species (turkey) from the

KDWP small game coordinator (J. Pitman, personal commu-

nication, 2006), and for migratory birds (duck, geese, sandhill

crane) from the KDWP waterfowl biologist (M. Kraft, personal

communication, 2006).

To estimate the AUs of each wildlife species in the

watershed, population data were first distributed over the

potential habitat for each species. Small mammals and turkey

population data were counted from a road survey. Most of the

small mammals were counted dead at the road shoulder. Sight

distances of 5 m for small mammals and 50 m for turkey from

each side of the road were assumed, and the population density

of each species was estimated as number of animals per unit

area, using total length of the road driven during survey. For

deer, the number of deer harvested in northeastern Kansas was

estimated and equally distributed in the total land area of

northeastern Kansas to determine deer population density.

Indigenous turkey population was equally distributed in the

woodland, cropland, and pastureland areas of the watershed as

data was collected by rural mail carriers from those land use

areas (J. Pitman, personal communication, 2006). Migratory

birds are present in the watershed for about seven months of

the year (January–March and September–December). The

Kansas population of these wildlife species was equally

distributed over the water surface area and wetland area to

determine population density. Population density in the

watershedwasestimatedbased onavailablewatersurface area.

Animal weights were estimated based on information

received from ‘‘Mammals of Kansas’’ (Timm et al., 2007) and

personal communication (J. Pitman, personal communication,

2006). Population in 1000-kg AUs of about 456 migratory birds,

760 turkey AUs, 304 deer AUs, and 89 small mammal AUs was

used to represent the current scenario of the Upper Wakarusa

watershed. All wildlife-generated manure was applied to the

woodland, cropland, and pastureland areas depending on

wildlife habitats to be considered as the baseline scenario.

2.4. Crops and vegetative filter strips

Corn and soybeans were major warm-season crops, and

winter wheat was the primary cool-season crop grown in 3

year’s rotation in the watershed (W. Boyer, personal commu-

nication, 2006). The warm season crop was planted on May 1

and harvested on October 1. The cool-season crop was planted

on October 20 and harvested on July 30. These dates represent

typical planting and harvesting dates in the watershed. Crop

residue was left on the cropland between crop periods. The

conservation tillage system is the most widely adopted system

for corn, soybean, and wheat in the watershed. Croplands of

a g r i c u l t u r a l w a t e r m a n a g e m e n t 9 5 ( 2 0 0 8 ) 1 1 8 9 – 1 2 0 0 1193

the watershed were simulated with a 5-m wide filter strip at

the edge of HRUs, which is smaller than Natural Resources

Conservation Service (NRCS) guidelines but selected to provide

an average sediment removal rate of 59%, consistent with local

field buffer strip data (Ngandu, 2004).

This study used the previously calibrated and validated

SWAT model in the Upper Wakarusa watershed. The VFS was

considered one of the BMPs in this study. Various VFS lengths

(0, 5, 10 and 20 m) were evaluated for effectiveness at reducing

FCB transport through the overland flow process. After the

SWAT model was tested for various VFS lengths for the whole

watershed, the recommended VFS length (15 m) was applied

in selected (0, 10, 25, 50 and 100%) sub-watersheds with both

target and random methods.

2.5. SWAT model

The SWAT model uses geospatially referenced data to satisfy

the necessary input parameters. USGS (1999) 30 m � 30 m

elevation data was used to delineate the watershed bound-

aries and topography. The Soil Survey Geographic Database

was used to create a soil database (USDA, 2005). The Gap

Analysis Program (GAP) 2001 land cover data that depicts 20

general land cover classes for the state of Kansas (KARS, 2001)

was used. Wardlow and Egbert (2003) evaluated GAP and

National Land Cover Data (NLCD) land use data (USGS, 2000)

for the state of Kansas. Kansas GAP data provided better

discrimination of most land cover classes compared with

NLCD. Accuracy assessment found an overall accuracy of 87%

for GAP and 81% for NLCD, and GAP had higher accuracies for

most individual land cover classes. The Kansas GAP and NLCD

land cover products were comparable in terms of characteriz-

ing broad scale land cover patterns, but the Kansas GAP land

cover map appears to be more appropriate for localized

applications that require detailed and accurate land cover

information.

Land use classes were re-classified into eight classes

(grazedland, non-grazedland/hay, cropland, woodland, CRP,

water, urban areas, and quarry) based on field-verified land

use conditions (Mankin and Koelliker, 2001; Mankin et al.,

2003). The stream threshold area was defined as 950 ha, which

is about 10% of the total watershed area (950 km2). The SWAT

model delineated 53 sub-basins ranging in area from

0.003 km2 to 66.709 km2. Watershed parameters for each

HRU in each watershed were defined on the basis of soil,

land use, and topographic characteristics of the watershed as

described in the SWAT 2005 documentation (Neitsch et al.,

2005).

The microbial survival and transport sub-model was added

to the SWAT model in 2000 and modified in 2005. The SWAT

model microbial component considers the fate and transport

of organisms for bacterial concentration. The microbial sub-

model uses Chick’s Law, as revised by Moore et al. (1989), to

model fecal bacteria die-off and re-growth. Chick’s law, a first

order decay equation, determines the quantity of bacteria that

are removed or added by die-off and re-growth as described by

Sadeghi and Arnold (2002):

Ct ¼ C0 � e�K20t uðT�20Þ(1)

where Ct = bacteria concentration at time t, count/100 mL;

C0 = initial bacteria concentration, count/100 mL; K20 = first-

order die-off rate at 20 8C, day�1; t = exposure time, days;

u = temperature adjustment factor; T = temperature, 8C.

In-stream bacteria die-off is the only process modeled in

SWAT (Neitsch et al., 2005). SWAT calculates loading of

pathogens and indicator bacteria for pathogens from land

areas in the watershed. The VFS are generally provided at the

edge of the field and can be defined in an HRU. Sediment and

bacteria loads in surface runoff from overland flow process are

reduced as the surface runoff passes through the filter strip.

Filter strip trapping efficiency for fecal bacteria (Moore et al.,

1988) is calculated by

trapef;bact ¼11:8þ 4:3 widthfiltstrip

100(2)

where trapef,bact is the fraction of the bacteria loading trapped

by the filter strip, and widthfiltstrip is the width of the filter strip

(m).

Looking at Eq. (2), 20.5 m is the maximum VFS length that

can be used in the model to remove 100% of fecal bacteria.

Eq. (2) is recommended not to exceed more than 75% of

bacteria removal, and the equation is still being researched.

VFS trapping efficiency for sediment is calculated by

trapef ¼ 0:367 ðwidthfiltstripÞ0:2967 (3)

where trapef is the fraction of the constituent loading

trapped by the filter strip, and widthfiltstrip is the width of

the filter strip (m).

2.6. Weather and hydrologic data

Weather data such as daily precipitation and daily ambient

temperatures were extracted from the National Climatic

Data Center, and records maintained by the Kansas state

climatologist also were used. Daily climate data were used

from nine weather stations near the watershed: Clinton

Lake, Auburn, Silver Lake, Overbrook, Eskridge, Pomona

Lake, Lawrence, Lecompton, and Topeka (Fig. 1) but only five

weather stations had complete daily precipitation data. The

Silver Lake weather database was used for daily solar

radiation, daily wind speed, and daily relative humidity

data. Missing data were adjusted using SWAT database

simulation. For simulation, the SWAT model uses data from

the Ottawa weather station (Franklin County), which is

located about 24 km south-east from the nearest point of

the watershed. Average annual rainfall data for 2004 to

2006 was measured for Overbrook (939 mm), Lecompton

(955 mm), Topeka (1008 mm), Auburn (935 mm) and Eskridge

(942 mm), which had complete daily precipitation data, are

given in Table 1.

3. Results and discussion

The SWAT model-predicted daily flow and sediment reason-

ably matched measured values during previous calibration

and verification studies for the Upper Wakarusa watershed

(Parajuli et al., 2007).

Table 1 – Description of weather stations rainfall data used in the study

Station name Yeara Total(mm per year)

Peak rainfall(mm per day)

Growingseason (%)b

No. ofrainfall-eventsc

No. of potentialrunoff-eventsd

Overbrook 2004 1126 69 69 83 29

2005 1180 81 77 70 23

2006 512 53 72 44 12

Lecompton 2004 1206 83 69 102 28

2005 1029 87 72 81 23

2006 629 40 68 59 18

Topeka 2004 1013 62 67 91 25

2005 1239 142 74 82 24

2006 772 64 81 62 20

Auburn 2004 1146 114 70 93 27

2005 1013 82 71 76 19

2006 646 60 73 59 17

Eskridge 2004 960 67 73 85 25

2005 1185 125 75 87 28

2006 681 58 75 65 16

a Year 2006 rainfall data from January to October only.b % of rainfall between April to September.c Rainfall greater than 1 mm.d Rainfall events greater than 14 mm.

Fig. 2 – Watershed sub-basins and annual average overland flow fecal bacteria response.

Fig. 3 – Watershed sub-basins and annual average overland sediment yield response.

a g r i c u l t u r a l w a t e r m a n a g e m e n t 9 5 ( 2 0 0 8 ) 1 1 8 9 – 1 2 0 01194

Fig. 4 – Distribution of watershed sub-basins for fecal coliform bacteria BMP selection: target method (a) 10%, (b) 25%, (c) 50%

and random method (d) 10%, (e) 25%, and (f) 50%.

Fig. 5 – Distribution of watershed sub-basins for sediment BMP selection: target method (a) 10%, (b) 25%, (c) 50% and random

method (d) 10%, (e) 25%, and (f) 50%.

a g r i c u l t u r a l w a t e r m a n a g e m e n t 9 5 ( 2 0 0 8 ) 1 1 8 9 – 1 2 0 0 1195

Table 2 – Fecal bacteria and sediment yield ranking fortargeting

Rank Fecal bacteria Sediment yield

Sub-basin

Bacteria(cfu per 100 mL)

Sub-basin

Sediment(Mg per ha)

1 49 5460 48 13.22

2 44 5407 46 10.18

3 34 5163 30 8.42

4 21 5011 38 8.25

5 17 4954 32 7.83

6 48 4573 39 7.73

7 43 4532 35 7.44

8 50 4456 16 6.58

9 36 4397 36 6.47

10 46 4293 18 5.86

11 35 4074 31 3.11

12 45 3936 44 1.65

13 42 3693 34 1.53

14 32 3626 1 1.48

15 41 3523 42 1.42

16 40 3144 5 1.25

17 37 3008 23 1.19

18 47 2834 3 1.12

19 16 2724 22 1.07

20 24 2723 20 1.04

21 25 2629 4 1.03

22 39 2610 27 0.96

23 30 2580 2 0.88

24 33 2519 17 0.85

25 26 2387 29 0.84

26 1 2185 21 0.82

27 31 2059 19 0.79

28 38 1989 14 0.73

29 3 1872 47 0.72

30 4 1667 40 0.69

31 5 1599 52 0.66

32 20 1397 49 0.64

33 19 1361 26 0.64

34 22 1334 51 0.61

35 23 1202 33 0.61

36 2 1153 25 0.57

37 18 864 24 0.56

38 6 770 53 0.54

39 11 636 6 0.51

40 51 630 50 0.50

41 10 561 7 0.48

42 7 539 43 0.46

43 8 535 10 0.45

44 28 514 37 0.45

45 9 483 15 0.43

46 27 375 45 0.43

47 53 366 28 0.37

48 13 346 11 0.31

49 14 345 12 0.25

50 29 313 41 0.18

51 15 313 9 0.17

52 52 219 8 0.13

53 12 189 13 0.10

a g r i c u l t u r a l w a t e r m a n a g e m e n t 9 5 ( 2 0 0 8 ) 1 1 8 9 – 1 2 0 01196

3.1. Vegetative filter strip length

Fig. 2 shows that increasing the length of a VFS in model

simulation reduces average overland annual FCB concentra-

tion from the watershed. The first 10 m of VFS reduced about

57% of FCB. The additional 5 m of a 15-m VFS removed up to

80% of the FCB, which was reasonable. The 20-m VFS removed

about 100% of FCB, which was anticipated based on Eq. (2).

Fig. 3 shows a similar trend. About 73% of sediment yield was

reduced with a 10-m VFS. The 15-m VFS removed up to 82% of

sediment yield, and the 20-m VFS removed up to 89% of

sediment yield, which was anticipated based on Eq. (3). Figs. 2

and 3 showed more noticeable results using Eqs. (2) and (3) in

the model. A 20-m VFS removed 100% of the FCB whereas 100%

reduction of sediment needed 30 m of VFS.

Parajuli et al. (2005) tested different lengths of VFS (0–25 m)

using SWAT 2000 and suggested that 20-m VFS were most

effective at reducing FCB concentration in the agricultural

watershed. They did not find any further reduction in FCB

concentration using 25-m VFS. However, using Eq. (2), which

was changed slightly in SWAT 2005, was recommended for up

to 75% bacteria removal efficiency. The study of Moore et al.

(1988) also recommends 15-m VFS.

For the target method, sub-basins were selected separately

for FCB concentration and sediment contribution by overland

flow based on ranking (Figs. 4 and 5). Sub-watersheds were

ranked separately for sediment and FCB concentration

prediction because model-identified top ranking sub-water-

sheds for sediment and fecal bacteria were different (Table 2),

which was anticipated. The SWAT model uses the modified

universal soil loss equation (Williams, 1995) to estimate

sediment yield. The slope and slope length parameters in

this equation can directly affect sediment yield estimates.

Generally, average slope of each HRU/sub-watershed influ-

enced sediment prediction, and total water yield from each

HRU/sub-basin influenced FCB concentration prediction.

3.2. Fecal bacteria and sediment yields

3.2.1. Overland flow outputRemoval of annual average FCB concentration and sediment

yields by overland flow process was affected due to VFS

adoption. When VFS were applied in 100% of sub-basins in the

watershed, reduction in annual average FCB concentration

was about 79%, from 2134 to 438 cfu per 100 mL. Reduction in

FCB concentration due to 10% VFS adoption was 20% for the

target approach and about 12% for the random approach.

The greatest difference between the target and random

approach to FCB concentration reduction was at 50% VFS

adoption; the target approach removed about 60% of FCB

concentration, and the random approach removed about

42% (Fig. 6). In this watershed, greater amounts of FCB

came from grazedlands than from non-grazedlands or

cropland. Grazedlands were fairly equally distributed in

the watershed. Because many sub-watersheds were ranked

(Table 2) closely with regard to FCB concentration, the

greatest reduction occurred only when 50% of the sub-

watersheds adopted VFS. Annual average sediment yield

removal by overland flow process also was affected by VFS

adoption. When VFS were applied in 100% of sub-basins in

the watershed, reduction in annual average sediment yield

was about 82%, from 2.17 to 0.39 Mg per ha.

Reduction in sediment yield due to 10% VFS adoption was

46% for the target approach and about 28% for the random

approach. The greatest difference between the target and

random approach to sediment yield reduction was at 25% VFS

Fig. 6 – Watershed sub-basins and annual average

overland flow fecal bacteria response.

Fig. 7 – Watershed sub-basins and annual average

overland flow sediment yield response.

Fig. 8 – Watershed sub-basins and annual average

overland and watershed outlet fecal bacteria response.

Fig. 9 – Watershed sub-basins and annual average

overland flow and watershed outlet sediment yield

response.

a g r i c u l t u r a l w a t e r m a n a g e m e n t 9 5 ( 2 0 0 8 ) 1 1 8 9 – 1 2 0 0 1197

adoption; the target approach removed about 63% of the

sediment yield, and the random approach removed about 33%

(Fig. 7). Sediment yield was mostly dependent on slope of the

HRU/sub-basin, and percentage of cropland area in the sub-

basins. Generally, longer VFS can reduce more sediment and

FCB than shorter VFS.

3.2.2. Watershed outlet flow outputApplying VFS to the edge of HRUs did not yield measurable

differences in annual average FCB concentration at the outlet

of the watershed (Richland outlet). Applying VFS had good

results only in the overland flow process. Because estimated

direct point loads from livestock (8.5%), human (10%), and

wildlife (10%) sources were input in the model, direct point

loads dominated total FCB prediction at the outlet of the

watershed. Fecal bacteria source loads applied on the land

were determined to have no or less sensitivity in a previous

study (Parajuli et al., 2007). Direct point loads were more

sensitive to channel flow process in the model. Interestingly,

contribution of overland flow process FCB concentration at 0%

VFS adoption (log transformed value = 3.3) was less than 1% of

total FCB concentration (log transformed value = 5.79) pre-

dicted at the outlet of the watershed (Richland) where direct

point source loads were input (Fig. 8). The NPS loads of FCB had

chances of reduction through die-off and sorption during the

overland flow process, but point loads had chances of

reduction through only the channel flow process.

Annual average sediment yield removal at the outlet of the

watershed (compared with the Richland outlet) was about

36% when VFS were applied in 100% of the sub-basins.

Reduction in sediment yield due to 10% VFS adoption was 12%

for the target approach and about 2% for the random

approach. The greatest difference between the target and

random approach to sediment yield reduction was at 50% VFS

adoption; the target approach removed about 22% of the

sediment yield, and the random approach removed about 7%

(Fig. 9).

4. Conclusions

The concept of identifying, selecting, and targeting critical

areas of point and NPS pollution for pollutant reduction has

been widely recognized and studied. In this study, a watershed

modeling approach was used to quantify effects of imple-

menting a single BMP on incremental increases in watershed

a g r i c u l t u r a l w a t e r m a n a g e m e n t 9 5 ( 2 0 0 8 ) 1 1 8 9 – 1 2 0 01198

sub-basins and evaluate effectiveness of a targeted approach

compared with a random approach at reducing estimated

pollutant loadings, both overland and at the watershed outlet

(Richland). Priority areas for the targeted approach were

selected based on model-predicted sediment yield and FCB

concentration. A targeted watershed modeling approach

using SWAT was found to be effective at reducing sediment

yield load both overland and at the watershed outlet, but this

approach was effective at reducing FCB only from overland

flow. This study also used SWAT to evaluate effectiveness of

various VFS lengths in removing FCB concentration and

sediment yield from the agricultural watershed. The model

determined that applying VFS using a target approach can be

more cost-effective than a random approach. Other BMPs,

such as stream fencing to reduce cattle access to the stream or

rotational grazing, and variable percentages of direct point

loads could be tested in future studies.

Acknowledgements

This material is based on work supported by the USDA

Cooperative State Research, Education, and Extension Service

under agreement no. 2003-04949, 2003-51130-02110. We

acknowledge the contributions of Mr. Will Boyer, extension

watershed specialist at Kansas State University; Mr. Mark

Jepson, environmental scientist at the Kansas Department of

Health and Environment (KDHE); and Mr. Matt Peek, furbearer

biologist, Dr. Lloyd Fox, big game coordinator, Mr. Jim Pitman,

small game coordinator, and Mr. Marvin Kraft, waterfowl

research biologist, at the Kansas Department of Wildlife and

Parks (KDWP). Contribution no. 08-284-J from the Kansas

Agricultural Experiment Station, Manhattan, KS.

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