prioritizing watersheds for conservation actions in … watersheds for conservation actions ......

Prioritizing Watersheds for Conservation Actionsin the Southeastern Coastal Plain Ecoregion

Taeil Jang • George Vellidis • Lyubov A. Kurkalova •

Jan Boll • Jeffrey B. Hyman

Received: 3 April 2014 / Accepted: 30 November 2014 / Published online: 21 December 2014

� Springer Science+Business Media New York 2014

Abstract The aim of this study was to apply and evaluate

a recently developed prioritization model which uses the

synoptic approach to geographically prioritize watersheds

in which Best Management Practices (BMPs) can be

implemented to reduce water quality problems resulting

from erosion and sedimentation. The model uses a benefit–

cost framework to rank candidate watersheds within an

ecoregion or river basin so that BMP implementation

within the highest ranked watersheds will result in the most

water quality improvement per conservation dollar inves-

ted. The model was developed to prioritize BMP imple-

mentation efforts in ecoregions containing watersheds

associated with the USDA-NRCS Conservation Effects

Assessment Project (CEAP). We applied the model to

HUC-8 watersheds within the southeastern Coastal Plain

ecoregion (USA) because not only is it an important agri-

cultural area but also because it contains a well-studied

medium-sized CEAP watershed which is thought to be

representative of the ecoregion. The results showed that the

three HUC-8 watersheds with the highest rankings (most

water quality improvement expected per conservation

dollar invested) were located in the southern Alabama,

northern Florida, and eastern Virginia. Within these

watersheds, measures of community attitudes toward con-

servation practices were highly ranked, and these indicators

seemed to push the watersheds to the top of the rankings

above other similar watersheds. The results, visualized as

maps, can be used to screen and reduce the number of

watersheds that need further assessment by managers and

decision-makers within the study area. We anticipate that

this model will allow agencies like USDA-NRCS to geo-

graphically prioritize BMP implementation efforts.

Keywords Synoptic assessment � Geographic

prioritization � Best Management Practices � Regional

scale � Indicator

Introduction

Non-point source (NPS) pollution has long been considered

a threat to water resources (Shen et al. 2011). Sediment

ranks as the largest NPS pollutant of surface waters in the

United States (Vellidis et al. 2003; USEPA 2005). To

address the problems caused by NPS pollution, and sedi-

ment in particular, a large number of studies have been

conducted over the past several decades to develop and

evaluate conservation practices, also referred to as Best

Management Practices (BMPs), for controlling NPS pol-

lution (USEPA 1996; Secchi et al. 2007; Maxted et al.

T. Jang

Rural Construction Engineering Department, Institute of

Agricultural Science and Technology, Chonbuk National

University, Jeonju-si, Jeollabuk-do 561-756, Republic of Korea

e-mail: [email protected]

G. Vellidis (&)

Crop and Soil Sciences Department, University of Georgia,

2360 Rainwater Road, Tifton, GA 31793-5766, USA

e-mail: [email protected]

L. A. Kurkalova

Economic & Finance Department, and Energy and

Environmental Systems Program, North Carolina A&T State

University, Greensboro, NC 27411, USA

J. Boll

Biological and Agricultural Engineering Department, University

of Idaho, Moscow, ID 83844, USA

J. B. Hyman

Conservation Law Center, Bloomington, IN 47408, USA

123

Environmental Management (2015) 55:657–670

DOI 10.1007/s00267-014-0421-9

2009; Nigel and Rughooputh 2010; Zhang et al. 2010; Shen

et al. 2011).

The United States Environmental Protection Agency

(USEPA) is actively engaged in assisting states to set total

maximum daily loads (TMDLs) for stream segments that

are not meeting water quality standards for their designated

use. Many causes of impairments for which TMDLs have

been developed in the US are typically sediment related.

The methods for reducing sediment loading in agricultural

landscapes have been studied extensively (Secchi et al.

2007; Nigel and Rughooputh 2010; Zhang et al. 2010;

Limbrunner et al. 2013a).

Beginning in 1975, the USEPA and the USDA funded a

series of studies to assess the effects of implemented BMPs

on water quality in the US. A general finding of all these

studies is that although water quality improvements

resulting from BMP implementation can be reliably mea-

sured at the field scale, it has been difficult to measure

improvements in water quality at the watershed scale

(Osmond 2010; Cho et al. 2010; Meals et al. 2012). Pos-

sible explanations for this include that decades are required

to overcome legacy effects of NPS pollution—especially

sedimentation of streams and rivers, and that much of the

investment in BMPs was widely distributed across the

agricultural landscape and not concentrated in areas con-

tributing the most to water quality problems or in areas

most likely to respond to BMPs (Jang et al. 2013).

Understanding the best way to allocate limited resources

is a constant challenge for water quality improvement

efforts. Under a geographic prioritization scheme, resour-

ces are allocated to watersheds and areas within watersheds

where the functional benefits from implementation are the

greatest (Babcock et al. 1996; Vellidis et al. 2003; Feng

et al. 2006; Sandoval-Solis et al. 2011; Jang et al. 2013;

Limbrunner et al. 2013b). In other words, geographic pri-

oritization attempts to allocate resources to the areas where

BMP implementation results in the most water quality

improvement for the funds invested. Geographic prioriti-

zation can be applied to many scales ranging from areas

within a relatively small watershed to watersheds within an

ecoregion or river basin.

A wide variety of modeling approaches that range from

relatively simple process-based models (Brooks and Boll

2011) to more complex hydrological models and heuristic

algorithms (Bekele and Nicklow 2005; Arabi et al. 2006;

Maringanti et al. 2009; Pandey et al. 2009; Rodriguez

et al. 2011) have been used to assess and prioritize the

placement of BMPs within the agricultural landscape and

to assess their effect on water quality. Depending on the

approach, these models can be easy to apply or demand

large amounts of data and resources. In almost all cases,

however, these models address only the biophysical

parameters of the prioritization and do not consider the

socioeconomic factors that also affect the success of

conservation efforts.

Jang et al. (2013) developed a prioritization model

which uses the synoptic approach to geographically prior-

itize watersheds within which suites of agricultural BMPs

can be implemented to reduce erosion and sediment

delivery to the watershed outlets. The model uses a bene-

fit–cost framework to rank candidate watersheds within a

river basin or ecoregion so that BMP implementation

within the highest ranked areas will result in the most

sediment reduction and water quality improvement per

conservation dollar invested. The model can be applied

anywhere and at many scales provided that the selected

suite of BMPs is appropriate for the evaluation area’s

biophysical and climatic conditions.

The synoptic approach uses a series of easily measured

indicators for prioritization rather than directly measuring

the ecological endpoint (in this case water quality

improvement) because direct measurement is typically

difficult and costly. Indicators which affect the ranking of

watersheds within ecoregions or river basins include

measures of community attitudes toward conservation

practices, cost of implementing BMPs, land available for

additional BMPs, and predicted sediment delivery to

waterways. Thus, the model considers both biophysical and

socioeconomic factors. Another important advantage of the

synoptic approach is that it allows best professional judg-

ment to supplant or supplement data in cases where

information and resources are limited. The model was

specifically developed as a tool for prioritizing BMP

implementation efforts in the ecoregions containing the 13

watersheds associated with the USDA-NRCS Conservation

Effects Assessment Project (CEAP) effort. Thus, unlike

other applications of the synoptic approach which were

developed for specific regions (Abbruzzese and Leibowitz

1997; Vellidis et al. 2003), this model can be applied

nationally under a wide variety of biophysical and climatic

conditions. The aim of this study was to apply the priori-

tization model to one of these ecoregions and assess its

performance.

Materials and Methods

Site Description

The continental US has been classified into ecoregions by

several different studies (e.g., Bailey 1983; McMahon et al.

2001). McMahon et al. (2001) proposed 84 ecological

regions within the conterminous US. Each region includes

areas within which biotic, abiotic, terrestrial, and aquatic

capacities, and potentials are similar. We applied the pri-

oritization model to the southeastern Coastal Plain

658 Environmental Management (2015) 55:657–670

123

ecoregion. The ecoregion is contained within the USDA-

NRCS Major Land Resource Area named the South

Atlantic and Gulf Slope Cash Crops, Forest, and Livestock

Region. We selected this ecoregion because not only is it

an important agricultural area but also because it contains

the Little River Experimental Watershed (LREW), a well-

studied 320 km2 CEAP watershed which is located in

southern Georgia and thought to be representative of the

southeastern Coastal Plain (Cho et al. 2010). In addition,

members of the project team have studied both the LREW

and the southeastern Coastal Plain ecoregion extensively

and are familiar with both its biophysical and socioeco-

nomic conditions, thus making this ecoregion a good test-

case for the prioritization model. Finally, the model was

initially validated on the LREW (Jang et al. 2013).

From southwest to northeast, the ecoregion spans por-

tions of Louisiana, Mississippi, Tennessee, Alabama,

Florida, Georgia, South Carolina, North Carolina, and

Virginia (Fig. 1). It partially or entirely contains 161 HUC-

8 watersheds (8-digit Hydrologic Unit Code). The ecore-

gion is characterized by nearly level and gently undulating

valleys. Elevation ranges from 25 to 200 m, increasing

gradually from the lower Coastal Plain northward. Local

relief is mainly 3–6 m, but it is 25–50 m in some of the

more deeply dissected areas.

The average annual precipitation in most of the ecore-

gion is 1,040–1,525 mm, increasing from north to south. It

is typically 1,550–1,830 mm in the extreme southwest of

the ecoregion, inland along the Gulf Coast. The average

annual temperature is 13–20 �C, increasing from north to

south.

The soils in the area have a thermic soil temperature

regime, an udic or aquic soil moisture regime, and siliceous

or kaolinitic mineralogy. They generally are very deep,

somewhat excessively drained to poorly drained, and

loamy. Corn, cotton, peanuts, soybeans, and wheat are the

major agronomic crops grown in the area. Conservation

tillage has become increasingly important although con-

ventional tillage is still the predominant agricultural land

management method.

Synoptic Approach

A synoptic assessment utilizes a prioritization criterion for

comparatively ranking conservation alternatives. The pri-

oritization criterion is generally expressed as the ratio of

the marginal change in ecological function per unit of

management effort (Vellidis et al. 2003). In the prioriti-

zation model, Jang et al. (2013) defined their prioritization

criterion as the marginal change in total sediment load at a

watershed outlet, dSL (kg/km2/year), per conservation

dollar expended (d$) for BMP implementation, or dSL/d$.

Change in total sediment load is not only a function of

the area conserved but also of the hydrologic response of

the watershed (Vellidis et al. 2003). Increased marginal

attenuation of the hydrologic response of a watershed is

primarily a function of the marginal change in conserved

area of a watershed. This process can be incorporated into

Eq. 1 by applying the chain rule:

dSLj

d$j

¼ dCAj

d$j

� dSLj

dCAj

ð1Þ

wheredSLj

d$j

is the marginal change in total sediment load per

conservation dollar invested in BMPs in watershed j,dCAj

d$j

is

the marginal change in conserved area per conservation

dollar invested in watershed j, anddSLj

dCAj

is the marginal

change in sediment load per conserved area j.

Equation 1 depicts the mathematical formulation of the

conceptual model that links the ecological endpoint (sedi-

ment load reduction per conservation dollar invested for

BMPs) with the descriptors selected to prioritize water-

sheds. The descriptors and indicators represent the social,

economic, and hydrologic drivers of sediment load reduc-

tion within a watershed and were selected from the litera-

ture and through consultation with appropriate

professionals (Lowrance and Vellidis 1995; Abbruzzese

and Leibowitz 1997; Vellidis et al. 2003; Machado et al.

2006; Khare et al. 2007). Our descriptors and their indi-

cators, measurement endpoints, and potential data sources

are discussed in detail below and are summarized in

Table 1.

The term dCA/d$ is a function of the community’s

support and willingness to engage in conservation activities

and the efficiency of BMP implementation within a

watershed, and can be expressed as follows:

dCAj

d$j

¼ f community support and willingness forð

conservation activities; BMP implementation factorsÞ:ð2Þ

Community support and willingness for conservation

activities and BMP implementation factors are the two

descriptors for this term. Community support and willing-

ness for conservation activities is a qualitative measure of the

watershed residents’ disposition toward watershed conser-

vation activities and was described by Norton et al. (2009) as

the social context affecting efforts to improve a watershed’s

condition. In this study, we adopted indicators for this

descriptor which are well supported in the literature (Norton

et al. 2009; USEPA 2012). For this study, we selected only

indicators for which we could readily identify measurement

endpoints supported by publicly and electronically accessi-

ble data sources (Table 1). This should hold true for any

Environmental Management (2015) 55:657–670 659

123

synoptic assessment. BMP implementation factors are

mostly biophysical and were selected to capture not only the

cost of implementing BMPs across the ecoregion but also the

availability of land on which to implement BMPs. The fol-

lowing paragraphs describe the data sources used in this

study to quantify the measurement endpoints.

Measurement Endpoints and Data Sources for dCA/d$

Community Support and Willingness for Conservation

Activities

Active watershed protection and environmental protec-

tion groups are obvious indicators of community

support and willingness for conservation activities. The

number of watershed protection groups in a watershed

(PG) was obtained from USEPA watershed data (www.

epa.gov/surf). The resulting data are shown graphically

in Fig. 1a. The number of all environmental group

chapters associated with each watershed (ENVG) was

not easily quantified so this endpoint was measured

with the chapters of three national environmental pro-

tection groups—The Nature Conservancy, Sierra Club,

and Audubon Society. These data were obtained from

their websites; however, the data were available only by

state and so we assigned all watersheds within a given

state with the same number for ENVG as shown in

Fig. 1b.

Fig. 1 Maps of measurement endpoint data for a number of

watershed protection groups (PG), b number of environmental group

chapters (ENVG), c number of university or colleges within the

watershed (UP), d percent of people 25 years and over who have

completed a bachelor’s degree (EL), e areas protected by conservation

easements (PREA), f agricultural and urban areas (ARUR), g area

ratio of implemented conservation practices associated with federal

agencies (NRCA), and h boundaries of soil and water conservation

districts (SWCD) in the southeastern Coastal Plain, which is

superimposed boundaries of the 8-digit Hydrologic Unit Code

(HUC) watersheds (Color figure online)


123

http://www.epa.gov/surf

http://www.epa.gov/surf

Proximity to a university and education level positively

affects community support and willingness for conserva-

tion activities because among other things universities are

sources of information and technical expertise for water-

shed residents and because educated residents may better

appreciate the societal benefits of conservation practices.

University proximity (UP) was determined from the data

found at UnivSource (www.univsource.com), a website

which provides detailed information about higher educa-

tion in the United States and Canada. The number of uni-

versities in a watershed was aggregated at the county level

as shown in Fig. 1c. Education level (EL) data were

aggregated at the county level from the U.S. Census

website (Fig. 1d).1

Existing conservation easements indicate a willingness

to implement conservation practices. Digital map of areas

protected by conservation easements (PREA) were

obtained from the United States Geological Survey (USGS)

Gap Analysis Program (GAP) data (Fig. 1e).2

BMP Implementation Factors

Land availability for conservation practices is an important

indicator. This was quantified using two measurement

endpoints—the proportion of agricultural land to urban

land and the proportion of a watershed already protected by

NRCS programs. The proportion of agricultural land to

urban land was determined by using land use data obtained

from the USGS GAP data (Fig. 1f). Because data of

implemented conservation practices (NRCA) associated

with NRCS programs are not publically available, we used

the percentage of a watershed’s farmland in conservation

tillage to estimate how much of the land in a watershed

might already have adopted BMPs. These data are avail-

able at the county level from the Conservation Technology

Information Center (CTIC) (www.ctic.purdue.edu) and

were reaggregated into the HUC-8 watershed scale

(Fig. 1g). The number of political or agency jurisdictions

such as counties, USDA-NRCS soil and water conservation

districts, etc., contained within a watershed (political

complexity) can negatively influence the speed of adoption

and effectiveness of conservation activities because each

county or soil and water conservation district has different

interests and approaches to conservation. Because in this

study we were concerned strictly with erosion control

BMPs which are typically implemented with NRCS

Table 1 Descriptors, indicators, measurement end points, data sources, and source data scales used for the marginal change in conserved area

per conservation dollar (dCA/d$) term

Descriptors Indicators Measurement endpoints Data sources Data scale

and remark

Community support and

willingness for conservation

activities (CW)

Watershed

protection

activities (WP)

Density of watershed protection

groups (PG)

USEPA (Surf your watershed) HUC-8

Density of environmental group

chapters (ENVG)

web sites of the Nature

Conservancy, Sierra Club, and

Audubon Society

State

Density of university proximity

(UP)

UnivSource.com County

Education level (EL) County-level educational

attainment data from the Census

County

Conservation

programs (CP)

Areas protected by conservation

easements or similar activities

(PREA)

USGS GAP Analysis program 30 9 30 m

based-

grid

BMP implementation factors (IF) Implementation

cost (IC)

Cost of conservation actions (CC) Various NRCS documents State

Land availability

and complexity

(LA)

Conservation practice areas

(NRCA)

Conservation Technology

Information Center

County

Stability and disturbance (AGUR) USGS GAP Analysis program 30 9 30 m

based-

grid

Soil and water conservation

district (SWCD)

Boundary of soil and water

conservation districts from

NRCS State Office

SHP file

1 Information can also be found at: www.census.gov/hhes/socdemo/

education/data/index.html.2 Information can also be found at: http://gapanalysis.usgs.gov/

gapanalysis.


123

http://www.univsource.com

http://www.ctic.purdue.edu

http://www.census.gov/hhes/socdemo/education/data/index.html

http://www.census.gov/hhes/socdemo/education/data/index.html

http://gapanalysis.usgs.gov/gapanalysis

http://gapanalysis.usgs.gov/gapanalysis

technical assistance, we used the number of NRCS soil and

water conservation districts within a watershed to quantify

the political complexity indicator (SWCD). Data were

obtained from NRCS state offices (Fig. 1h).

Unfortunately, BMPs are notoriously hard to cost cor-

rectly (Secchi et al. 2007, Jang et al. 2013) as USDA-

NRCS State Offices report these costs in a wide variety of

ways. For example, the cost of installing a terrace for

erosion control is reported in terms of dollars per linear

foot with several scenarios and dollars per cubic yard of

soil. Converting to a uniform unit of cost proved very

difficult and obtaining more detailed information directly

from USDA-NRCS was hampered by confidentiality rules.

Table 2 shows BMP costs reported by each of the states

included in the southeastern Coastal Plain ecoregion for the

suite of conservation practices selected for this study.

Using the best professional judgment of the project team

and of natural resource managers from state and federal

agencies, we used a variety of techniques to convert the

information shown in Table 2 to useable form with pref-

erence given to converting the data to units of dollars per

acre (Table 3). For estimating the costs of implementing

conservation tillage and grassed waterways, we used the

following rules. If a state reported a per acre cost, we used

that rate for all watersheds in that state. If a state reported a

range of implementation costs, we used the average of the

range. If the state did not report a cost, we used an average

of the reporting neighboring states. For estimating the costs

of implementing terraces, we applied the costs used in

LREW across the entire ecoregion. We assumed a con-

struction cost of $0.50 per linear foot and a terrace density

that was a function of slope as shown in Table 3. For each

of the watersheds, the cost of implementing terraces was

determined by applying the terrace density associated with

the average slope of the agricultural land available for

additional BMPs. The cost of implementing the suite of

conservation practices in each of the ecoregion’s water-

sheds using the information in Table 3 (measurement

endpoint, CC) is visualized in Fig. 2.

Measurement Endpoints and Data Sources for dSL/dCA

In this study, hydrologic and sediment response within a

watershed was estimated using the Hydrologic Character-

ization Tool (HCT) (Brooks and Boll 2011; Brooks et al.

2013). The HCT is a web-interface program which uses a

modified version of the WEPP (Water Erosion Prediction

Project) model (Boll et al. 2011) to identify the effects of

various management practices on hydrologic flow paths

and sediment transport through specific land types in a

region. By limiting input to a few essential parameters,

HCT is easy to learn and apply over a wide range of

conditions. Jang et al. (2013) describe in detail how HCT is

used in our prioritization model.

Table 4 presents the physical attributes used for running

HCT in the southeastern Coastal Plain ecoregion. Annual

erosion rates were estimated from HCT combinations of

climatic conditions, slope, depth to the hydrologically

restrictive soil layer, land use, crop rotation, and tillage

practice. The erosion estimates were averages for a 30-year

simulation period with generated climatic conditions esti-

mated by using natural neighbor interpolation based on a

Thiessen polygon network of the 24 climate stations listed

in Table 4. Slope was classified with five grades and soil

types were clustered into five classifications based on depth

to the hydrologically restrictive soil layer using the USDA-

Table 2 Published costs for each of the three BMPs selected for

erosion control in the states included in the southeastern Coastal Plain

ecoregion

State Conservation tillage Grassed

waterways

Terraces

Alabama $68.50–$120.25 per

acre; 3 intensity

levels of no-till

$591.20–

$1,103.07

per acre; 4

scenarios

$0.64–$2.57

per linear

foot; 3

scenarios

Florida $93.28 per acre $3,774 per

acre

$6.17 per

cubic yard

Georgia No data $3,900–

$10,876 per

acre; 2

scenarios

$0.50 per

linear foot

Louisiana $22–$37 per acre; the

lower rate is for

ridge and mulch till,

and the higher—for

no-till

$3.10 per

cubic yard

$9.20 per

cubic yard

Mississippi $36–$45 per acre; 2

versions of no-till

$994.52 per

acre

$1.24–$10.32

per linear

foot; 6

scenarios

North

Carolina

$38 per acre $4.46–$7.14

per linear

foot

$2.85 per

cubic yard;

$1.51 per

linear foot

South

Carolina

$36.25 per acre $2,631.94–

$8,435.08

per acre; 5

scenarios

$7.05 per

linear foot

Tennessee No data $2,227.88–

$3,231.22

per acre

$1.00 per

cubic

yard—$1.09

per linear

foot

Virginia $22.01–$59.76 per

acre; the lower rate

is for mulch till, and

the higher—for no-

till

$2,515.20 per

acre

$0.70 per

linear foot


123

NRCS Soil Survey Geographic (SSURGO) database. Land

cover obtained from the USGS GAP data was classified as

forest, grass, and cultivated areas with five major crop

rotations and two tillage practices (conventional and no-

till).

We identified the dominant crop rotations within each

watershed using data from the USDA National Agricultural

Statistics Service (NASS) for the latest three-year period

from 2008 to 2010. The data were available at the county

level. The same rotation was used for the entire 30-year

simulation period. A random array function was used to

distribute the crop within the rotation which was being

grown and the tillage system used. Tillage was distributed

in proportion to the ratio of conventional tillage to conser-

vation tillage obtained from the CTIC (see earlier discus-

sion). Using ArcGIS (ESRI, Redlands, CA, USA), the data

layers used for HCT were converted into raster layers with

30 9 30 m grid cells. HCT was run for each grid cell in the

entire ecoregion, and each of the grid cells was assigned an

annual erosion rate based on its unique combination of

climate, slope, soil, crop rotation, and tillage practice.

Mathematically Combining Descriptors, Indicators,

and Measurement Endpoints

Implementing the model entails developing the mathemati-

cal expressions that combine the descriptors, indicators, and

measurement endpoints using standard combination rules

(Skutch and Flowerdew 1976; Hopkins 1977; O’Banion

1980; Smith and Theberge 1987; Abbruzzese and Leibowitz

1997; Leibowitz and Hyman 1999; and Hyman and Leibo-

witz 2000). We also incorporated the Judgment-based

Structural Equation Modeling (JSEM) approach developed

by Hyman and Leibowitz (2000). The details of

Table 3 BMP costs used for applying the prioritization model to the

southeastern Coastal Plain ecoregion

State Conservation

tillage

Grassed

waterways

Terraces

Alabama $94.48 per

acre

$847.14

per acre

$0.50 per linear foot: (1)

400 feet per acre in 40

acre field for 2–5 %

slope, (2) 500 feet per

acre in 40 acre field for

5–7 % slope, and (3)

600 feet per acre in 40

acre field for 7–15 %

slope

Florida $93.28 per

acre

$3,774.00

per acre

Georgia $56.21 per

acre

$7,388.00

per acre

Louisiana $29.50 per

acre

$5,001.33

per acre

Mississippi $40.50 per

acre

$994.52

per acre

North

Carolina

$38.00 per

acre

$4,250.00

per acre

South

Carolina

$36.25 per

acre

$5,533.51

per acre

Tennessee $53.44 per

acre

$2,729.55

per acre

Virginia $40.89 per

acre

$2,515.20

per acre

Fig. 2 Cost of implementing

the suite of conservation

practices (conservation tillage,

grassed waterways, and

terraces) across the southeastern

Coastal Plain ecoregion using

data from Table 3 (Color figure

online)


123

mathematically combining descriptors, indicators, and

measurement endpoints for the dCA/d$ and dSL/dCA terms

are described in Jang et al. 2013.

Results

There are 161 HUC-8 watersheds either partially or entirely

in the southeastern Coastal Plain ecoregion. However, sev-

eral of the watersheds have only a small portion of their area

within the ecoregion, while most of their land area is in the

Piedmont ecoregion. Because the Piedmont ecoregion’s

topography, land use, and agricultural land management are

sharply different from the Southeastern Plain, we were

concerned that these watersheds might skew the results of the

analysis. To address this concern, we analyzed land use

distribution in each watershed and also conducted a least

significant difference (LSD) analysis for three land use types

in all 161 watersheds. LSD was used for mean separation at

a = 0.05 level. As a result, 36 of the partially included

watersheds were excluded from the study because their land

use was significantly different from prevalent land use in the

ecoregion leaving 125 (78 %) of the original 161 HUC-8

watersheds.

Marginal Change in Conserved Area per Conservation

Dollar Invested (dCA/d$)

The visualization of watershed ranks for dCA/d$ is shown

in Fig. 3a. We classified the distribution of ranks using the

Fisher-Jenks procedure for determining natural break

classes (Jenks 1967). This procedure was preferred over a

quantile or equal area approach as it defines classes based

on a distribution pattern (Schweiger et al. 2002). Six

watersheds were grouped in the highest rank, and all had

the highest values for the majority of this term’s indicators.

The watershed with the highest rank also had the highest

ENVG and UP ranks.

Marginal Change in Sediment Load Per Conserved

Area (dSL/dCA)

Figure 4 shows the map of erosion rates resulting from

applying HCT to the 30 9 30 m grid cells. The grid cell

Table 4 Parameters used for running the HCT model over a 30-year simulation period with generated climate conditions in the southeastern

Coastal Plain ecoregion

Climate station (24-stations) Slope (5-classifications) Soil (5-classification) Management including crop rotation (12-types)

Brewton (AL) Flat (0–2 %) Luverne (to 18 cm)a Forest

Greensboro (AL) Moderate flat (2–5 %) Dulac (to 58 cm) Grass

Mobile (AL) Moderate (5–8 %) Dothan (to 84 cm) Corn_Peanuts with no tillage

Ozark (AL) Moderate steep (8–12 %) Atmore (to 122 cm) Corn_Peanuts with conventional tillage

Thomasville (AL) Steep (12–35 %) Orangeburg (to 160 cm) Corn_Soybeans with no tillage

High Springs (FL) Corn_Soybeans with conventional tillage

Tallahassee (FL) Corn_Cotton with no tillage

Talbotton (GA) Corn_Cotton with conventional tillage

Dublin (GA) Cotton_Peanuts with no tillage

Tifton (GA) Cotton_Peanuts with conventional tillage

Forest PO (MS) Cotton_Soybeans with no tillage

Poplarville (MS) Cotton_Soybeans with conventional tillage

State College (MS)

University (MS)

Fayetteville (NC)

Goldsboro (NC)

Cheraw (SC)

Columbia City (SC)

Yemassee (SC)

Martin UTN (TN)

Savannah (TN)

Hopwell (VA)

Lincoln (VA)

Walkerton (VA)

a Depth to restrictive soil layer


123

erosion rates within a watershed were aggregated to

determine the individual watershed’s erosion rate

(SLOADj) and divided by the maximum observed water-

shed erosion rate (SLOADmax) to calculate dSL/dCA (see

Jang et al. 2013). The watersheds were ranked as shown in

Fig. 3b. The five HUC-8 watersheds with the highest ero-

sions rates also had the some of the highest proportions of

land cultivated with conventional tillage (37.1, 33.5, 37.8,

24.9, and 27.2 %, respectively.) This was not surprising,

however, because predicted erosion rates were consistently

greater in cultivated areas with conventional tillage as

shown in Fig. 5. The Corn–Peanuts, Corn–Soybeans, and

Cotton–Peanuts crop rotations with conventional tillage

showed the greatest mean erosion rates. The greatest ero-

sion rates were found in Tennessee, Mississippi, and North

Carolina with the Corn–Soybean crop rotation.

Marginal Change in Total Sediment Load

per Conservation Dollar Invested (dSL/d$)

Figure 3c visualizes the watersheds’ ranks for dSL/d$—

the marginal change in erosion rates per conservation

dollar invested. Three watersheds, located in southern

Alabama, north-western Florida, and eastern Virginia,

make up the highest ranking group shown with the

darkest shading. In addition to having high erosion rates

and agricultural land available for BMPs, these water-

sheds have well-educated residents, high concentration of

universities, watershed protection groups, and low BMP

implementation costs making them the best candidate

watersheds for implementing conservation practices for

water quality improvement. These same three watersheds

are also in the group of the highest ranked watersheds

Fig. 3 Comparison of mapped ranks for a the marginal change in

conserved area per conservation dollar invested (dCA/d$), b the

marginal change in sediment load per conserved area (dSL/dCA), and

c the marginal change in total sediment load per conservation dollar

invested (dSL/d$) in the southeastern Coastal Plain ecoregion. Rank

is indicated by shade and the darker the shade, the higher the rank.

High ranks indicate high conservation priority. The highest ranking

watersheds are in southern Alabama, north-western Florida, and

eastern Virginia (Color figure online)


123

for dCA/d$ indicating the importance of the socioeco-

nomic indicators in the prioritization model. The priori-

tization results can be used to screen and reduce the

number of watersheds that need further assessment by

decision-makers and managers at agencies such as

USDA-NRCS.

Fig. 4 Map of erosion rates

predicted by the HCT model

based on running HCT for each

of the 30 9 30 m grid cells in

the southeastern Coastal Plain

ecoregion (Color figure online)

Fig. 5 Box-plot of erosion rates

versus crop rotation and tillage

system predicted by the from

the HCT model for southeastern

Coastal Plain ecoregion. For this

graph, CP, CS, CC, CtP, and

CtS indicate the crop rotations

of Corn–Peanuts, Corn–

Soybeans, Corn–Cotton,

Cotton–Peanuts, and Cotton–

Soybeans, respectively. NT and

CT indicate the Conservation

Tillage and Conventional

Tillage, respectively


123

Discussion

Data for a synoptic assessment can come from multiple

sources and are found in a variety of formats including

tabular data, computerized data bases, and mathematical

predictive models (Abbruzzese and Leibowitz 1997; Velli-

dis et al. 2003). Furthermore, best professional judgment is

occasionally used in the absence of data. Consequently, the

results of synoptic assessments are sometimes questioned

by decision-makers accustomed to using data-intensive,

process-based biophysical models. Game et al. (2013)

suggested that the quality and usefulness of conservation

priority setting can be improved by broader recognition that

conservation planners act as both modelers and decision

analysts and need to be trained in the science and philoso-

phy of these disciplines. In order to reduce ambiguity in our

application of the prioritization model, we selected only

descriptors and indicators which are well supported in the

literature (Norton et al. 2009), and we followed the JSEM

approach for evaluating indicators and developing the spe-

cific mathematical relationship between indicators.

We further evaluated the effect of the indicators we

selected and the data sources by which we quantified them.

Figure 6 shows the distribution of the values of each indi-

cator and term for the 125 ranked watersheds using a box and

whiskers plot. If we examine PGj/PGmax (density of water-

shed protection groups) and UP/UPmax (density of university

proximity) specifically, we can see that the highest ranked

watershed had twice the value of the next ranked. For some of

the community willingness indicators, the outliers drove the

rankings of the watersheds. In other words, the watersheds

which produced the outliers were the watersheds which

ranked the highest for the dCA/d$ and for dSL/d$.

The outliers do not necessarily indicate problems with the

data sources. If we again examine the two watersheds for

which PGj/PGmax and UP/UPmax are at 1.0, we find that these

watersheds contain multiple institutions of higher education

and multiple watershed protection groups. The presence of a

large number of watershed protection groups in watersheds

with multiple institutions of higher education is not sur-

prising and should indicate a watershed population more

aware of conservation issues and more likely to participate in

conservation actions. The real question is does this high

ranking in PG and UP really translate to greater success in

implementing BMPs to improve water quality? A number of

recent studies (Norton et al. 2009; Genskow and Prokopy

2009; Prokopy 2011; Reimer et al. 2012) suggest that the

indicators used in this study are appropriate for this purpose.

Additional indicators and their measurement endpoints were

suggested by Norton et al. (2009).

Although there are reasonable explanations for the

outliers, their effect on the prioritization model results are

of great concern to model users. The model’s ranking

results are an approximation of reality and cannot be

treated as scientific findings. The prioritization model

integrates both biophysical and socioeconomic factors that

affect the success of BMP implementation efforts and as

such is subject to bias in the results. Nevertheless, the

model results provide a starting point for policy makers and

natural resource managers to make resource allocation

decisions. The strength of the prioritization model is that it

identifies a group of watersheds which are the best candi-

dates for the planned conservation effort. But, prior to

allocation of resources for BMP implementation, additional

verification of the highest ranked watersheds must be done

through ground-truthing and/or the application of more

sophisticated watershed transport models (Schweiger et al.

2002; Jang et al. 2013).

Other concerns with the data include the different scales

at which data are available. Some of the data were avail-

able at the HUC-8 scale, while others were available at

30 9 30 m grid cell, county, or state scales. Clearly

aggregating up from fine scales is more preferable than

scaling down from coarse scales. This is particularly

important for indicators which greatly affect the model

results such as PG, ENVG, and UP. It is therefore quite

important that reasonable data estimates based on the same

scale should be obtained for individual watersheds. Indi-

cator data must always be evaluated for accuracy and

usefulness relative to the assessment objectives using

clearly established protocols (Vellidis et al. 2003).

This study’s aim was to demonstrate both the strengths

and the weaknesses of the synoptic approach and the spe-

cific prioritization model we used. A significant weakness

of the study was our inability to gain access to accurate

costs of implementing BMPs across the ecoregion. That

these data were available but that we were not able to

access them was perhaps the most frustrating aspect of the

entire study. Despite repeated efforts to obtain these data

from USDA-NRCS at both the state and national levels, it

proved difficult to do so, and we were forced to find the

information from various sources in the literature. Since the

aim of the prioritization model is to rank watersheds in

which the best water quality results can be achieved for the

financial investment, it is critical that accurate data become

available for these types of studies. Any future applications

of the prioritization model results of which will be used to

make resource allocation decisions must include accurate

and watershed-specific BMP implementation costs.

The erosion rates predicted by HCT for the HUC-8

watersheds might be different from those predicted by

other hydrological models such as SWAT. The primary

reason for differences in prediction is the scale of aggregate

areas and model types associated with a limited number of

soils applied by HCT. Jang et al. (2013) compared the

responses of HCT and SWAT when applied to the LREW


123

(the Georgia CEAP watershed) and found generally similar

results from both models. HCT is an appropriate tool to use

for a synoptic assessment because it is easy to use, requires

no validation or calibration, and provides reasonable esti-

mates of NPS pollutant transport. In contrast, to apply

another watershed model to an area, the size of the

southeastern Coastal Plain ecoregion would require months

of work and many resources.

Conclusions

The aim of this work was to apply a recently developed

model for prioritizing watersheds within which agricultural

BMPs can be implemented to reduce erosion to the ecoregion

scale. The model considers both biophysical and socioeco-

nomic factors which affect the implementation of agricul-

tural BMPs and ranks candidate watersheds within an

ecoregion or river basin. The prioritization model is not a

process-based simulation tool, so the rankings only indicate

which watersheds may provide the most cost-effective water

quality response to the implementation of a suite of BMPs

best suited to control erosion. As a final product, it does not

rank the watershed with the worst water quality although that

can be an intermediate product. Furthermore, the model does

not evaluate the water quality effect of the BMPs, and it is

incumbent on the model’s users to select the BMPs most

suitable for the area under consideration. The model was

developed as a tool for prioritizing BMP implementation

efforts in the watersheds of ecoregions associated with

CEAP watersheds. This work is the first application of the

model to one of these ecoregions.

The synoptic approach is based on using readily available

data and the best professional judgment which is occasionally

used in the absence of data or to supplement data. To reduce

ambiguity in our application of the prioritization model, we

selected only descriptors and indicators which are well sup-

ported in the literature and by JSEM and for which data were

readily available. The reliability of the model’s results could

be enhanced by identifying and using better populated and

vetted regionwide datasets for our measurement endpoints.

The reliability of the model’s results could also be enhanced

by defining the weighting factors associated with indicators

such as PG, ENVG, UP, EL, and PREA through surveys of

relevant professionals, managers, and other stakeholders.

This shows again that the prioritization model is largely dri-

ven by socioeconomic factors, and this differs greatly from

the traditional process-based biophysical approaches taken to

identify watersheds for BMP implementation.

Acknowledgments Funding for this project was provided by a grant

from the USDA-CSREES Integrated Research, Education, and

Extension Competitive Grants Program—National Integrated Water

Quality Program, Conservation Effects Assessment Project (CEAP)

(Award No. 2007-51130-03992). This research was also supported by

the Basic Science Research Program through the National Research

Foundation of Korea (NRF) funded by the Ministry of Science, ICT &

Future Planning [NRF-2013R1A1A1057929].

References

Abbruzzese B, Leibowitz SG (1997) A synoptic approach for assessing

cumulative impacts to wetlands. Environ Manag 21:457–475

Arabi M, Govindaraju RS, Hantush MM (2006) Cost-effective

allocation of watershed management practices using a genetic

algorithm. Water Resour Res 42:W10429

Fig. 6 Box and whiskers plot of

indicators and major terms used

in the prioritization model. The

circles show outliers (1.5 times

or more outside the interquartile

range above the upper quartile

and bellow the lower quartile).

PG represents PGj/PGmax,

ENVG represents ENVGj/

ENVGmax, UP represents UPj/

UPmax, EL represents ELj/

ELmax, PREA represents

PREAj/PREAmax, CC represents

1/CC, NRCA represents

(1 - NRCAj)/(1 - NRCAmin),

AGUR represents

(1 - AGURj)/(1 - AGURmin),

and SWCD represents

(1 - SWCDj)/(1 - SWCDmax)


123

Babcock BA, Laksminarayan PG, Wu J, Zilberman D (1996) The

economics of a public fund for environmental amenities: a study

of CRP contracts. Am J Agric Econ 78:961–971

Bailey RG (1983) Delineation of ecosystem regions. Environ Manag

7:365–373

Bekele EG, Nicklow JW (2005) Multiobjective management of

ecosystem services by integrative watershed modeling and

evolutionary algorithms. Water Resour Res 41:W10406

Boll J, Brooks E, Easton Z, Steenhuis T, Wulfhurst JD, Vellidis G,

Kurkalova L, Jang TI (2011) CEAP synthesis: analysis and tools

for selection of the optimal suite of conservation practices. In:

2011 land and sea grant water conference. Washington, DC

Brooks E, Boll J (2011) Building process-based understanding for

improved adaptation and management. In: International sympo-

sium erosion & landscape evolution, ISELE paper no. 11077,

Alaska

Brooks ES, Saia SM, Boll J, Wetzel L, Easton Z, Steenhuis TS (2013)

Hydrologic characterization tool: development and applications

of a simple pollutant transport decision support tool. J Am Water

Resour Assoc (under review)

Cho J, Vellidis G, Bosch DD, Lowrance R, Strickland T (2010) Water

quality effects of simulated conservation practice scenarios in

the Little River Experimental Watershed. J Soil Water Conserv

45:463–473

Feng H, Kurkalova LA, Kling CL, Gassman PW (2006) Environ-

mental conservation in agriculture: land retirement vs. changing

practices on working land. J Environ Econ Manag 52:600–614

Game ET, Kareiva P, Possingham HP (2013) Six common mistakes in

conservation priority setting. Conserv Biol 27:480–485

Genskow K, Prokopy LS (2009) Lessons learned in developing social

indicators for regional water quality management. J Soc Nat

Resour 23:83–91

Hopkins LD (1977) Methods for generating land suitability maps: a

comparative evaluation. J Am Inst Plan 43:386–400

Hyman JB, Leibowitz SG (2000) A general framework for prioritiz-

ing land units for ecological protection and restoration. Environ

Manag 25:23–35

Jang T, Vellidis G, Hyman JB, Brooks E, Kurkalova LA, Boll J, Cho

JP (2013) A prioritizing model for sediment load reduction.

Environ Manag 51:209–224

Jenks GF (1967) The data model concept in statistical mapping. Int

Yearb Cartogr 7:186–190

Khare D, Singh KS, Dhore KA, Bhatnagar T (2007) Watershed

prioritization conservation soil erosion status: a case study of

India. In: McFarland, Saleh A, San Antonio (eds) Watershed

management to meet water quality standards and TMDLS

procedure of the 10–14 March 2007, ASABE publication no.

701P0207, Texas

Leibowitz SG, Hyman JB (1999) Use of scale invariance in evaluating

judgment indicators. Environ Monit Assess 58:283–303

Limbrunner J, Vogel R, Chapra S, Kirshen P (2013a) Classic

optimization techniques applied to stormwater and nonpoint

source pollution management at the watershed scale. J Water

Resour Plan Manag 139:486–491

Limbrunner J, Vogel R, Chapra S, Kirshen P (2013b) Optimal

location of sediment-trapping best management practices for

nonpoint source load management. J Water Resour Plan Manag

139:478–485

Lowrance R, Vellidis G (1995) A conceptual model for assessing

ecological risk to water quality function of Bottomland Hard-

wood forests. Environ Manag 19:239–258

Machado EA, Stoms DM, Davis FW, Kreitler J (2006) Prioritizing

farmland preservation cost-effectively for multiple objectives.

J Soil Water Conserv 61:250–258

Maringanti C, Chaubey I, Popp J (2009) Development of a

multiobjective optimization tool for the selection and placement

of best management practices for nonpoint source pollution

control. Water Resour Res 45:W06406

McMahon G, Gregonis SM, Waltman SW, Omernik JM, Thorson TD,

Freeouf JA, Rorick AH, Keys JE (2001) Developing a spatial

framework of common Ecological Regions for the Conterminous

United States. Environ Manag 28:293–316

Maxted J, Diebel M, Vander Zanden M (2009) Landscape planning

for agricultural non-point source pollution reduction II, balanc-

ing watershed size, number of watersheds, and implementation

effort. Environ Manag 43:60–68

Meals D, Vellidis G, Cho J, Crow S, Hawkins G, Mullen J, Bosch D,

Sullivan D, Lowrance R, Wall A, Luloff A, Hoag D, Arabi M,

Jennings G, Osmond D (2012) Little River Experimental

Watershed, Georgia: National Institute of Food and Agricul-

ture-Conservation Effects Assessment Project Watershed Pro-

ject. In: Osmond D, Meals D, Hoag D, Arabi M (eds) How to

Build Better Agricultural Conservation Programs to Protect

Water Quality: The NIFA-CEAP Experience. Soil and Water

Conservation Society, Ankeny

Nigel R, Rughooputh SD (2010) Soil erosion risk mapping with new

datasets: an improved identification and prioritisation of high

erosion risk areas. Catena 82:191–205

Norton DJ, Wickham JD, Wade TG, Kunert K, Thomas JV, Zeph P

(2009) A method for comparative analysis of recovery potential in

impaired waters restoration planning. Environ Manag 44:356–368

O’Banion K (1980) Use of value functions in environmental

decisions. Environ Manag 4:3–6

Osmond D (2010) USDA water quality projects and the National

Institute of Food and Agriculture Conservation Effects Assess-

ment Project watershed studies. J Soil Water Conserv 65:142A–

146A

Pandey A, Chowdary VM, Mal BC, Billib M (2009) Application of

the WEPP model for prioritization and evaluation of best

management practices in an Indian watershed. Hydrol Process

23:2997–3005

Prokopy LS (2011) Agricultural human dimensions research: the role

of qualitative research methods. J Soil Water Conserv 66:9A–

12A

Reimer AP, Thompson AW, Prokopy LS (2012) The multi-dimen-

sional nature of environmental attitudes among farmers in

Indiana: implications for conservation adoption. Agric Human

Values 29:29–40

Rodriguez HG, Popp J, Maringanti C, Chaubey I (2011) Selection and

placement of best management practices used to reduce water

quality degradation in Lincoln Lake watershed. Water Resour

Res 47:W01507

Sandoval-Solis S, McKinney D, Loucks D (2011) Sustainability index

for water resources planning and management. J. Water Resour

Plan Manag 137:381–390

Schweiger EW, Leibowitz SG, Hyman JB, Foster WE, Downing MC

(2002) Synoptic assessment of wetland function: a planning tool

for protection of wetland species and biodiversity. Biodivers

Conserv 11:379–406

Secchi S, Kling CL, Feng H, Gassman PW, Jha M, Campbell T,

Kurkalova LA (2007) The cost of cleaner water: assessing

agricultural pollution reduction at the watershed scale. J Soil

Water Conserv 62:10–20

Shen Z, Hong Q, Chu Z, Gong Y (2011) A framework for priority

non-point source area identification and load estimation inte-

grated with APPI and PLOAD model in Fujiang Watershed,

China. Agric Water Manag 98:977–989

Skutch MM, Flowerdew RTN (1976) Measurement techniques in

environmental impact assessment. Environ Conserv 3:209–217

Smith PGR, Theberge JB (1987) Evaluating natural areas using

multiple criteria: theory and practice. Environ Manag 11:

447–460


123

US Environmental Protection Agency (USEPA) (1996) Nonpoint

sources pollution: the nation’s largest water quality problem. US

US Environmental Protection Agency, Office of Water EPA841-

F96-004A

US Environmental Protection Agency (USEPA) (2005) Contaminated

sediment remediation guidance for hazardous waste sites. Report

EPA-540-R-05-012. US Environmental Protection Agency,

Office of Solid Waste and Emergency Response, Washington,

DC

US Environmental Protection Agency (USEPA) (2012) Social context

indicator: recovery potential screening. http://water.epa.gov/

lawsregs/lawsguidance/cwa/tmdl/recovery/indicatorssocial.cfm.

Accessed 14 Aug 2012

Vellidis G, Leibowitz SG, Ainslie WB, Pruitt BA (2003) Prioritizing

wetland restoration for sediment yield reduction: a conceptual

model. Environ Manag 31:301–312

Zhang Z, Wu B, Ling F, Zeng Y, Yan N, Yuan C (2010) Identification

of priority areas for controlling soil erosion. Catena 83:76–86


123

http://water.epa.gov/lawsregs/lawsguidance/cwa/tmdl/recovery/indicatorssocial.cfm

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