1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
Supporting Information
Potential Impacts of Clean Air Act Regulations on Nitrogen Fate and Transport in the
Neuse River Basin: A Modeling Investigation using CMAQ and SWAT
Mark Gabriel1*, Chris Knightes1, Robin Dennis2, Ellen Cooter2
(1) USEPA/Office of Research and Development(ORD)/National Exposure Research Laboratory
(NERL)/Ecosystem Research Division (ERD), 960 College Station Rd., Athens, GA, 30605,
USA
(2) USEPA/ORD/NERL/Atmospheric Modeling and Analysis Division (AMAD), 109 T W
Alexander Drive, Research Triangle Park, NC, 27711, USA
*Corresponding Author- email: gabriel.mark@epa.gov, phone: 706-355-8349, fax: 706-3558326
18
19
20
21
22
23
24
25
26
27
28
29
30
31
S1
32
SWAT Model Parameterization, Calibration and Validation
33
34
Following SWAT data entry, we performed parameter sensitivity analysis. The most
35
sensitive model parameters for flow and nutrient loading (e.g., runoff curve number, soil water
36
evaporation and nitrogen percolation constants, etc.) were identified with an automated
37
sensitivity analysis procedure in SWAT. This sensitivity analysis was performed for the sub-
38
watersheds that contained the outlet for the entire watershed. Latin Hypercube (LH) sampling
39
and the one-factor-at-a-time (OAT) method was applied [1] to determine parameter sensitivity.
40
Following simulation, the overall effect of each parameter was ranked. The top 10 th percentile of
41
all parameters for both categories (flow, nutrients) was used for the next step, calibration and
42
validation. Model calibration was performed using SUFI-2 (Sequential Uncertainty Fitting
43
version 2) within the SWAT-CUP program (Calibration and Uncertainty Program, version 2.1.3)
44
[2]. SUFI-2 is an automated calibration procedure that accounts for sources of uncertainty
45
associated with driving variables, the conceptual model, parameters and measured data.
46
Representative combinations of parameters were obtained through LH sampling [3]. For a full
47
description of the SWAT-CUP and SUFI-2 programs, refer to Abbaspour (2009) [2]. National
48
Climatic Data Center (NCDC, http://www.ncdc.noaa.gov/oa/ncdc.html: Accessed 1/18/2013)
49
precipitation and temperature data was used in model calibration. Wet deposited nitrogen data
50
used for calibration was obtained from National Atmospheric Deposition Program (NADP,
51
http://nadp.sws.uiuc.edu/: Accessed 1/18/2013) and dry deposited nitrogen data from CASTNet
52
(http://epa.gov/castnet/javaweb/index.html: Accessed 1/18/2013) model estimates. Calibrations
53
were first performed for average monthly flow and then for monthly nitrate loading (kg). For all
54
cases, the Nash-Sutcliffe Efficiency test (NS) was chosen as the calibration objective function
S2
55
because of the large literature base interpreting values for the NS function from a hydrologic
56
modeling perspective. NS values ≥ 0.5 are considered satisfactory [4-7] although marginal
57
simulations may have NS values as low as 0.36 [8]. Two supporting measures used to evaluate
58
model calibration and validation results were the regression coefficient (r2) and percent bias
59
(PBIAS). The following periods were used for nitrate and flow calibration and validation for
60
both watersheds: 2001-2006 (calibration) and 2007-2009 (validation). SWAT was run on a daily
61
time step and results are displayed annually. All SWAT calibration and validation runs contained
62
a three-year spin up (1998 to 2001). Table S1 shows calibration and validation results for both
63
watersheds. As is commonly observed, results for nitrogen calibration are weaker than flow [5,
64
9-12], which is likely due to several inter-related issues stemming from uncertainties or
65
inaccuracies in observed data to misrepresentation of actual agricultural/landuse practices. Errors
66
can also occur with imperfect weather data and NLCD inaccuracies. When calibrating on a load
67
basis, uncertainties associated with flow will in some capacity cascade into load calibration. The
68
discrepancy between the observed and simulated data for nitrate is also likely an artifact of
69
observed data development since a large portion of the observed data was developed from
70
statistical regression [13-14]. The bias percentages are within the acceptable range of ±75% for
71
nutrient calibration [11]. Using the SWAT-Check program we confirmed there were no mass
72
balance issues for flow, nitrogen and plant growth.
73
74
Description of SWAT Model Nitrogen Transport
75
76
SWAT simulates nitrogen cycles in the soil profile and groundwater [15]. Within both
77
media, nitrogen is highly reactive and exists in many forms. Nitrogen may be added to soil
S3
78
through fertilizer and manure application, atmospheric deposition (dry and wet) and biological
79
fixation [16]. Nitrogen may be removed from soil through plant uptake, soil erosion, leaching,
80
volatilization and denitrification [16]. In SWAT, nitrogen is divided into organic and inorganic
81
forms. There are three organic forms (fresh, active, and stable). Fresh organic nitrogen is
82
associated with crop residue and microbial biomass, and the active and stable organic nitrogen
83
pools are associated with the soil humus. The fresh component is much more bioavailable than
84
humus. Organic nitrogen associated with humus is partitioned into active and stable pools to
85
account for variations in availability of humic substances to mineralization. Humus is a complex
86
mixture of organic substances that have been significantly modified from their original form over
87
time, and also contains other substances that have been synthesized by soil organisms [16].
88
Inorganic nitrogen may be transported with runoff, enter lateral flow or percolate through soil.
89
Inorganic nitrogen entering shallow groundwater in recharge from soil percolation may remain in
90
groundwater, move into the main river or stream channel, the soil zone in response to water
91
deficiencies or move from the shallow soils to deep groundwater. For low-lying areas,
92
groundwater flow is the primary transport pathway and plays an important role in the delivery of
93
inorganic nitrogen to the main channel or to the soil zone [16].
94
95
96
97
98
99
S4
100
Table S1: Calibration and validation results for flow (m3/s, monthly average) and nitrate load
101
(kg/month)
102
Little River
Calibration
Validation
†
PBIAS
PBIAS†
NS*
r2
NS*
r2
(%)
%
Nahunta
Calibration
Validation
†
PBIAS
PBIAS†
NS*
r2
NS*
r2
%
%
Flow
0.77
0.77
-11.5
0.70
0.72
18.49
0.79
0.80
-11.3
0.40
0.56
7.78
Nitrate
load
0.24
0.35
-35.3
0.47
0.54
-16.8
0.15
0.32
21.6
0.03
0.26
-26.0
103
*Nash-Sutcliffe Efficiency test
104
†Percent bias
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
S5
135
References:
136
(1)
Van Griensven, A., Meixner, T., Grunwald, S., Bishop, T., Di Luzio, A., Srinivasan, R.
137
A. (2006). global sensitivity analysis tool for the parameters of multivariable watershed
138
models. Journal of Hydrology, 324, 10-23.
139
140
(2)
141
Abbaspour, K.C. (2009). SWAT-CUP2. SWAT Calibration and Uncertainty Programs,
Version 2.
142
143
(3)
Abbaspour, K.C., Yang, J. Maximov, I., Siber, R., Bogner, K., Mieleitner, J., Zobrist, J.,
144
Srinivasn, R. (2007). Modeling hydrology and water quantity in the pre-alpine/alpine
145
Thur watershed using SWAT. Journal of Hydrology, 333, 413-430.
146
147
(4)
Wang, X., Shang, S. Yang, W. Clary, C. Yang, D. (2010). Simulation of land use-soil
148
interactive effects on water and sediment yields at watershed scale. Ecological
149
Engineering, 36, 328-344.
150
151
(5)
152
Shilling, K.E., Wolter, C.F. (2009). Modeling nitrate-nitrogen load reduction strategies
for the Des Moines River, Iowa using SWAT. Environmental Management, 44, 671-682.
153
154
155
(6)
Setegn, S.G., Srinivasan, R., Dargahi, B. (2008). Hydrological modeling in the Lake tana
Basin, Ethiopia using SWAT Model. The Open Hydrology Journal, 2, 49-62.
156
S6
157
(7)
Lam, Q.D., Schmalz, B., Fohrer, N. (2010). Modeling point and diffuse source pollution
158
of nitrate in a rural lowland catchment using the SWAT model. Agricultural Water
159
Management, 97, 317-325.
160
161
(8)
Tobin, K.J., Bennett, M.E. (2009). Using SWAT to model streamflow in two river basins
162
with ground and satellite precipitation data. Journal of the American Water Resources
163
Association, 45, 253-271.
164
165
(9)
Douglas-Mankin, K.R, Srinivasan, R., Arnold, J.G. (2010). Soil and Water Assessment
166
Tool (SWAT) Model: Current developments and applications. American Society of
167
Agricultural and Biological Engineers, 53, 1423-1431.
168
169
(10)
Bosch, N. (2008). The influence of impoundments on riverine nutrient transport: An
170
evaluation using the Soil and Water Assessment Tool. Journal of Hydrology, 355, 131-
171
147.
172
173
(11)
Moriasi, D. N., Arnold, J.G., Van Liew, M.W., Binger, R.L., Harmel, R.D., Veith, T.L.
174
(2007). Model evaluation guidelines for systematic quantification of accuracy in
175
watershed simulations. American Society of Agricultural and Biological Engineers, 50,
176
885-900.
177
S7
178
(12)
Bosch, N.S., Allan J.D., Dolan, D.M., Han, H., Richards, R.P. (2011). Applciation of the
179
Soil and Water Assessment Tool for six watersheds of Lake Erie: Model parameterization
180
and calibration. Journal of Great Lakes Research, 37, 263-271.
181
182
(13)
Richards, R.P. (1998). Estimation of Pollutant Loads in Rivers and Streams: A Guidance
183
Document for NPS Programs. U.S. Environmental Protection Agency, Denver, CO, 108
184
pp.
185
186
(14)
Gabriel, M.C., Knightes, C., Cooter, E., Dennis, R. (2014). Impact of different
187
meteorology data sets on nitrogen fate and transport in the SWAT watershed model.
188
Environmental Modeling and Assessment, DOI: 10.1007/s10666-014-9400-z
189
190
(15)
191
Neitsch, S.L., Arnold, J.G., Kiniry, J.R., Williams., J.R. (2005). Soil and Water
Assessment Tool Theoretical Documentation.
192
193
(16)
Lam, Q.D., Schmalz, B., Fohrer, N. (2011). The impact of agricultural Best Management
194
practices on water quality in a North German lowland catchment. Environmental
195
Monitoring and Assessment, 183, 351-379.
196
197
198
199
200
201
202
203
204
205
S8