Pesticides in Surface Water: Monitoring, Modeling, Risk Assessment, and Management Downloaded from pubs.acs.org by BETHEL UNIV on 04/01/19. For personal use only.
Chapter 20
Modeling Pesticide Fate and Transport at Watershed Scale Using the Soil & Water Assessment Tool: General Applications and Mitigation Strategies Ruoyu Wang,1 Huajin Chen,1 Yuzhou Luo,2 Haw Yen,3 Jeffrey George Arnold,3 David Bubenheim,4 Patrick Moran,5 and Minghua Zhang*,1 1Department
of Land, Air and Water Resources, University of California, Davis, California 95616, United States 2Department of Pesticide Regulation, California Environmental Protection Agency, Sacramento, California 95812, United States 3Blackland Research and Extension Center, Texas A&M University, Temple, Texas 76502, United States 4NASA Ames Research Center, Moffett Field, California 94035, United States 5USDA-ARS, Invasive Species and Pollinator Health Research Unit, Albany, California 94710, United States *E-mail:
[email protected].
Pesticide residue runoff to surface water is of great concern to local stakeholders who seek to preserve or achieve good water quality. The Soil & Water Assessment Tool (SWAT) has been widely used to assess many environmental problems related to water resources and nonpoint-source pollution, including sediment and nutrient loads and pesticides. However, a comprehensive review of existing studies on SWAT-based pesticide modeling is still not available in the literature. Therefore, in this chapter, we present an overview summarizing all previous SWAT applications in studying the fate and transport of pesticides around the world, based on the peer-reviewed literature. We aim to (1) introduce the fundamental mechanisms for SWAT pesticide modeling, (2) summarize the required modeling inputs and associated © 2019 American Chemical Society
parameterization processes, (3) provide an overview of global applications and evaluate model performance, (4) discuss SWAT representations of mitigation strategies to alleviate potential pollution, and finally, yet importantly; 5) recommend future needs to address current modeling limitations to fill research gaps.
1. Introduction Pesticide use has enabled a tremendous increase in crop yields and food production over the past century (1). At the same time, pesticide use has been recognized to cause water-quality problems and to negatively impact ecosystems and human health (2, 3). Modeling has been widely used by scientific communities and regulatory agencies to quantify the exposure risks of pesticides to surface water (4). For example, Ippolito et al. (5) applied a spatially explicit model to estimate the global distribution of agricultural insecticides in surface waters. The U.S. Environmental Protection Agency (U.S. EPA) Office of Pesticide Programs relies on mathematical models to estimate aquatic pesticide exposures (6). A national study performed by the U.S. Geological Survey (USGS) (7) analyzed 123 pesticide compounds in streams and rivers at 125 sites across the conterminous United States from 2002 to 2011. The study showed that 61% of assessed agricultural streams, 46% of mixed-land-use streams, and 90% of urban streams contained one or more pesticides in amounts that exceeded the U.S. EPA chronic aquatic life benchmarks. A regional study performed by the California Department of Pesticide Regulation (CDPR) (8) analyzed pesticide detections in California surface waters from 1991 to 2010. Organophosphate pesticides including diazinon, chlorpyrifos, and malathion were among the active pesticide ingredients that most frequently exceeded the U.S. EPA aquatic life benchmarks. The exceedance frequencies were about 11% of 12,000 samples for diazinon, 7% of 11,000 samples for chlorpyrifos, and 3% of 10,000 samples for malathion. Yearly exceedance frequencies were similar among the three compounds, ranging from less than 1% to 26%. In addition, agricultural organophosphate runoff has led to listings of water bodies as impaired and restrictions on total maximum daily loads (TMDL) in many watersheds of central California (9), so that mitigation measures are needed to reach water-quality protection goals (3). To prevent and mitigate the negative impacts of pesticide use, researchers and government regulators need to obtain a quantitative understanding of pesticide exposure in surface water, to assess the potential risk of pesticide use to human health and aquatic ecosystems. Modeling and monitoring are both important tools for assessing aquatic pesticide exposures. Numerical models have been increasingly used to estimate pesticide concentrations in surface water. This is mainly because models are much more cost-effective than monitoring studies and can be used to generate exposure estimates across a wide range of scenarios (4). Recent advances in geographic information system technology and growth in computing power have facilitated the development of spatially distributed, watershed-scale models that are capable of predicting contaminant exposures 392
over complex landscapes (10). Watershed models are useful tools for supporting decision-making processes such as the development of a TMDL plan and the implementation of best management practices (BMPs) in high-risk areas (11). Over the past few decades, various watershed models have been developed for spatially explicit predictions of hydrological processes and associated pesticide transport (12). Borah and Bera (13) compared 11 watershed models including the Annualized Agricultural Non-Point Source (AnnAGNPS) (14), Hydrological Simulation Program—Fortran (HSPF) (15), European Hydrological System (MIKE SHE) (16), and Soil & Water Assessment Tool (SWAT) (17, 18). AnnAGNPS, HSPF, and SWAT are continuous simulation models that can be applied to assess the long-term effects of hydrological changes and management practices, whereas MIKE SHE has both long-term and single-event simulation capabilities. Borah and Bera (13) concluded that AnnAGNPS, HSPF, MIKE SHE, and SWAT include all of the major components (hydrology, sediments, and chemical) applicable to watersheds. However, they considered MIKE SHE too complex to be applied in an efficient way to large watersheds. SWAT was recognized as promising for simulating intensively cultivated watersheds, and HSPF was considered more suitable for mixed agricultural and urban watersheds. Borah and Bera (13) also compared HSPF and SWAT based on their applications found in the literature (19). SWAT and HSPF were found to be suitable for yearly simulations of flow, sediment and nutrient loads, good for monthly simulations except for months having extreme storm events, but relatively poor for daily simulations of extreme flow events. Quilbe et al. (20) developed a framework to facilitate model selection based on a multicriteria analysis. They assessed 36 watershed models using five classes of selection criteria: modeling characteristics, output variables, model applicability, possibilities for simulating BMPs, and ease of use. Among the watershed models evaluated, SWAT was identified as the best-suited model for simulating the effect of BMPs on reducing pesticide exposure at the watershed scale. The SWAT model was developed to predict water, sediment, and agricultural chemical yields under varying soil, land-use, and management conditions (21). It has been used extensively in the United States and around the world. The ability of SWAT to simulate streamflow, sediment, and nutrients on an annual or monthly basis has been demonstrated (21–23). There is a rapidly growing body of literature using SWAT to model watershed-scale pesticide fate and transport (24, 25). However, a comprehensive review of existing studies on SWAT modeling of pesticide exposure is currently lacking in the literature. Therefore, the goal of this study is to review the mechanisms and applications of SWAT modeling for pesticide fate and transport at the watershed scale. Specifically, the objectives of this chapter are (1) to present a general overview of the SWAT modeling mechanism for pesticide fate and transport, (2) to provide a brief description of model inputs and parameters required for SWAT pesticide simulation, (3) to summarize existing pesticide modeling studies using SWAT and evaluate model performance, (4) to discuss implementations of SWAT in supporting best management practice (BMP) scenarios and other extended uses, and (5) to discuss research gaps and future research needs regarding pesticide exposure modeling using SWAT. 393
2. Basic SWAT Modeling Mechanism for Pesticide The Soil & Water Assessment Tool (SWAT) (26) is a process-based, semi-distributed, continuous-time watershed model, which was developed by the U.S. Department of Agriculture’s (USDA’s) Agricultural Research Service (ARS) in the mid-1990s, and it has been under continuous development for the past 20 years. SWAT can be used to simulate various hydrologic and water-quality processes through a mixture of physical-based empirical relationships. The field-level fate simulation module of SWAT was adapted from the Groundwater Loading Effects of Agricultural Management Systems (GLEAMS) (27) model, whereas the in-stream transformation process was developed and adapted from the work of Chapra (28). 2.1. Fate Simulation at the Field Scale Pesticide fate at the field scale is mainly simulated in the crop foliage and soil environments. The basic simulation units of SWAT are agricultural hydrological response units (HRUs), which are each identified by a unique combination of land use, soil, and slope. Pesticides applied in SWAT are partly intercepted by crop foliage and partly reach the soil surface. The wash-off component from foliage to soil can also be simulated during rainfall and irrigation events. The degradation rates of pesticides on crop foliage and soil are controlled by first-order kinetics. SWAT uses a lumped half-life to represent the net effect of volatilization, photolysis, hydrolysis, and biolysis in both soil and foliage. Pesticides in the soil environment are further distributed between liquid and solid phases. If a pesticide is hydrophobic, then most of the mass is absorbed to soil particles; otherwise, hydrophilic pesticides are more easily dissolved in the soil water phase. The partitioning ratio between the liquid and solid phases is determined by the soil organic carbon content (orgC) and the soil adsorption coefficient (SKOC), which are user-specified for each pesticide. 2.2. Transport at the Basin Scale After field-level simulation, pesticides at the edge of the field are ready to be transported from any individual HRU to receiving water bodies. Pesticides in the soluble phase of the soil environment can be transported with soil water fluxes (surface runoff, lateral flow, or percolation). The change in the pesticide mass in the soil layers due to flow movement is determined by the runoff duration, the pesticide concentration, and the amount of mobile water. For both surface and underlying soil layers, mobile water includes lateral flow and percolation. For the surface soil layer, only surface runoff is considered as mobile water. For pesticide transport in subsurface flow, the current SWAT model is limited to the lateral flow (interflow) from the root zone, which means that the model does not track pesticide movement in the shallow groundwater contributing to baseflow. Pesticides attached to soil particles can also be transported by surface runoff, which is related to erosion processes. The amount of pesticide transported with sediment is dictated by the pesticide concentration in the top 10 mm of soil; the 394
sediment yield due to soil erosion; and an enrichment ratio that indicates the enrichment of clay-size particles, which are more easily picked up by surface runoff and contain more sorbed phase of the pesticide. The “time of concentration” is the time needed for water to be transported from the most remote point in a watershed to the pour point. For large basins with a time of concentration greater than 1 day, only a portion of the generated surface runoff/lateral flow/sediment will reach the main channel. Pesticides that are carried out by surface/subsurface flow or sediment follow a “lag” when transported from agricultural HRUs to the main channel. When pesticides are transported from upstream to downstream through channel segments, there are other physiochemical processes involved, including degradation, volatilization, settling, resuspension, diffusion, and burial. The in-stream pesticide processes can be simulated by SWAT using a simple mass balance equation, assuming a uniformly mixed water layer over a river sediment layer.
3. Model Inputs and Parameters for Pesticide Simulation 3.1. SWAT Inputs for Pesticide Modeling For flow, sediment, and nutrient simulations, SWAT requires topographic inputs for watershed delineation, landuse and soil information for generating basic simulation units at the field scale, and daily or subdaily climate inputs (precipitation, air temperature, solar radiation, wind speed, relative humidity) to drive the model for fundamental water balance computations. For nutrient loading calculations, fertilization and crop information are necessary. Actually, fertilizer and crop information also impact the water balance and sediment (erosion) processes, and many other additional factors in addition to fertilizer and crop information (e.g., crop stresses) impact nutrient loadings, making the modeling system quite complicated. For pesticide fate and transport simulations, SWAT requires additional inputs, including pesticide physiochemical properties, application date, and application rate and associated water-management information (Table 1). Compared to other inputs (topographic, land-use, soil, and climate data), pesticide application information usually cannot be easily obtained and varies between crop fields (29). However, the accurate prediction of pesticide loading is highly dependent on the correct estimation of pesticide application rate and timing (30, 31). Currently, the U.S. pesticide usage database (32) is available only at the county spatial level, and application amounts are summed to annual totals, which prevents the estimation of intraanual application times and rates, especially for pesticides applied multiple times in each year. Modelers usually either make assumptions based on state-level data (33) or derive pesticide application schedules from local farmer surveys (30). For example, Larose et al. (34) estimated the atrazine losses to surface water in the Cedar Creek watershed in the midwestern United States. Their application times for SWAT modeling were estimated from National Agricultural Statistics Service (NASS) seasonal progress data for crop development in northeastern Indiana and farm activities 395
provided by local soil and water conservation districts. Inevitably, the rough estimation of pesticide application times and rates will introduce uncertainties into predictions of pesticide runoff by modeling approaches (23), especially if multiple precipitation events close to the application time(s) are not well incorporated into the model (35, 36). More spatially and temporally detailed pesticide application information is available only at the regional scale, for example, in California (37). The Pesticide Use Reporting (PUR) database (38) is a comprehensive pesticide usage database that is managed by the California Department of Pesticide Regulation (CDPR). This database contains records of California agricultural pesticide applications since the early 1990s at the levels of Public Land Survey System sections and field borders, with roughly 1 mile by 1 mile resolution. Many modeling studies have benefitted from the detailed application information available in the California Pesticide Use Reporting database (24, 25, 39–41).
Table 1. SWAT Inputs for Pesticide Runoff Simulations SWAT inputs
Type
Purpose
Digital elevation model
Topographic information
Watershed delineation
Landuse and soil information
SWAT basic simulation unit HRU generation
Daily or subdaily climate information
Water balance computations
Crop-management information
Sediment and nutrient computations
Hydrograph Land use/cover maps Soil data Precipitation Air temperature Solar radiation Wind speed Relative humidity Fertilization Water management (irrigation, drainage)
Pesticide fate and transport computations
Pesticide application rate and date Pesticide properties
3.2. SWAT Pesticide Modeling Parameters and Sensitivity Analysis Ecohydrological models employ a large number of physically based or empirical equations to represent and simplify natural processes. These equations are characterized by parameters and, in turn, affect modeled behavior. Some parameters are used in empirical equations, which have no physical meanings, introducing difficulties to a priori estimations (42). Other parameters have 396
physical meanings and could be obtained from laboratory experiments or field studies. However, some of those parameters have extreme heterogeneity, which limits the accuracy of their measurement, especially for an entire watershed (43). Understanding key parameters controlling water-quality processes is critical for modeling pesticide fate and transport. The most commonly used parameters for SWAT pesticide modeling are summarized in Table 2. Sensitivity analysis allows users to capture the most important processes in determining pesticide fate and, therefore, to identify the most “sensitive” parameters that drive SWAT simulations. Among current published SWAT pesticide modeling studies, the most sensitive parameters are usually SKOC, HLIFE_S, PERCOP, and CHPST_KOC (see Table 2 for definitions) (24, 31, 44–46). However, for specific pesticides, the most sensitive parameters may vary case by case. For example, SWAT was used to simulate three pesticides (atrazine, chlorothalonil, and endosulfan) in the Mae Sa catchment in northern Thailand (47). SKOC, which mainly affects sorption behavior, was identified as a more sensitive parameter for two of the pesticides (atrazine and chlorothalonil) but not for the third (endosulfan). On the other hand, HLIFE_S was a sensitive parameter when simulating chlorothalonil and endosulfan but not atrazine. It must be emphasized that Table 2 lists only parameters directly related to pesticide fate and transport. In fact, many parameters controlling hydrological or erosion processes also have indirect impacts on pesticides. For example, Holvoet et al. (31) used the LH-OAT method (48) to study the sensitivity of SWAT parameters on pesticide loadings in the Nil catchment, Belgium. Hydrologic parameters were found to be “dominant in controlling pesticide prediction” (31). They also modified the original SWAT code and included point-source contamination due to drift and other processes. The parameter apfp_pest was also found to be sensitive for driving the direct losses of atrazine to the river system. A management-oriented sensitivity analysis was conducted in Orestimba Creek watershed, central California, to examine the yield and transport of diazinon and chlorpyrifos (49). Compared to commonly used global sensitivity analysis, management-oriented sensitivity analysis is conducted based on local variables and parameters that directly impacted the effectiveness of pesticide-management practices. Therefore, it can provide more meaningful information and is thus more helpful in evaluating local water-quality and water-management practices.
Table 2. SWAT Parameters Controlling Pesticide Fate and Transport Parameter
Meaning
File
SKOC
Organic-carbon-normalized partition coefficient (L/kg)
pest.dat
WOF
Wash-off fraction
pest.dat
WSOL
Water solubility
pest.dat
HLIFE_F
Half-life on foliage (day)
pest.dat
Process
Pesticide fate
Continued on next page.
397
Table 2. (Continued). SWAT Parameters Controlling Pesticide Fate and Transport
a
Parameter
Meaning
File
HLIFE_S
Half-life in soil (day)
pest.dat
AP_EF
Application efficiency
pest.dat
PERCOP
Pesticide percolation coefficient
.bsn
PSTENR
Enrichment ratio for pesticide in the soil
.chm
PLTPST
Initial pesticide amount on foliage
.chm
SOLPST
Initial pesticide amount in soil
.chm
CHPST_CONC
Initial pesticide concentration in reacha (mg/m3)
.swq
CHPST_KOC
Pesticide partition coefficient between water and sediment in reacha (m3/g)
.swq
CHPST_REA
Pesticide reaction coefficient in reacha (day–1)
.swq
CHPST_VOL
Pesticide volatilization coefficient in reacha (m/day)
.swq
CHPST_STL
Settling velocity for pesticide sorbed to sediment (m/day)
.swq
CHPST_RSP
Resuspension velocity for pesticide sorbed to sediment (m/day)
.swq
CHPST_MIX
Mixing velocity (diffusion, dispersion) for pesticide in reacha (m/day)
.swq
SEDPST_CONC
Initial pesticide concentration in reacha bed sediment (mg/m3)
.swq
SEDPST_REA
Pesticide reaction coefficient in reacha bed sediment (day–1)
.swq
SEDPST_BRY
Pesticide burial velocity in reacha bed sediment (m/day)
.swq
SEDPST_ACT
Depth of active sediment layer for pesticide (m)
.swq
Process
Pesticide transport
Pesticide transport (in-stream process, flow)
Pesticide transport (in-stream process, sediment)
A reach is a length of a stream or river.
4. Global Applications SWAT has been widely applied around the world to study various environmental problems that are closely related to pesticide fate and transport, and these studies appear in about 50 peer-reviewed journal publications. Figure 1 displays a map of all SWAT pesticide modeling study sites in the SWAT 398
Literature Database (https://www.card.iastate.edu/swat_articles/add.aspx) as of August 2018. Although SWAT pesticide modeling studies have mainly been conducted in the United States, studies have also been peformed in four countries in Asia (China, Japan, Thailand, and Philippines) and four countries in Europe (United Kingdom, Belgium, Germany, and France). In the United States, there are two “hot-spot” SWAT pesticide modeling areas: one in the Central Valley of California and the other in the US Corn Belt. Most California SWAT pesticide implementations (24, 25, 39, 40, 49–53) have been contributed by the AGIS laboratory at the University of California, Davis (http://agis.ucdavis.edu/, accessed October 8, 2018), whereas most Midwest pesticide modeling studies (34, 45, 54–57) were conducted by Purdue University and the National Soil Erosion Research Laboratory of the USDA-ARS (https://www.ars.usda.gov/midwestarea/west-lafayette-in/national-soil-erosion-research/, accessed October 8, 2018). In Europe, more than half of the published SWAT pesticide studies focused on the Nil catchment, central Belgium (31, 58–61), and were mainly contributed by the Flemish Institute for Technological Research (VITO), Antwerp, Belgium (https://vito.be/en, accessed October 8, 2018).
Figure 1. SWAT pesticide modeling study sites around the world. Table 3 lists all of the reported SWAT pesticide simulations and their study sites. Because most studies found SKOC and HLIFE_S to be the most important parameters for accurate pesticide modeling, we also included the values or ranges of these two parameters (when given) in Table 3, which provide reliable references for further studies, especially for cases in which the same pesticide of interest is to be simulated. According to Table 3, 29 pesticides have been simulated using SWAT to study their fate and transport with the results published in peer-reviewed journals. Atrazine is the most widely studied pesticide, with 14 published studies, followed by chlorpyrifos (4 studies), metolachlor (3 studies), diazinon (3 studies), chlorothalonil (2 studies), diuron (2 studies), malathion (2 studies), trifluralin (2 studies), and other pesticides (1 study). 399
Table 3. SWAT-Simulated Pesticides and Key Parameters Active ingredient
Type
Area(s) (reference)
SKOC (mL/kg)
HLIFE_S (days)
2,4-D
Herbicide
Dallas, TX; Columbia, MO; Minneapolis, MN, USA (62)
50
10
Atrazine
Herbicide
Auglaize watershed, OH; USA (29)
100
60
Isoxaflutole
Herbicide
102–227
0.5–0.6
RPA 202248
Herbicide
La Belle Lake watershed, MO, USA (63)
62–204
66–89
Atrazine
Herbicide
Walnut Creek watershed, IA, USA (64)
81
137–315
Bentazone
Herbicide
13–176
4–21
Cyanazine
Herbicide
Bedfordshire, U.K. (44)
116–500
12–15
Terbutryn
Herbicide
335–1070
7–358
Terbuthylazine
Herbicide
162–447
30–60
Fluometuron
Herbicide
Bogue Phalia basin, MS, USA (65)
242
85
Atrazine
Herbicide
Chloridazon
Herbicide
Nil catchment, Belgium (61)
Diuron
Herbicide
Linear alkylbenzene sulphonate
Anionic surfactant
Colworth, U.K. (66)
27.6
7–30
Atrazine
Herbicide
Cedar Creek watershed, IN, USA (34)
Atrazine
Herbicide
155.3
140
Metolachlor
Herbicide
Sugar Creek watershed, IN, USA (67)
2.2 × orgC
6.921
Trifluralin
Herbicide
7200
169
Atrazine
Herbicide
St. Joseph River watershed, IN, USA (55)
100
60
Atrazine
Herbicide
Salt River basin, MO, USA (68)
Diazinon
Insecticide
1000
40
Chlorpyrifos
Insecticide
Northern San Joaquin Valley watershed, CA, USA (24)
6070
30
Continued on next page.
400
Table 3. (Continued). SWAT-Simulated Pesticides and Key Parameters Active ingredient
Type
Area(s) (reference)
Atrazine
Herbicide
Route J watershed, MO, USA (69)
Chlorpyrifos
Insecticide
Diazinon
Insecticide
Metolachlor
Herbicide
Trifluralin
Herbicide
Atrazine
Herbicide
Wildcat Creek watershed, IN, USA (56)
Chlorpyrifos
Insecticide
Diazinon
Insecticide
Atrazine
Herbicide
Chlorothalonil
Fungicide
Endosulfan
Insecticide
Metolachlor
Herbicide
Aclonifen
Herbicide
Mefenacet
SKOC (mL/kg)
HLIFE_S (days)
Orestimba Creek, CA, USA (49)
1000
40
6070
30
Save River catchment, France (46)
667
90
13,196
60
Sacramento River watershed, CA, USA (40)
1000
40
6070
30
Mae Sa catchment, Thailand (47)
100
66
850
15.7
11,500
39
Save River catchment, France (36)
200
90
8203
90
Herbicide
Sakura River basin, Japan (70)
1099
use processexplicit degradation rate instead of lumped half life
Flufenacet
Herbicide
202–401
28–43
Metazachlor
Herbicide
Kielstau watershed, Germany (30)
53.8–220
3–21
Atrazine
Herbicide
Eagle Creek watershed, IN, USA (57)
Atrazine
Herbicide
Goodwater Creek Experimental Watershed, MO, USA (71)
Chlorothalonil
Fungicide
Cypermethrin
Insecticide
Mae Sa catchment, Thailand (72)
Diuron
Herbicide
480
90
San Joaquin watershed, CA, USA (25)
Continued on next page.
401
Table 3. (Continued). SWAT-Simulated Pesticides and Key Parameters Active ingredient
Type
Area(s) (reference)
SKOC (mL/kg)
HLIFE_S (days)
Malathion
Insecticide
Pagsanjan-Lumban basin, Philippines (73)
3594.52
6.26
Dimethoate
Insecticide
Delta region, CA, USA (74)
Atrazine
Herbicide
Northeast China (75)
100
60
Oxadiazon
Herbicide
3200
60
Isoprothiolane
Insecticide and fungicide
1352
20
Atrazine
Herbicide
MidwesternUnited States and Texas, USA (76)
171
61
Malathion
Insecticide
Mill Creek and Threemile Creek, OR, USA (77)
217
7.5
5. Performance Evaluation for Pesticide Modeling Model performances in capturing predicted time-series variables (e.g., flow discharge) are usually judged by performance measures and performance evaluation criteria that are calculated based on model simulations and ground measurements. Moriasi et al. recommended using the coefficient of determination (R2), Nash–Sutcliffe efficiency (NSE), index of agreement, root-mean-square error (RMSE), percent bias (PBIAS), and graphical performance as performance measures and provided corresponding performance evaluation criteria (78). For example, the SWAT model performance in monthly flow can be treated as satisfactory if R2 > 0.6, NSE > 0.5, and |PBIAS| < 15%. The SWAT performance for water quality has more flexible criteria than that for flow discharge, because of the increased complexity of the modeled processes and the decreased accuracy of the modeled variables for the former (79). The greater flexibility for water quality also stems from the uncertainty in the data measurements, especially because of the much lower frequency of water-quality measurements than flow-discharge measurements. Uncertainty in the water-quality output can also arise from the input data, model structure, and parameter estimation (80–85). Therefore, flow has the highest reliability in the model, whereas nutrient and pesticide loads have the lowest prediction reliability. Unlike flow, sediment, and nutrient simulations, there are no recommended performance evaluation criteria for SWAT pesticide simulations. This is mainly because of the (1) relatively scarcity of pesticide modeling studies, other than water-quality modeling studies, and (2) the lower reliability of capturing pesticide loadings than other water-quality variables (79). Actually, based on our 402
meta-analysis, the reported performance of SWAT pesticide simulations exhibits extensive variability among different studies (Table 4).
Table 4. Summary of SWAT Pesticide Modeling Performances Study
Temporal scale
Performance measures
Performance evaluation criteria
Kim et al., 2004 (29)
Monthly
Graphical comparison (atrazine)
Ramanarayanan et al., 2005 (63)
Daily
Graphical comparison (isoxaflutole, RPA 202248)
Du et al., 2006 (64)
Monthly
NSE, PBIAS (atrazine)
NSE ∈ (0.31, 0.92), PBIAS ∈ (–0.97, 0.06)
Daily
NSE, PBIAS (atrazine)
NSE ∈ (0.09, 0.51), PBIAS ∈ (–0.97, 0.06)
Kannan et al., 2006 (44)
Event
Graphical comparison (terbuthylazine, terbutryn, cyanazine, bentazone)
Vazquez-Amabile et al., 2006 (54)
Daily
R2, NSE, RMSE (atrazine)
R2 ∈ (0.10, 0.81), PBIAS ∈ (–2.94, 0.40), RMSE ∈ (1.29, 2.95)
Monthly
R2, NSE, RMSE (atrazine)
R2 ∈ (0.20, 0.63), PBIAS ∈ (–3.69, 0.40), RMSE ∈ (0.90, 2.60)
Coupe, 2007 (65)
Daily
Scatter plot (fluometuron)
Kannan et al., 2007 (66)
Daily
Time-series plot (linear alkylbenzene sulphonate)
Larose et al., 2007 (34)
Monthly
R2, RMSE, NSE, PBIAS (atrazine)
R2 = 0.66, RMSE = 0.98, NSE = 0.59, PBIAS = 37%
Daily
R2, RMSE, NSE, PBIAS (atrazine)
R2 = 0.57, RMSE = 1.38, NSE = 0.50, PBIAS = 0.42 Continued on next page.
403
Table 4. (Continued). Summary of SWAT Pesticide Modeling Performances Study
Performance measures
Performance evaluation criteria
Monthly
R2, NSE, RMSE (atrazine)
R2 ∈ (0.03, 0.68), NSE ∈ (–7.38, 0.50), RMSE ∈ (1.03, 4.89)
Daily
R2, NSE, RMSE (atrazine)
R2 ∈ (0.01, 0.50), NSE ∈ (–0.67, 0.42), RMSE ∈ (1.58, 3.42)
Heathman et al., 2008 (55)
Monthly
R2, NSE (atrazine)
R2 = 0.40, NSE = –17.15, RMSE = 5.62
Luo et al., 2008 (24)
Annually
R2, NSE, RMSE (diazinon)
R2 ∈ (0.82, 0.86), NSE ∈ (0.36, 0.80), RMSE ∈ (0.59, 20.10)
Annually
R2, NSE, RMSE (chlorpyrifos)
R2 ∈ (0.79, 0.90), NSE ∈ (0.55, 0.87), RMSE ∈ (0.47, 6.83)
Monthly
NSE, PBIAS (diazinon)
NSE ∈ (0.52, 0.70), PBIAS ∈ (0.14, 0.50)
Monthly
NSE, PBIAS (chlorpyrifos)
NSE ∈ (0.50, 0.64), PBIAS ∈ (0.20, 0.28)
Monthly
R2, NSE, RMSE (diazinon)
R2 = 0.995, NSE = 0.921, RMSE = 15.3
Monthly
R2, NSE, RMSE (chlorpyrifos)
R2 = 0.873, NSE = 0.824, RMSE = 23.5
Annually
R2, NSE, RMSE (diazinon)
R2 = 0.989, NSE = 0.886, RMSE = 64.7
Annually
R2, NSE, RMSE (chlorpyrifos)
R2 = 0.963, NSE = 0.917, RMSE = 43.1
Monthly
R2, NSE, RMSE (diazinon)
R2 ∈ (0.59, 0.81), NSE ∈ (0.50, 0.79), RMSE ∈ (–0.55, 0.21)
Monthly
R2, NSE, RMSE (chlorpyrifos)
R2 ∈ (0.45, 0.76), NSE ∈ (0.63, 0.72), RMSE ∈ (–0.096, 0.20)
Mehdi et al., 2013 (86)
Monthly
R2, NSE, RMSE (atrazine)
R2 ∈ (0.50, 0.81), NSE ∈ (0.14, 0.52), PBIAS ∈ (–0.3, 0.42)
Bannwarth et al., 2014 (47)
Daily
NSE (atrazine)
NSE ∈ (0.61, 0.92)
Daily
NSE (chlorothalonil)
NSE ∈ (0.28, 0.67)
Daily
NSE (endosulfan)
NSE ∈ (0.31, 0.86)
Quansah et al., 2008 (45)
Luo and Zhang, 2009 (49)
Zhang and Zhang, 2011 (52)
Ficklin et al., 2013 (40)
Temporal scale
Continued on next page.
404
Table 4. (Continued). Summary of SWAT Pesticide Modeling Performances Study
Temporal scale
Performance measures
Performance evaluation criteria
Boithias et al., 2014 (36)
Daily
R2, PBIAS (metolachlor)
R2 = 0.22, PBIAS = –0.57
Boulange et al., 2014 (70)
Daily
R2, NSE (mefenacet)
R2 = 0.61, NSE = 0.65
Fohrer et al., 2014 (30)
Daily
R2, NSE (metazachlor)
R2 = 0.68, NSE = 0.62
Daily
R2, NSE (flufenacet)
R2 = 0.51, NSE = 0.13
Chen et al., 2017 (25)
Monthly
R2, NSE, PBIAS (diuron)
R2 = 0.69, NSE = 0.58, PBIAS = 0.19
Ligaray et al., 2017 (73)
Daily
R2, NSE (malathion)
R2 = 0.52, NSE = 0.36
Ouyang et al., 2017 (75)
Event
Graphical comparison (atrazine, isoprothiolane, oxadiazon)
Winchell et al., 2018 (76)
Daily
RMSE, PBIAS (malathion)
RMSE ∈ (0.01, –1.56), PBIAS ∈ (1, 45.7)
Zhang et al., 2018 (53)
Daily
R2, NSE, PBIAS (chlorpyrifos)
R2 = 0.31, NSE = 0.18, PBIAS = –0.016
6. Mitigation Strategies in Pollution Control Considering the negative effects of pesticide use on nontarget organisms due to the presence of pesticide residues in both surface water and groundwater, mitigation strategies are necessary to alleviate pesticide pollution. The most common approach is to avoid or decrease the usage or to increase the application of reduced-risk pesticides. For example, Zhang et al. (39) examined the impact of almond pest-management practices on water quality in the San Joaquin River watershed by means of a pesticide use survey and SWAT modeling. They found that, in the study area, traditional dormant organophosphate usage had been replaced by other pesticide application methods, which helped to reduce the organophosphate concentrations in the water dramatically. Applications of lower-risk pesticides (e.g., Bacillus thringiensis and pheromones) had become more popular in this area, revealing a promising future of reduced organophosphate contamination from almond fields in the southern Central Valley of California. If a reduction of pesticide usage is hard to achieve, other effective approaches should be employed to alleviate pesticide transport from the crop fields where the pesticides are applied to receiving water bodies. For example, the vegetative filter 405
strip (VFS) is considered to be a vital tool for trapping hydrophobic pesticides in the same manner as it traps sediment (52, 87). Aside from the VFS, many other best management practices (BMPs), both structural and nonstructural, for helping to minimize the transport of pesticides from application areas have also been modeled using SWAT. Table 5 summarizes commonly used BMPs for controlling pesticide contamination and how they were implemented in SWAT files for BMP modeling. It should be pointed out that BMPs are not default settings for SWAT pesticide modeling. Users can either turn on or turn off BMPs. Once a specific BMP has been turned on, users need to provide SWAT with specific parameters to represent the structural and nonstructural BMPs, which are usually located in the files listed in Table 5. Structural BMPs for pesticides include VFSs, strip cropping, grassed waterways, and sediment ponds. Strip cropping aims to increase surface roughness by including bands of other crops within a pesticide application field, which reduces both the overland flow velocity and the amounts of transported sediment and agrochemicals. A grassed waterway is a vegetated channel that increases the flow retention time and sediment trapping efficiency. Sediment ponds or wetlands are impoundments that receive loadings from all land areas including agricultural fields and help to remove suspended solids and associated pollutants in both agricultural and urban settings. Non-structural BMPs usually refer to any management method, but not a physical structure, capable of alleviating agrochemical pollution. Examples include using cover crops or residue management to cover bare soils and prevent erosion and altering pesticide application dates to avoid major precipitation events. In SWAT modeling, most structural BMPs are governed by .ops files, whereas nonstructural BMPs are mainly governed by .mgt files. Many SWAT modeling studies have indicated the great usefulness of structural and nonstructural BMPs in controlling pesticide loadings. For example, Hovelt et al. modeled the effectiveness of several BMPs in reducing atrazine fluxes towards the surface water system in Nil catchment, Belgium (59). They found that strip cropping was the most efficient way to reduce the atrazine load (–38.7%), compared to other BMPs (residue management, contour farming, use of cover crops or buffer strips). Farrand and Heidenreich evaluated the effects of two nonstructural BMPs (crop rotation and changing pesticide application time) in reducing atrazine loadings in the Route J watershed, Missouri, USA (69). They found that postponing pesticide application from pre-emergence to postemergence could avoid rainfall events and reduce atrazine loadings by as much as 80%. Zhang and Zhang (52) combined several different BMPs to evaluate their joint mitigation effects in controlling pesticide loadings in Orestimba Creek, California, USA. SWAT simulations suggested that combining multiple BMPs was effective and was able to reduce the diazinon and chlorpyrifos loads in the study area by up to 94%.
406
Table 5. Mitigation Strategies for Pesticide Pollution Control in SWAT Modeling BMP
Purpose
SWAT files
VFS
Strip of dense vegetation located between crop fields and streams to remove sediment and sediment-attached pollutants
.ops
Strip cropping
Arrangement of bands of other crops within a field to increase surface roughness
.ops
Grassed waterway
Vegetated channel that reduces flow velocity and traps sediment
.ops
Pond or wetland
Impoundment receiving loadings generated from cropland and urban areas
.pnd
Cover crops
Crops planted to cover the soil during winter time to minimize erosion
.mgt
Contour planting
Tilling and planting of crops following the contour of the land, as opposed to straight rows, to help increase surface storage and roughness
.ops
Residue management
Keeping residue on crop fields after harvest
.mgt
Plow operation
Plowing and tillage to redistribute residues, nutrients, pesticides, and bacteria in the soil
.mgt
Crop rotation
Rotating the crop planted in a certain area to reduce pesticide applied there for one specific crop
.mgt
Pesticide application time
Changing pesticide application timing to avoid major rainfall events
.mgt
7. Research Gaps and Future Improvements Although SWAT has been widely used for pesticide fate and transport studies, it is still undergoing continuous development to more accurately reflect various complicated modeling environments. This overview indicates that there are still some limitations to the model and critical research gaps to be filled. Therefore, future efforts in model improvement are still required. Our recommendations for future SWAT development are summarized in the six areas listed below. 7.1. Developing a Framework with Modulized Field- and Basin-Level Simulations SWAT modeling at the watershed scale provides an important hub for the development and application of pesticide simulations for two directions of field and regional scales. At the field scale (equivalent to the HRU scale in SWAT), a promising simulation design is that SWAT generates predictions for hydrologic, sediment, and nutrient responses and provides the option for users to couple 407
their own loadings for pesticides (which are usually generated from field-scale models specialized for pesticide simulations). In that way, end users can either use SWAT to conduct field-level simulations to generate pesticide loadings for basin-level routing (default SWAT settings) or employ their own preferred field-scale models for pesticide simulations and then couple them with the SWAT basin-level transport module (a new framework). For example, the U.S. EPA uses the Pesticide Root-Zone Model (PRZM), a field-scale model, to estimate pesticide off-site movement from treated surfaces. The PRZM was specifically developed to use the limited inputs according to the data requirements and provides modeling options for pesticide management and regulation. Although it is theoretically possible to incorporate all of those pesticide-focused functions at the field scale into SWAT, it would be more efficient to introduce data interfaces for importing results from the specialty models for simulations at larger scales. In a recent study (88), PRZM and SWAT were coupled to build the Pesticide Runoff Management Tool, in which the PRZM-predicted edge-of-field pesticide fluxes were used by SWAT for further fate and transport simulations in a watershed. Similar modeling efforts have also been employed for integrating paddy-scale rice pesticide models into SWAT (70, 89). It is noteworthy that SWAT is more complete in its hydrology than many field-scale models. Therefore, the inconsistencies in the surface hydrology simulations and associated pesticide loadings between the two models should be considered during the integration. 7.2. Improvements in Best Management Practices (BMPs) SWAT has built-in modeling capability for evaluating BMP effectiveness but with some issues for representing field conditions related to BMP implementation for pesticide management. First, empirical regression equations are used in SWAT for modeling a VFS during each runoff event, whereas pesticide residues and fate processes in a VFS are not considered. Continuous simulations are required for more realistic representations of the mitigation effects of implementing a VFS (90). Second, some BMPs are modeled at the subbasin level in SWAT, which might not be appropriate for BMP evaluation. For example, grass waterways and vegetated ditches are simulated in SWAT by altering the roughness, cover, and erodibility of all channels in a subbasin (91, 92). At the same time, field-scale models specialized for structural BMPs, such as VFSMOD for vegetative filter strips (93) and VFDM for vegetated filter ditches (http://www.waterborne-env.com/model/vfdm/, accessed October 8, 2018), are not capable of simulating BMP effectiveness at the watershed scale where surface water monitoring and ecological risk assessments are usually conducted. Therefore, the coupling between SWAT and those models will provide better results to support management decisions. 7.3. In-Pond Process Simulation Before routing into a main channel, a pond is usually considered at either the field or subbasin level for pesticide mitigation and evaluation. For example, the Federal Insecticide, Fungicide, and Rodenticide Act modeling framework is based 408
on a hypothetical pond (called “USEPA pond”) for pesticide risk assessment in aquatic ecosystems. In addition, the effectiveness of sediment ponds in removing suspended solids and associated pollutants has been demonstrated in both agricultural and urban settings. SWAT currently uses in-pond processes only for water, sediment, and nutrients, but not for pesticides. Similar SWAT equations used for pesticide simulations in lakes and reservoirs could be migrated for modeling pesticide removal in ponds (94). 7.4. In-Stream Transport Improvement One potential improvement for in-stream transport could be incorporating the temperature-dependent degradation of pesticides. Currently, SWAT implements first-order kinetics with a fixed value of reaction rate constant for a pesticide. A commonly accepted approach in modeling in general is to introduce water temperature as a factor in adjusting that rate constant. For example, the U.S. EPA uses a factor in the form of 2(t–t0)/10, where t0 is the reference temperature at which the input rate constant is determined and t is the water temperature at each simulation step, both in degrees Celsius. This factor suggests that, with an increase of 10 °C in the water temperature, the mass of pesticide degraded will be doubled. Because the input rate constants are more commonly estimated for warmer seasons (usually, 20 or 25 °C), the introduction of a temperature dependence would mainly affect the prediction of pesticide concentrations during colder seasons. In some agricultural areas, such as those in California, winter rainy seasons are important periods for pesticide-management regulation. Related practices include the Dormant Spray Water Quality Initiative (95) and recent efforts to reduce residential uses of insecticides during winter months (96). 7.5. Integration between SWAT and the USGS Hydrologic Unit Code (HUC) System Integration between SWAT and USGS hydrologic data would be highly appreciated. Initial development of such efforts has been accomplished, such as in the Hydrologic and Water Quality System (HAWQS, https://epahawqs.tamu.edu/, accessed October 8, 2018) supported by the U.S. EPA (97, 98), which is at either the HUC8 or HUC12 level. With the release of the NHDPlus database (99), it is possible to refine watershed boundaries to the catchment level (100). The general purpose is to make use of the predefined watershed delineations, stream networks, and associated attribute data provided by USGS, so that users could skip most of the SWAT configuration and parameterization steps and place more focus on their specific modeling objectives. In addition, the modeling results arranged by the HUC (hydrologic unit code) system are more compatible with the spatial scale of water-quality management, such as the 303(d) listing of the Clean Water Act. 7.6. Soft Data Incorporation Soft data for model calibration should also be considered in future extensions. Hard data are temporal observation records of streamflow-discharge and 409
water-quality variables (such as daily flow rate or monthly nitrate loads), usually obtained at the watershed outlet, which represent aggregations of information, without sufficient descriptions of the spatial distribution of individual sources and sinks for various constituents of interest (101). Soft data (102), on the other hand, are everything else that is missing from the hard data. This can include data from soil columns, field plots, subbasins, and other modeling research efforts among other sources, such as the denitrification rate at the field scale (103), the total nitrogen contributed by groundwater flow (104), the regional crop Leaf Area Index dynamics (105, 106), the harvest index (107), the soil water content (108), storm peak intensity (109), and the physical and spatial proximity between two watersheds (110). Model calibration is the inverse approach for determining the optimum set of parameters by minimizing differences between observation data and simulation outputs. Thus, it is possible to obtain a model that successfully captures hard data but does not follow the actual interior watershed behavior, which is also known as the problem of “equifinality” (111). Under this circumstance, any available soft data could be highly critical during the calibration process as constraint criteria, ensuring that the modeling results follow the actual watershed behavior instead of being “precisely wrong” as a result of overparameterization. Yen et al. (100) integrated 59 soft data variables to ensure that SWAT captured the physical processes known to occur in Eagle Creek watershed, Indiana. Currently, soft data have been incorporated as a part of the ongoing federal project Conservation Effects Assessment Project Phase II (CEAP II) for the contiguous United States (https://www.nrcs.usda.gov/wps/portal/nrcs/main/national/technical/nra/ceap/, accessed October 8, 2018). Current published SWAT pesticide modeling studies rarely consider soft data as calibration constraints, mainly because of the scarcity of measurements related to pesticide fate and transport at both the outlet and field levels. However, it will be very helpful to incorporate soft data in future modeling studies. The literature values of SKOC and HLIFE_S recorded in Table 3 could be a good starting point for users to constrain their parameters within reasonable ranges to avoid the problems caused by overparameterization.
8. Conclusions This chapter has provided a rigorous overview of recent SWAT pesticide modeling studies at the watershed scale. Fundamental simulation mechanisms were first introduced. Required modeling inputs and associated parameterization processes were then covered. We summarized pesticide modeling performances and critical paramerters from peer-reviewed articles recorded in the SWAT literature database. Mitigation strategies to control pesticide contamination, especially structural and non structural BMPs, were highlighted in this chapter. Efficient BMPs and how they are represented in SWAT simulations were emphasized. The information provided by this chapter allows interested readers to delve into more detail as needed on specific topics, which helps provide a thorough understanding of the capabilities and limitations of current studies, as 410
well as opportunities for future research. The six suggested improvements should be seriously considered by both model developers and end users in the future. We believe that SWAT will continue to have broader implementation around the world and that its use will provide further contributions to pesticide fate and transport modeling while filling the research gaps mentioned in this chapter.
Acknowledgments This study was partially funded by the USDA-ARS Delta Region Areawide Aquatic Weed Project (DRAWWP). The authors also thank Ms. Ruoxin Wu for help in generating Figure 1 and Dr. Michael L. Grieneisen for proof reading.
References Alexandratos, N.; Bruinsma, J. World Agriculture Towards 2030/2050: The 2012 Revision; Food and Agriculture Organization: Rome, 2012. 2. Carvalho, F. P. Pesticides, Environment, and Food Safety. Food Energy Secur. 2017, 6, 48–60. 3. Chen, H.; Grieneisen, M. L.; Zhang, M. Predicting Pesticide Removal Efficacy of Vegetated Filter Strips: A Meta-Regression Analysis. Sci. Total Environ. 2016, 548–549, 122–130. 4. Chen, H.; Zhang, X.; Demars, C.; Zhang, M. Numerical Simulation of Agricultural Sediment and Pesticide Runoff: RZWQM and PRZM Comparison. Hydrol. Processes 2017, 31, 2464–2476. 5. Ippolito, A.; Kattwinkel, M.; Rasmussen, J. J.; Schäfer, R. B.; Fornaroli, R.; Liess, M. Modeling Global Distribution of Agricultural Insecticides in Surface Waters. Environ. Pollut. 2015, 198, 54–60. 6. About Water Exposure Models Used in Pesticide Assessments; U.S. Environmental Protection Agency. https://www.epa.gov/pesticide-scienceand-assessing-pesticide-risks/about-water-exposure-models-used-pesticide (accessed October 2018). 7. Stone, W. W.; Gilliom, R. J.; Ryberg, K. R. Pesticides in U.S. Streams and Rivers: Occurrence and Trends during 1992–2011. Environ. Sci. Technol. 2014, 48, 11025–11030. 8. Starner, K.; Zhang, X. Analysis of Pesticide Detections in California Surface Waters, 1991–2010: Identification of Detections Exceeding US EPA Aquatic Life Benchmarks; Department of Pesticide Regulation, California Environmental Protection Agency: Sacramento, CA, 2011. 9. Final 2012 California Integrated Report (Clean Water Act Section 303(d) List/305(b) Report); State Water Resources Control Board. http://www.waterboards.ca.gov/water_issues/programs/tmdl/ integrated2012.shtml (accessed December 2017). 10. Chen, J. Assessment of Urbanization Impacts on Surface Runoff and Effects of Green Infrastructure on Hydrology and Water Quality. Ph.D. dissertation. Purdue University, 2018. 1.
411
11. Niraula, R.; Kalin, L.; Wang, R.; Srivastava, P. Determining Nutrient and Sediment Critical Source Areas with SWAT: Effect of Lumped Calibration. Trans. ASABE 2012, 55, 137–147. 12. Mottes, C.; Lesueur-Jannoyer, M.; Bail, M. L.; Malézieux, E. Pesticide Transfer Models in Crop and Watershed Systems: A Review. Agron. Sustainable Dev. 2014, 34, 229–250. 13. Borah, D. K.; Bera, M. Watershed-Scale Hydrologic and Nonpoint-Source Pollution Models: Review of Mathematical Bases. Trans. ASAE 2003, 46, 1553–1566. 14. Bingner, R.; Theurer, F.; Yuan, Y. AnnAGNPS Technical Processes Documentation, version 5.2; U.S. Department of Agriculture: Oxford, MS, 2011. 15. Bicknell, B.; Imhoff, J.; Kittle, J. J.; Donigian, A. J.; Johanson, R.; Barnwell, T. Hydrological Simulation Program—Fortran User’s Manual for Release 11; U.S. Environmental Protection Agency: Athens, GA, 1996. 16. MIKE SHE User Manual; DHI Water & Environment: Hørsholm, Denmark, 2007. 17. Arnold, J. G.; Kiniry, J. R.; Srinivasan, R.; Williams, J. R.; Haney, E. B.; Neitsch, S. L. Soil & Water Assessment Tool: Input/Output File Documentation, Version 2012; TWRI Report TR-439; Texas Water Resources Institute: College Station, TX, 2015. 18. Neitsch, S. L.; Arnold, J. G.; Kiniry, J. R.; Williams, J. R. Soil & Water Assessment Tool: Theoretical Documentation, Version 2009; TWRI Report TR-406; Texas Water Resources Institute: College Station, TX, 2011. 19. Borah, D. K.; Bera, M. Watershed-Scale Hydrologic and Nonpoint-Source Pollution Models: Review of Applications. Trans. ASAE 2004, 47, 789–803. 20. Quilbé, R.; Rousseau, A. N.; Lafrance, P.; Leclerc, J.; Amrani, M. Selecting a Pesticide Fate Model at the Watershed Scale Using a Multi-criteria Analysis. Water Qual. Res. J. Can. 2006, 41, 283–295. 21. Arnold, J. G.; White, M. J.; Harmel, R. D.; Moriasi, D. N.; Gassman, P. W.; Abbaspour, K. C.; Srinivasan, R.; Santhi, C.; Kannan, N.; Van Griensven, A.; Van Liew, M. W.; Jha, M. K. SWAT: Model Use, Calibration, and Validation. Trans. ASABE 2012, 55, 1491–1508. 22. Gassman, P. W.; Reyes, M. R.; Green, C. H.; Arnold, J. G. The Soil and Water Assessment Tool: Historical Development, Applications, and Future Research Directions. Trans. ASABE 2007, 50, 1211–1250. 23. Gassman, P. W.; Sadeghi, A. M.; Srinivasan, R. Applications of the SWAT Model Special Section: Overview and Insights. J. Environ. Qual. 2014, 43, 1–8. 24. Luo, Y.; Zhang, X.; Liu, X.; Ficklin, D.; Zhang, M. Dynamic Modeling of Organophosphate Pesticide Load in Surface Water in the Northern San Joaquin Valley Watershed of California. Environ. Pollut. 2008, 156, 1171–1181. 25. Chen, H.; Luo, Y.; Potter, C.; Moran, P. J.; Grieneisen, M. L.; Zhang, M. Modeling Pesticide Diuron Loading from the San Joaquin Watershed into the Sacramento-San Joaquin Delta Using SWAT. Water Res. 2017, 121, 374–385. 412
26. Arnold, J. G.; Srinivasan, R.; Muttiah, R. S.; Williams, J. R. Large Area Hydrologic Modeling and Assessment Part I: Model Development. J. Am. Water Resour. Assoc. 1998, 34, 73–89. 27. Knisel, W. G.; Leonard, R. A.; Davis, F. M. Agricultural Management Alternatives: GLEAMS Model Simulations. Proc. Summer Comput. Simul. Conf. 1989, 21, 701–706. 28. Chapra, S. C. Surface Water-Quality Modeling; Waveland Press: Long Grove, IL, 2008. 29. Kim, D.-S.; Kumar, A.; Parab, R.; Palmer, M. Simulation of Atrazine Discharge in the Auglaize Watershed Using Satellite-Generated Images. Bull. Environ. Contam. Toxicol. 2004, 73, 319–325. 30. Fohrer, N.; Dietrich, A.; Kolychalow, O.; Ulrich, U. Assessment of the Environmental Fate of the Herbicides Flufenacet and Metazachlor with the SWAT Model. J. Environ. Qual. 2014, 43, 75–85. 31. Holvoet, K.; van Griensven, A.; Seuntjens, P.; Vanrolleghem, P. A. Sensitivity Analysis for Hydrology and Pesticide Supply towards the River in SWAT. Phys. Chem. Earth 2005, 30, 518–526. 32. Thelin, G. P.; Gianessi, L. P. Method for Estimating Pesticide Use for County Areas of the Conterminous United States; Open-File Report 00-250; U.S. Geological Survey: Sacramento, CA, 2000. 33. Ostojski, M. S.; Niedbala, J.; Orlinska-Wozniak, P.; Wilk, P.; Gebala, J. Soil and Water Assessment Tool Model Calibration Results for Different Catchment Sizes in Poland. J. Environ. Qual. 2014, 43, 132–144. 34. Larose, M.; Heathman, G. C.; Norton, L. D.; Engel, B. Hydrologic and Atrazine Simulation of the Cedar Creek Watershed Using the SWAT Model. J. Environ. Qual. 2007, 36, 521–531. 35. Louchart, X.; Voltz, M.; Andrieux, P.; Moussa, R. Herbicide Transport to Surface Waters at Field and Watershed Scales in a Mediterranean Vineyard Area. J. Environ. Qual. 2001, 30, 982–991. 36. Boithias, L.; Sauvage, S.; Srinivasan, R.; Leccia, O.; Sánchez-Pérez, J.-M. Application Date as a Controlling Factor of Pesticide Transfers to Surface Water during Runoff Events. Catena 2014, 119, 97–103. 37. Wang, R.; Yuan, Y.; Luo, Y.; Pitchford, A.; Bingner, R. L.; Denton, D.; Yen, H.; Zhang, M. Tiered Approaches in Analyzing Rice Field Pesticide Fate and Transport for Ecological Risk Assessment. In Managing and Analyzing Pesticide Use Data for Pest Management, Environmental Monitoring, Public Health, and Public Policy; American Chemical Society: Washington, D.C., 2018; Vol. 1283, pp 347–377. 38. Pesticide Use Reporting (PUR) Database; Department of Pesticide Regulation, California Environmental Protection Agency, 2015. https:// www.cdpr.ca.gov/docs/pur/purmain.htm (accessed October 2018). 39. Zhang, X.; Liu, X.; Luo, Y.; Zhang, M. Evaluation of Water Quality in an Agricultural Watershed as Affected by Almond Pest Management Practices. Water Res. 2008, 42, 3685–3696. 40. Darren, F.; Yuzhou, L.; Minghua, Z. Watershed Modelling of Hydrology and Water Quality in the Sacramento River Watershed, California. Hydrol. Processes 2013, 27, 236–250. 413
41. WangR.; LuoY.; ChenH.; YuanY.; BingnerR. L.; PitchfordA.; DentonD.; LockeM.; ZhangM. Environmental fate and impact assessment of thiobencarb application in California rice fields using RICEW. Sci. Total Environ. 2019, 664, 669–682. 42. Nepal, S.; Flugel, W. A.; Krause, P.; Fink, M.; Fischer, C. Assessment of Spatial Transferability of Process-Based Hydrological Model Parameters in Two Neighbouring Catchments in the Himalayan Region. Hydrol. Processes 2017, 31, 2812–2826. 43. Bardossy, A.; Singh, S. K. Robust Estimation of Hydrological Model Parameters. Hydrol. Earth Syst. Sci. 2008, 12, 1273–1283. 44. Kannan, N.; White, S. M.; Worrall, F.; Whelan, M. J. Pesticide Modelling for a Small Catchment Using SWAT-2000. J. Environ. Sci. Health B 2006, 41, 1049–1070. 45. Quansah, J. E.; Engel, B. A.; Chaubey, I. Tillage Practices Usage in Early Warning Prediction of Atrazine Pollution. Trans. ASABE 2008, 51, 1311–1321. 46. Boithias, L.; Sauvage, S.; Taghavi, L.; Merlina, G.; Probst, J.-L.; Sánchez Pérez, J. M. Occurrence of Metolachlor and Trifluralin Losses in the Save River Agricultural Catchment during Floods. J. Hazard. Mater. 2011, 196, 210–219. 47. Bannwarth, M. A.; Sangchan, W.; Hugenschmidt, C.; Lamers, M.; Ingwersen, J.; Ziegler, A. D.; Streck, T. Pesticide Transport Simulation in a Tropical Catchment by SWAT. Environ. Pollut. 2014, 191, 70–79. 48. Van Griensven, A.; Meixner, T.; Grunwald, S.; Bishop, T.; Diluzio, M.; Srinivasan, R. A Global Sensitivity Analysis Tool for the Parameters of Multi-Variable Catchment Models. J. Hydrol. 2006, 324, 10–23. 49. Luo, Y.; Zhang, M. Management-Oriented Sensitivity Analysis for Pesticide Transport in Watershed-Scale Water Quality Modeling Using SWAT. Environ. Pollut. 2009, 157, 3370–3378. 50. Luo, Y.; Zhang, M. Multimedia Transport and Risk Assessment of Organophosphate Pesticides and a Case Study in the Northern San Joaquin Valley of California. Chemosphere 2009, 75, 969–978. 51. Ficklin, D. L.; Luo, Y.; Luedeling, E.; Gatzke, S. E.; Zhang, M. Sensitivity of Agricultural Runoff Loads to Rising Levels of CO2 and Climate Change in the San Joaquin Valley Watershed of California. Environ. Pollut. 2010, 158, 223–234. 52. Zhang, X.; Zhang, M. Modeling Effectiveness of Agricultural BMPs to Reduce Sediment Load and Organophosphate Pesticides in Surface Runoff. Sci. Total Environ. 2011, 409, 1949–1958. 53. Zhang, X.; Luo, Y.; Goh, K. S. Modeling Spray Drift and Runoff-Related Inputs of Pesticides to Receiving Water. Environ. Pollut. 2018, 234, 48–58. 54. Vazquez-Amabile, G.; A. Engel, B.; C. Flanagan, D. Modeling and Risk Analysis of Nonpoint-Source Pollution Caused by Atrazine Using SWAT. Trans. ASABE 2006, 49, 667–678. 55. Heathman, G.; Flanagan, D.; Larose, M.; Zuercher, B. Application of the Soil and Water Assessment Tool and Annualized Agricultural Non-Point Source 414
56.
57.
58.
59.
60.
61.
62. 63.
64.
65.
66.
67.
68.
Models in the St. Joseph River Watershed. J. Soil Water Conserv. 2008, 63, 552–568. Maringanti, C.; Chaubey, I.; Arabi, M.; Engel, B. Application of a MultiObjective Optimization Method to Provide Least Cost Alternatives for NPS Pollution Control. Environ. Manage. 2011, 48, 448–461. Ahmadi, M.; Arabi, M.; Fontane, D. G.; Engel, B. A. Application of Multicriteria Decision Analysis with A Priori Knowledge to Identify Optimal Nonpoint Source Pollution Control Plans. J. Water Resour. Plann. Manage. 2015, 141, 04014054. Gevaert, V.; Van Griensven, A. N. N.; Holvoet, K.; Seuntjens, P.; Vanrolleghem, P. A. SWAT Developments and Recommendations for Modelling Agricultural Pesticide Mitigation Measures in River Basins. Hydrol. Sci. J. 2008, 53, 1075–1089. Holvoet, K.; Gevaert, V.; van Griensven, A.; Seuntjens, P.; Vanrolleghem, P. A. Modelling the Effectiveness of Agricultural Measures to Reduce the Amount of Pesticides Entering Surface Waters. Water Resour. Manage. 2007, 21, 2027–2035. Holvoet, K.; van Griensven, A.; Gevaert, V.; Seuntjens, P.; Vanrolleghem, P. A. Modifications to the SWAT Code for Modelling Direct Pesticide Losses. Environ. Modell. Software 2008, 23, 72–81. Holvoet, K. M. A.; Seuntjens, P.; Vanrolleghem, P. A. Monitoring and Modeling Pesticide Fate in Surface Waters at the Catchment Scale. Ecol. Model. 2007, 209, 53–64. King, K. W.; Balogh, J. C. Water Quality Impacts Associated with Converting Farmland and Forests to Turfgrass. Trans. ASAE 2001, 44, 569–576. Ramanarayanan, T.; Narasimhan, B.; Srinivasan, R. Characterization of Fate and Transport of Isoxaflutole, a Soil-Applied Corn Herbicide, in Surface Water Using a Watershed Model. J. Agric. Food Chem. 2005, 53, 8848–8858. Du, B.; Saleh, A.; B. Jaynes, D.; G. Arnold, J. Evaluation of SWAT in Simulating Nitrate Nitrogen and Atrazine Fates in a Watershed with Tiles and Potholes. Trans. ASABE 2006, 49, 949–959. Coupe, R. H. Use of a Watershed Model To Characterize the Fate and Transport of Fluometuron, a Soil-Applied Cotton Herbicide, in Surface Water. Int. J. Environ. Anal. Chem. 2007, 87, 883–896. Kannan, N.; White, S. M.; Whelan, M. J. Predicting Diffuse-Source Transfers of Surfactants to Surface Waters Using SWAT. Chemosphere 2007, 66, 1336–1345. Parker, R.; Arnold, J. G.; Barrett, M.; Burns, L.; Carrubba, L.; Neitsch, S. L.; Snyder, N. J.; Srinivasan, R. Evaluation of Three Watershed‐Scale Pesticide Environmental Transport and Fate Models1. J. Am. Water Resour. Assoc. 2007, 43, 1424–1443. Lerch, R.; Sadler, E.; Kitchen, N.; Sudduth, K.; Kremer, R.; Myers, D.; Baffaut, C.; Anderson, S.; Lin, C.-H. Overview of the Mark Twain Lake/Salt River Basin Conservation Effects Assessment Project. J. Soil Water Conserv. 2008, 63, 345–359. 415
69. Farrand, D. T.; Heidenreich, L. K. Monroe City Route J Watershed: Environmental Analysis; FAPRIUMC Report 0600; Food and Agricultural Policy Research Institute, University of Missouri: Columbia, MO, 2000. 70. Boulange, J.; Watanabe, H.; Inao, K.; Iwafune, T.; Zhang, M.; Luo, Y.; Arnold, J. Development and Validation of a Basin Scale Model PCPF-1@SWAT for Simulating Fate and Transport of rice pesticides. J. Hydrol. 2014, 517, 146–156. 71. Baffaut, C.; John Sadler, E.; Ghidey, F.; Anderson, S. H. Long-Term Agroecosystem Research in the Central Mississippi River Basin: SWAT Simulation of Flow and Water Quality in the Goodwater Creek Experimental Watershed. J. Environ. Qual. 2015, 44, 84–96. 72. Bannwarth, M. A.; Grovermann, C.; Schreinemachers, P.; Ingwersen, J.; Lamers, M.; Berger, T.; Streck, T. Non-Hazardous Pesticide Concentrations in Surface Waters: An Integrated Approach Simulating Application Thresholds and Resulting Farm Income Effects. J. Environ. Manage. 2016, 165, 298–312. 73. Ligaray, M.; Kim, M.; Baek, S.; Ra, J.-S.; Chun, J.; Park, Y.; Boithias, L.; Ribolzi, O.; Chon, K.; Cho, K. Modeling the Fate and Transport of Malathion in the Pagsanjan-Lumban Basin, Philippines. Water 2017, 9, 451. 74. Aslund, M. W.; Breton, R. L.; Padilla, L.; Winchell, M.; Wooding, K. L.; Moore, D. R. J.; Teed, R. S.; Reiss, R.; Whatling, P. Ecological Risk Assessment for Pacific Salmon Exposed to Dimethoate in California. Environ. Toxicol. Chem. 2017, 36, 532–543. 75. Ouyang, W.; Cai, G.; Tysklind, M.; Yang, W.; Hao, F.; Liu, H. Temporal-Spatial Patterns of Three Types of Pesticide Loadings in a Middle-High Latitude Agricultural Watershed. Water Res. 2017, 122, 377–386. 76. Winchell, M. F.; Peranginangin, N.; Srinivasan, R.; Chen, W. Soil and Water Assessment Tool Model Predictions of Annual Maximum Pesticide Concentrations in High Vulnerability Watersheds. Integr. Environ. Assess. Manage. 2018, 14, 358–368. 77. Winchell, M. F.; Pai, N.; Brayden, B. H.; Stone, C.; Whatling, P.; Hanzas, J. P.; Stryker, J. J. Evaluation of Watershed-Scale Simulations of In-Stream Pesticide Concentrations from Off-Target Spray Drift. J. Environ. Qual. 2018, 47, 79–87. 78. Moriasi, D. N.; Gitau, M. W.; Pai, N.; Daggupati, P. Hydrologic and Water Quality Models: Performance Measures and Evaluation Criteria. Trans. ASABE 2015, 58, 1763–1785. 79. Kalin, L.; Hantush, M. M. Evaluation of Sediment Transport Models and Comparative Application of Two Watershed Models; Report EPA/600/R-03/ 139; National Risk Management Research Laboratory, Office of Research and Development, U.S. Environmental Protection Agency: Cincinnati, OH, 2003. 80. Yen, H.; Wang, R.; Feng, Q.; Young, C.-C.; Chen, S.-T.; Tseng, W.-H.; Wolfe III, J. E.; White, M. J.; Arnold, J. G. Input Uncertainty on Watershed Modeling: Evaluation of Precipitation and Air Temperature Data by Latent Variables Using SWAT. Ecol. Eng. 2018, 122, 16–26. 416
81. Yen, H.; Lu, S.; Feng, Q.; Wang, R.; Gao, J.; Brady, D. M.; Sharifi, A.; Ahn, J.; Chen, S.-T.; Jeong, J. Assessment of Optional Sediment Transport Functions via the Complex Watershed Simulation Model SWAT. Water 2017, 9, 76. 82. Wang, R. Modeling Hydrologic and Water Quality Responses to Changing Climate and Land Use/Cover in the Wolf Bay Watershed, South Alabama. M.S. Thesis, Auburn University, Auburn, AL, 2010. 83. Wang, R.; Kalin, L. Combined and Synergistic Effects of Climate Change and Urbanization on Water Quality in the Wolf Bay Watershed, southern Alabama. J. Environ. Sci. 2018, 64, 107–121. 84. Wang, R. Y.; Kalin, L.; Kuang, W. H.; Tian, H. Q. Individual and Combined Effects of Land Use/Cover and Climate Change on Wolf Bay Watershed Streamflow in Southern Alabama. Hydrol. Processes 2014, 28, 5530–5546. 85. Chen, J.; Theller, L.; Gitau, M. W.; Engel, B. A.; Harbor, J. M. Urbanization impacts on surface runoff of the contiguous United States. J. Environ. Manage. 2017, 187, 470–481. 86. Ahmadi, M.; Arabi, M.; Hoag, D. L.; Engel, B. A. A Mixed Discrete‐Continuous Variable Multiobjective Genetic Algorithm for Targeted Implementation of Nonpoint Source Pollution Control Practices. Water Resour. Res. 2013, 49, 8344–8356. 87. Sabbagh, G. J.; Fox, G. A.; Kamanzi, A.; Roepke, B.; Tang, J.-Z. Effectiveness of Vegetative Filter Strips in Reducing Pesticide Loading: Quantifying Pesticide Trapping Efficiency. J. Environ. Qual. 2009, 38, 762–771. 88. Gali, R. K.; Cryer, S. A.; Poletika, N. N.; Dande, P. K. Modeling Pesticide Runoff from Small Watersheds through Field-Scale Management Practices: Minnesota Watershed Case Study with Chlorpyrifos. Air, Soil Water Res. 2016, 9, ASWR.S32777. 89. Tu, L. H.; Boulange, J.; Iwafune, T.; Yadav, I. C.; Watanabe, H. Improvement and Application of the PCPF-1@SWAT2012 Model for Predicting Pesticide Transport: A Case Study of the Sakura River Watershed. Pest Manage. Sci. 2018, 74, 2520–2529. 90. Muñoz-Carpena, R.; Fox, G. A.; Ritter, A.; Perez-Ovilla, O.; RodeaPalomares, I. Effect of Vegetative Filter Strip Pesticide Residue Degradation Assumptions for Environmental Exposure Assessments. Sci. Total Environ. 2018, 619–620, 977–987. 91. Bracmort, K. S.; Arabi, M.; Frankenberger, J. R.; Engel, B. A.; Arnold, J. G. Modeling Long-Term Water Quality Impact of Structural BMPs. Trans. ASABE 2006, 49, 367–374. 92. Arabi, M.; Frankenberger, J. R.; Engel, B. A.; Arnold, J. G. Representation of Agricultural Conservation Practices with SWAT. Hydrol. Processes 2007, 22, 3042–3055. 93. Sabbagh, G. J.; Fox, G. A.; Muñoz-Carpena, R.; Lenz, M. F. Revised Framework for Pesticide Aquatic Environmental Exposure Assessment that Accounts for Vegetative Filter Strips. Environ. Sci. Technol. 2010, 44, 3839–3845. 417
94. Luo, Y.; Zhang, M. Management-Oriented Sensitivity Analysis for Pesticide Transport in Watershed-Scale Water Quality Modeling. Water Res. 2009, 157, 3370–3378. 95. Dormant Spray Water Quality Initiative; California Department of Pesticide Regulation, Sacramento, CA. http://www.cdpr.ca.gov/docs/emon/ dormspray/ (accessed October 2018). 96. Budd, R.; Luo, Y.; Singhasemanon, N. Evaluation of Alternative Fipronil Use Scenarios: Modeling Results, Runoff Trials, and Product Efficacy. Addendum to “Fipronil Monitoring and Model Scenarios”. California Department of Pesticide Regulation: Sacramento, CA, 2017. http://cdpr.ca.gov/docs/emon/pubs/analysmemos.htm (accessed October 2018). 97. Yen, H.; Daggupati, P.; White, M.; Srinivasan, R.; Gossel, A.; Wells, D.; Arnold, J. Application of Large-Scale, Multi-Resolution Watershed Modeling Framework Using the Hydrologic and Water Quality System (HAWQS). Water 2016, 8, 164. 98. Yuan, Y.; Wang, R.; Cooter, E.; Ran, L.; Daggupati, P.; Yang, D.; Srinivasan, R.; Jalowska, A. Integrating Multi-Media Models to Assess Nitrogen Losses from the Mississippi River Basin to the Gulf of Mexico. Biogeosciences 2018, 2018, 1–41. 99. McKay, L.; Bondelid, T.; Dewald, T.; Johnston, J.; Moore, R.; Rea, A. NHDPlus Version 2: User Guide; National Operational Hydrologic Remote Sensing Center: Washington, D.C., 2012. 100. Hill, R. A.; Weber, M. H.; Leibowitz, S. G.; Olsen, A. R.; Thornbrugh, D. J. The Stream‐Catchment (StreamCat) Dataset: A Database of Watershed Metrics for the Conterminous United States. J. Am. Water Resour. Assoc. 2016, 52, 120–128. 101. Yen, H.; White, M. J.; Ascough, J. C.; Smith, D. R.; Arnold, J. G. Augmenting Watershed Model Calibration with Incorporation of Ancillary Data Sources and Qualitative Soft Data Sources. J Am. Water Resour. Assoc. 2016, 52, 788–798. 102. Seibert, J.; McDonnell, J. J. On the Dialog Between Experimentalist and Modeler in Catchment Hydrology: Use of Soft Data for Multicriteria Model Calibration. Water Resour. Res 2002, 38, 1241. 103. Yen, H.; White, M. J.; Arnold, J. G.; Keitzer, S. C.; Johnson, M.-V. V.; Atwood, J. D.; Daggupati, P.; Herbert, M. E.; Sowa, S. P.; Ludsin, S. A. Western Lake Erie Basin: Soft-Data-Constrained, NHDPlus Resolution Watershed Modeling and Exploration of Applicable Conservation Scenarios. Sci. Total Environ. 2016, 569, 1265–1281. 104. Yen, H.; Bailey, R. T.; Arabi, M.; Ahmadi, M.; White, M. J.; Arnold, J. G. The Role of Interior Watershed Processes in Improving Parameter Estimation and Performance of Watershed Models. J. Environ. Qual. 2014, 43, 1601–1613. 105. Wang, R. Y.; Cherkauer, K.; Bowling, L. Corn Response to Climate Stress Detected with Satellite-Based NDVI Time Series. Remote Sens. 2016, 8, 269. 106. Wang, R. Y.; Bowling, L. C.; Cherkauer, K. A.; Cibin, R.; Her, Y.; Chaubey, I. Biophysical and Hydrological Effects of Future Climate Change Including 418
107.
108.
109.
110. 111.
Trends in CO2, in the St. Joseph River Watershed, Eastern Corn Belt. Agric. Water Manage. 2017, 180, 280–296. Trybula, E. M.; Cibin, R.; Burks, J. L.; Chaubey, I.; Brouder, S. M.; Volenec, J. J. Perennial Rhizomatous Grasses as Bioenergy Feedstock in SWAT: Parameter Development and Model Improvement. GCB Bioenergy 2015, 7, 1185–1202. Wang, R. Y.; Bowling, L. C.; Cherkauer, K. A. Estimation of the Effects of Climate Variability on Crop Yield in the Midwest USA. Agric. Forest Meteorol. 2016, 216, 141–156. Chen, J.; Gitau, M. W.; Engel, B. A.; Flanagan, D. C. Suitability of CLIGEN precipitation estimates based on an updated database and their impacts on urban runoff: a case study of the Great Lakes Region, USA. Hydrolog. Sci. J. 2018, 63, 1502–1518. Wang, R. Y.; Kalin, L. Modelling Effects of Land Use/Cover Changes under Limited Data. Ecohydrology 2011, 4, 265–276. Beven, K. A Manifesto for the Equifinality Thesis. J. Hydrol. 2006, 320, 18–36.
419
