Quantifying Hydrologic and Water Quality Responses to Bioenergy Crops in Town Creek Watershed in Mississippi

doi:10.4236/jsbs.2013.33028

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Journal of Sustainable Bioenergy Systems, 2013, 3, 202-208

http://dx.doi.org/10.4236/jsbs.2013.33028 Published Online September 2013 (http://www.scirp.org/journal/jsbs)

Quantifying Hydrologic and Water Quality Responses to

Bioenergy Crops in Town Creek Watershed in Mississippi

Prem B. Parajuli, Sarah E. Duffy

Department of Agricultural and Biological Engineering, Mississippi State University, Starkville, USA

Email: [email protected]

Received June 10, 2013; revised July 11, 2013; accepted July 28, 2013

tion License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly

cited.

ABSTRACT

Bioenergy crops are considered as a feedstock source, which can be grown in marginal soils. However, these crops may

have different levels of crop yield potential and environmental benefits. The objectives of this study were to model and

compare the effects of four bioenergy crops (corn—Zea mays, soybean—Glycine max (L.) Merr., miscanthus—Miscan-

thus-giganteus, and switchgrass—Panicum virgatum) in the Town Creek watershed (TCW) in northeast Mississippi

using the Soil and Water Assessment Tool (SWAT) model. The calibrated SWAT model for TCW was used to quantify

impacts to streamflow, crop yield, and sediment yield. The SWAT model reasonably (<−12%) predicted long-term

(January, 1990 to September, 2009) monthly streamflow (25.88 m3·s−1) from the TCW when compared with the USGS

observed stream flow (29.34 m3·s−1). In addition, model reasonably predicted (±6%) average annual corn yield (4.66

Mg·ha−1) and soybean yield (1.42 Mg·ha−1) as compared to National Agricultural Statistics Service (NASS) reported

average annual corn (4.96 Mg·ha−1) and soybean yield (1.34 Mg·ha−1) from the watershed. Further, the model simulated

results from this study determined that long-term average annual feedstock yield from TCW is the greatest when grow-

ing miscanthus grass (817,732 Mg) followed by switchgrass (477,317 Mg), corn (236,132 Mg), and soybeans (65,235

Mg). The SWAT model predicted the greatest annual average sediment yield (6.62 Mg·ha−1) from continuous corn crop

scenario while the perennial grasses (switchgrass and miscanthus) had the lowest sediment yield (2.91 Mg·ha−1 and 3.20

Mg·ha−1 respectively). Overall, producing a perennial grass in the TCW would provide the largest biomass feedstock

source with the least environmental impact. The results of this study will help to compare benefits of landuse change

practices in bioenergy and water quality.

Keywords: Perennial Grass; Water Quality; Watershed; SWAT

1. Introduction

There has been a worldwide increase in energy consump-

tion during the twentieth century, and it is expected to

increase by at least 50 percent in the next 20 years [1].

Consequently, there has been an exponential increase in

atmospheric carbon dioxide (CO2) concentrations [2].

Global dependence on oil, coal, and natural gas has al-

ready led to several crises directly caused by shortages or

sudden price spikes. These events have been tocsins of

the shaky and limited future of fossil fuels. At present,

there is no dire shortage of fossil fuels. However, the

long-term availability and desirability of using fossil

fuels are a matter of concern. Bioenergy crops are one of

the renewable energy sources of the future [3], and are

increasingly considered as the key to any strategy for

reduction of fossil fuel dependence, energy independence,

and mitigation for global climate change [4]. Bioenergy

crops are simply defined as any plant material used to

produce bioenergy, which is in turn defined broadly as

any conversion of biomass materials into an energy source,

such as power, heat, or liquid biofuels. Bioenergy crops

produce a large volume of biomass, have high energy

potential, and can be grown in marginal soils [5]. To be

practical, bioenergy crops must not only be viable feed-

stock, they must also be attractive to farmers to choose to

grow in place of conventional crops [5].

Initial forays in biofuel production focused on food

crops, most commonly corn and soybeans. However,

using food crops as fuel sources created concern over

competition. Thus, second generation bioenergy produc-

tion has shifted focus to use cellulosic material as feed-

stock instead [4,6]. Perennial grasses such as switchgrass

and miscanthus have garnered increased attention and are

the two leading cellulosic biofuels [4,5,7-11]. Cellulosic

P. B. PARAJULI, S. E. DUFFY 203

biomass yields more fuel per unit land area with less ag-

ricultural input such as fertilizer and pesticides than that

is achieved in grain-based ethanol production [4,7,10,12],

which offers forage material [13], and is proven to be

beneficial for water quality [14].

In addition to their promise as energy sources, bio-

energy crops, especially grasses, they have the capacity to

serve as important carbon sinks which could lead to no-

table offset of greenhouse gases [15]. The need for agri-

cultural involvement in greenhouse gas mitigation via

terrestrial carbon sequestration has been widely recog-

nized since the 1990s [3]. A plant removes CO2 from the

atmosphere through photosynthesis whereby the CO2 is

then broken down into carbon and oxygen. The oxygen is

re-released to the atmosphere as waste while the carbon

is used for food and incorporated into the plant. As plants

die or are harvested, the carbon-based biomass (such as

leaves and stems) is converted into biofuel, and the left-

over plant residue (such as roots and stalks) decay in the

soil whereby the carbon becomes soil organic carbon

(SOC). In fact, soil organic carbon constitutes more than

twice as much stored carbon as that of the earth’s vegeta-

tion [16].

Among all of the benefits associated with bioenergy

crop production, uncertainties linger [17]. It is important

to consider all of the implications associated with land

use at varying spatial and temporal scales. Realistic pro-

duction potential of the candidate bioenergy crops in

different regions should be assessed on the state as a

whole and on a watershed scale. Additionally it is critical

to determine the impact of traditional crops versus cellu-

losic bioenergy crops on soil and water quality, as well as

their impact on overall watershed health. Research

should also focus on the long-term effects on carbon se-

questration and greenhouse gas emissions. Once the

benefits and consequences have been satisfactorily and

thoroughly researched, an economic analysis should be

performed to determine the feasibility of large or small-

scale bioenergy crop production.

Predicting how changes in the agricultural landscape

will influence water quality is a complex issue that re-

quires an appropriate modeling tool capable of represent-

ing important aspects of the system [17]. The model se-

lected for this study depended on three factors: 1) its

ability to represent watershed influences on water quality

at varying spatial scales; 2) its ability to simulate water-

shed influences of natural, agricultural, and urban land as

well as bioenergy crops; and 3) its ability to accurately

predict the yields of bioenergy crops. Therefore, the

model used in this study was the Soil and Water Assess-

ment Tool (SWAT) [18]. SWAT is a computational hy-

drologic model that has been used extensively to effec-

tively assess the potential watershed-scale impacts of

land management changes across various temporal scales

[19] and has shown promise for biofuel-related applica-

tions [14,17,20,21]. There is only a limited body of lit-

erature available for the second generation bioenergy

crops. Switchgrass and miscanthus have only recently

been studied using SWAT [14,21].

The objectives of this study were to model and com-

pare the effects of four bioenergy crops in the Town

Creek watershed (TCW) in northeast Mississippi using

the SWAT model. The calibrated SWAT model for TCW

was used to identify impacts to streamflow, crop yields,

and sediment yield if all land uses designated as cropland

(corn, cotton, hay, soybeans and winter wheat) were

converted to corn, soybeans, switchgrass or miscanthus.

These four scenarios were compared to values predicted

using the baseline model which models current water-

shed conditions and assumes annual rotation of corn and

soybeans, as well as a diversity of other crops throughout

the watershed.

2. Materials and Methods

2.1. Study Area

The Town Creek watershed (TCW) located in northeast

Mississippi was the study area of this study. The TCW is

estimated to have about 177,500 ha area (Figure 1),

which is a part of the Upper Tombigbee River Basin. The

majority of the TCW is located within the Lee, Union,

and Pontotoc counties. However, the watershed also has

some areas in Chickasaw, Monroe and Itawamba coun-

ties. Based on climate data from 1990 to 2009 [22], av-

erage annual rainfall is 154 cm with average annual

temperature of 16.3˚C. The TCW is known as agricul-

tural watershed with about 1,000 farms of different sizes

[23]. The Town Creek runs from the watershed and

drains near Nettleton, MS (USGS-02436500) [24].

2.2. Model Description

The SWAT model is freely available from the public

domain in different versions. This study utilized Arc-

SWAT version of the model, which is interfaced with

ArcGIS 9.3. The SWAT model is a watershed scale hy-

drological model that simulates watershed processes on

three time steps (daily, monthly, yearly). SWAT model

predicts surface runoff, sediment yields, potential evapo-

transpiration, and crop yields [18]. More detail model

algorithms are described in the model documentation

[18,25]. This study used the Curve Number (CN) method

to estimate surface flow [26]. The SWAT model divides

a watershed into sub-watersheds, which are connected by

a stream network. Each sub-watershed is sub-divided in

to several hydrologic response units (HRUs), which are

unique spatial units of soils, land use, and topography.

The SWAT simulations and calculations are first per-

formed at the HRU and sub-watershed levels, and then

P. B. PARAJULI, S. E. DUFFY

204

Figure 1. Location of Town Creek watershed showing rain gages in northeast Mississippi.

literature. Based on the successful hydrologic calibration

of the model, it was assumed that the scenarios could be

expected to quantify reasonable results with minimum

user bias as accurate estimates are necessary in agricul-

tural management and decision-makings [20,32,33].

routed through stream network to the watershed outlet

[18,26,27].

The SWAT model has built-in Environmental Impact

Policy Climate (EPIC) crop growth model [28] to predict

crop biomass and crop yields using the harvest index of

the crop. Crop growth model considers the accumulation

of heat units and crop growth ceases when the crop meets

the cumulative heat unit required to reach the maturity of

the crop [21]. This study utilizes field level information

such as planting date, harvesting date, tillage operations,

fertilizer application date and rate in the model manage-

ment as described in the SWAT model [19,25].

2.4. Crop Simulation

Switchgrass is a warm season, tall-growing, perennial

grass that is native to much of the United States include-

ing Mississippi. It is well adapted to summer conditions

with peak growth occurring from May through Septem-

ber. Switchgrass produces large amounts of cellulose,

which can be converted to ethanol and can also produce

high-quality forage [5]. The SWAT model default pa-

rameters for Alamo switchgrass were used, with a few

modifications [17]. The cropland simulated with switch-

grass were initialized as mature stands with a leaf area

index of 0.5, initial biomass of 500 kg·ha−1, and 3 m

rooting depth [34]. It was also assumed that switchgrass

required 1,100 physiological heat units to reach maturity.

Planting and harvesting dates for switchgrass were ob-

tained from published literature [35]. The automatic fer-

tilization option was selected in the SWAT model for

fertilizer application management in order to account for

spatial and site-specific differences in nutrient require-

ments for switchgrass and miscanthus grass since that

data is currently unavailable [17]. Miscanthus is a rela-

tively new crop to be considered commercially viable

and it is not available in the SWAT crop database.

Therefore all of the crop parameters for miscanthus were

taken from published literatures [14,36].

2.3. Model Input and Evaluation

The SWAT model requires geospatial data to develop

model input data such as topography, soils data, land

use/land cover data, climate data, and management data.

This study used US Geological Survey (USGS) 30

meter by 30 meter grid digital elevation model (DEM)

data [29]. The State Soil Geographic Database) [30] was

used to create a soil database for watershed. Model util-

izes landcover data from the cropland data layer [31].

The climatic data for the watershed was used from the

available three local stations (Tupelo, Pontotoc, Verona)

as maintained by respective weather stations [22] and the

SWAT model weather generator for missing data [25].

Model performances were evaluated based on the

USGS observed monthly stream flows, NASS reported

annual yields for corn and soybeans, and reported yields

for switchgrass and miscanthus in Mississippi from the

P. B. PARAJULI, S. E. DUFFY 205

3. Results and Discussion

3.1. Crop Yield

The long-term crop yields of corn (1989-2011) and soy-

beans (1989-2011) were compared to the baseline model

which was calibrated using observed crop yield data

from NASS. The annual simulated yield was averaged

across the entire watershed and compared to the average

annual yield across the watershed assuming a cropping

pattern of soybeans in year one followed by corn in year

two. Crop yield scenarios were simulated on a yearly

time step from 1990 through 2011. An overall represent-

tation of the predicted yields is reported in Figure 2.

Results for the annual yield for soybeans in soy-

bean-only cropping as compared to the baseline alternat-

ing corn-soybean cropping pattern were 1.42 Mg·ha−1

and 1.19 Mg·ha−1 respectively. The watershed average

yield reported by NASS was 1.34 Mg·ha−1. Average an-

nual yield for continuous corn was 5.14 Mg·ha−1 com-

pared with 4.66 Mg·ha−1 in the baseline calibrated model.

Comparatively, the watershed average yield reported by

NASS was 4.96 Mg·ha−1. The results showed the 21-year

(1990-2011) average yield of 10.39 Mg·ha−1 for switch-

grass in the TCW.

The yield had a range of 4.18 Mg·ha−1 with a mini-

mum predicted yield of 8.30 Mg·ha−1 and maximum pre-

dicted yield of 12.48 Mg·ha−1. These predicted values are

slightly lower than what has been reported in other lit-

erature for the US [17]. The SWAT-predicted switch-

Figure 2. Estimated annual yield of four bioenergy crops

from 1990-2011 in TCW.

grass yields varied from zero in the northern U. S. to over

16 Mg·ha−1 in southern Illinois, Arkansas, western Ken-

tucky, and Tennessee, while yields predicted across the

southern extremes of the eastern US were between 6 and

12 Mg·ha−1. Other studies showed that the predicted

switchgrass yield ranged from 8 to 40 Mg·ha−1 in the

Midwestern US [9] and from 9 to 24 Mg·ha−1 in the en-

tire Upper Mississippi River Basin [20]. The predicted

yield values in this study were slightly higher than ob-

served field data from trials in the upper plains states

which had 5-year average yield of 5 Mg·ha−1 [37].

For miscanthus, the 21-year (1990-2011) average yield

was 17.80 Mg·ha−1 and had a range of 9 Mg·ha−1 (maxi-

mum 22.2 Mg·ha−1, minimum 13.2 Mg·ha−1). Similar to

these findings, previous studies also reported great varia-

tion. Previous studies reported [9] miscanthus yield be-

tween 0 and 62 Mg·ha−1 in the Midwestern U. S. and 30

and 42 Mg·ha−1 in Illinois [8]. It was estimated that the

available cropland of the TCW (45,940 ha) can produce

817,732 Mg of average feedstock annually if miscanthus

grass is grown in the watershed. Similarly switchgrass,

corn, and soybeans have the potential to produce an av-

erage annual feedstock of 477,317 Mg, 236,132 Mg, and

65,235 Mg respectively in TCW.

3.2. Streamflow

Streamflow evaluation was conducted on monthly time

scale for each of the four scenarios. The baseline model

had an average monthly flow of 25.88 m3·s−1 at the wa-

tershed outlet located in Nettleton, Mississippi during the

237-month (January 1990 through September 2009)

study period. The results of this portion of the study

show very similar results to both the baseline model and

the four cropping scenarios (Table 1). Mean monthly

streamflow at the watershed outlet for both continuous

corn and continuous soybean production during the study

period were almost identical, as were the mean monthly

streamflow values for switchgrass and miscanthus. As

expected, the streamflow was reduced in both grass sce-

narios. Reduced streamflow is a result of the grasses al-

lowing less runoff to contribute to the streamflow. It

could also be a result of longer periods of land cover

since the growing season for grasses is longer than for

either corn or soybeans. The average monthly observed

streamflow for the USGS gage station at this location

was 29.34 m3·s−1.

3.3. Sediment Yield

There was no observed sediment yield data to calibrate

the baseline condition of the model. Thus, for this study

sediment load for the four new cropping scenarios was

compared to the baseline model to assess differences due

P. B. PARAJULI, S. E. DUFFY

206

to land use change. For all five scenarios, SWAT pre-

dicted similar sediment yield curves. The results show

that the continuous corn cropping scenario had the

greatest annual average sediment yield (6.62 Mg·ha−1)

while the perennial grasses (switchgrass and miscanthus)

had the lowest sediment yield (2.91 Mg·ha−1 and 3.20

Mg·ha−1 respectively) (Figure 3). These results are in

agreement with other research which has shown that

grasses improve both water quality [14] and soil quality

[3].

4. Conclusions

Model estimated mean monthly streamflow evaluation

showed very close results to both the baseline model and

the four cropping scenarios with corn and soybeans pro-

ducing similar values and the grasses producing similar

values. When compared to observed gage data at the

same location, it was found that the model under predicted

streamflows. The overall results of this study determined

that long-term average annual feedstock yield from the

TCW is greatest when growing miscanthus grass as fol-

lowed by switchgrass, corn and soybeans. Miscanthus

grass can produce 817,732 Mg of feedstock annually,

Table 1. Comparison of monthly stream flows from Ja-

nuary 1990 to September, 2009.

Mean

streamflow (m3·s−1)

Maximum

(m3·s−1)

Minimum

(m3·s−1)

USGS gage station 29.34 237.24 0.51

Baseline 25.88 157.16 0.0938

Continuous corn 24.89 153.45 0.0043

Continuous

soybeans 25.01 153.88 0.0348

Switchgrass 22.75 161.36 0.00

Miscanthus 19.20 161.37 0.00

Sediment yield (Mg ha

-1

)

Soybeans Corn Switchgrass Miscanthus Baseline

Figure 3. Predicted annual sediment yield (Mg·ha−1) for

four bio-energy crops scenarios in the watershed.

followed by switchgrass (477,317 Mg), corn (236,132

Mg), and soybeans (65,235 Mg).

An analysis of sediment yield showed the continuous

corn cropping scenario in the watershed, which had the

greatest annual average sediment yield (6.62 Mg·ha−1),

and the switchgrass scenario had the least (2.91 Mg·ha−1)

sediment yield. It was also observed that SWAT pre-

dicted similar sediment yield curves for all five scenarios.

Overall it would seem that producing a perennial grass in

the TCW would provide the largest biomass feedstock

source with the least water quality and environmental

impact.

5. Acknowledgements

This material is based upon work performed through the

Sustainable Energy Research Center at Mississippi State

University and is supported by the Department of Energy

under Award Number E-FG3606GO86025; Micro CHP

and Bio-fuel Center. We acknowledge the contributions

of landowners of the research fields in the watershed for

this research.

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