Open access peer-reviewed chapter

Setting Up a Computer Simulation Model in an Arkansas Watershed for the MRBI Program

By Gurdeep Singh and Mansoor Leh

Submitted: May 28th 2018Reviewed: August 13th 2018Published: November 28th 2018

DOI: 10.5772/intechopen.80902

Downloaded: 183

Abstract

The Mississippi River Basin Healthy Watersheds Initiative (MRBI) program launched by the USDA Natural Resources Conservation Service (NRCS) aims to improve the water quality within the Mississippi River Basin. Lake Conway Point Remove (LCPR) watershed, being one of the MRBI watersheds, is a potential candidate for evaluating the effectiveness of MRBI program. Recommended best management practices (BMPs) for LCPR watershed are pond, wetland, pond and wetland, cover crops, vegetative filter strips, grassed waterways, and forage and biomass planting. Before simulating these practices, it is essential to prepare the data needed for model setup to avoid the issue of garbage in, garbage out. This chapter focuses on detailed steps of preparing the data for model setup along with the calibration and validation of the model. The calibration and validation results were within the acceptable bounds. The results from this study provide the data to help simulate the MRBI best management practices effectively and prioritize monitoring needs for collecting watershed response data in LCPR.

Keywords

  • best management practices
  • modeling
  • water quality
  • SWAT
  • MRBI

1. Introduction

The Mississippi River Basin Healthy Watersheds Initiative (MRBI) program aims at implementing best management practices (BMPs) to control water quality. Quantifying the impacts of BMPs is important to demonstrate the worth of the MRBI program. Out of various MRBI-selected watersheds, the Lake Conway Point Remove (LCPR) watershed is the one listed in the 2011–2016 priority watershed by the Arkansas Natural Resources Commission (ANRC) [1, 2].

Field studies can be laborious and time-consuming; therefore, watershed modeling technique is generally used for analyzing the effects of BMPs on water quality. The Soil and Water Assessment Tool (SWAT, [3]) model was selected for this study. The SWAT model has been widely applied across the globe to assess the impact of various BMPs [4]. SWAT has also been applied to various watersheds in Arkansas—L’Anguille River Watershed [5, 6], Cache River Watershed [7], and Illinois River Watershed [8]. SWAT allows modifications of various parameters to simulate BMPs [9] and was applied at various spatial and temporal scales [10]. SWAT has been used to simulate impacts of land uses and BMPs [11, 12], develop maximum daily load plans [13, 14], and evaluate impacts on water quality [15, 16]. However, before simulating BMPs, it is essential to acquire and process the data needed for setting up a good model.

The goal of this chapter is to describe the steps in detail for acquiring and processing the data needed to set up, calibrate, and validate the SWAT model for the LCPR watershed.

2. Methodology

2.1. Study area

The Lake Conway Point Remove (LCPR) watershed is a 2950 km2 (1140 miles2) watershed located in central Arkansas within the counties of Conway, Faulkner, Perry, Pope, Pulaski, Van Buren, and Yell (Figure 1). The watershed has mixed land uses of forest, pasture, urban, and cropland. An increase in urbanization, in parts of the watershed, has occurred since 1999. The subwatersheds within LCPR along with the area and hydrological unit codes (HUC) can be seen in Table 1.

Figure 1.

Lake Conway Point Remove watershed.

SubwatershedSubwatershed nameArea (km2)HUC no.
1Trimble creek-west fork point remove creek77.0111102030102
2Brock creek113.1111102030101
3Devils creek-west fork point remove creek88.2111102030107
4Barns branch-east fork point remove creek102.7111102030204
5Galla creek118.0111102030303
6Whig creek-Arkansas river106.3111102030302
7Mountain view-east fork point remove creek97.8111102030201
8Upper clear creek120.4111102030103
9Rock creek-west fork point remove creek156.2111102030105
10Sunny side creek-east fork point remove creek100.9111102030202
11Lower clear creek106.5111102030104
12Prairie creek-east fork point remove creek106.9111102030203
13Gum log creek130.4111102030106
14Portland bottoms-Arkansas river90.9111102030503
15Headwaters rocky Cypress creek100.1111102030501
16Jim creek-Palarm creek92.4111102030402
17Little creek-Palarm creek106.8111102030403
18Beaverdam creek-Arkansas river88.0111102030507
19Little Palarm creek-Palarm creek89.9111102030405
20Taylor creek-Arkansas river65.1111102030506
21Tupelo bayou110.8111102030505
22Outlet rocky cypress creek70.5111102030502
23Pierce creek-Palarm creek100.0111102030404
24Little cypress creek-Palarm creek53.4111102030401
25Overcup creek81.1111102030205
26Khun Bayou-Arkansas River131.1111102030304
27Long Lake-Harris creek148.2111102030301
28Point remove creek80.2111102030206
29Miller Bayou-Arkansas river116.4111102030504

Table 1.

List of HUC 12 subwatersheds and area in LCPR watershed.

2.2. Data preparation

The objective of this task was to collect and organize all data needed for the SWAT model setup at a 12-digit hydrological unit code within the LCPR watershed. Geospatial, watershed management, water quantity, and point source data that were available and usable at the time of modeling were collected and reorganized in a consistent format for use in the SWAT model.

2.2.1. Elevation

The elevation dataset was retrieved at a 5 m resolution from GeoStor. This 5 m dataset was resampled to a 10 m resolution to reduce the size of huge files and increase the computation efficiency. The elevation map for LCPR can be seen in Figure 2.

Figure 2.

Lake Conway Point Remove watershed elevation.

2.2.2. Soils

The soil data were acquired from the Soil Survey Geographic (SSURGO) database for all LCPR counties in Arkansas and combined to make a soil map for the entire watershed. The SSURGO is the most comprehensive and detailed soil dataset available for LCPR. The soil map for LCPR can be seen in Figure 3.

Figure 3.

Soil map of Lake Conway Point Remove watershed, Arkansas, showing major soil series.

2.2.3. Land use/land cover

Land use and land cover data were acquired for 1999, 2004, and 2006 from GeoStor. Forest area was observed to be the most dominant land use and cover in the LCPR watershed. All land use and land covers were reclassified to make it compatible with the SWAT model. The land use and land cover map for LCPR can be seen in Figure 4.

Figure 4.

Land use and land cover in the Lake Conway Point Remove watershed.

2.2.4. Climate

Climatic data specifically daily precipitation and maximum and minimum temperature data were obtained from 90 climate stations from the NOAA’s National Climatic Data Center (NCDC). Data are available from 1980 to 2012 for at least one of the climatic parameters. The procedure recommended by USDA-ARS in developing SWAT-formatted climate data were followed. Daily climate data were obtained using an inverse distance-weighted interpolation algorithm. The average data were calculated for each subwatershed using a pseudo-weather station. NCDC validation results at each calibration station using leave-one-out cross-validation technique can be seen in Table 2. NEXRAD data were obtained from the Arkansas Basin River Forecasting Center (ABRFC).

StationParameterDRAIN1DNO_RAIN2ME3d4PBIAS5%R26NSE7MAE8RMSE9
Center Ridge, 4.S, AR, USAPRCP0.940.86−0.120.95−0.30.830.8315.4845.03
Conway, AR, USAPRCP0.910.79−0.640.87−1.90.590.5823.5363.56
Dardanelle, AR, USAPRCP0.950.790.510.851.50.540.5224.5571.4
Hattieville, AR, USAPRCP0.950.820.080.920.20.740.7318.1357.15
Morrilton, AR, USAPRCP0.900.820.970.92.80.690.6819.8459.78
North Little Rock Airport, AR, USAPRCP0.900.810.230.850.70.560.5524.3769.37
Perry, AR, USAPRCP0.900.82−1.190.89−3.30.650.6421.7164.82
Russellville Municipal Airport, AR, USAPRCP0.680.841.850.675.90.240.0334.799.07
Conway, AR, USATMAX0.450.990.20.950.9514.4922.31
Dardanelle, AR, USATMAX−5.020.99−2.20.950.9415.1422.95
Morrilton, AR, USATMAX−1.90.99−0.80.940.9417.3923.86
North Little Rock Airport, AR, USATMAX4.0511.80.990.999.0311.83
Russellville Municipal Airport, AR, USATMAX2.420.9910.950.9513.7122.57
Conway, AR, USATMIN−7.550.98−7.10.950.9415.5922.75
Dardanelle, AR, USATMIN−7.890.99−7.80.950.9514.1821.36
Morrilton, AR, USATMIN5.270.985.70.940.9415.8923.35
North Little Rock Airport, AR, USATMIN−9.940.99−8.30.970.9514.7919.68
Russellville Municipal Airport, AR, USATMIN6.760.996.90.960.9513.1120.5

Table 2.

NCDC precipitation and minimum and maximum temperature validation results at each calibration station using leave-one-out cross-validation.

NEXRAD detection conditioned on exceeding a given threshold gauge observations (DRAIN).


NEXRAD detects no rainfall event (DNO_RAIN).


Mean error (ME).


Index of agreement (d).


Percent bias (PBIAS).


Coefficient of determination (R2).


Nash-Sutcliffe efficiency (NSE).


Mean absolute error (MAE).


Root-mean-square error (RMSE).


2.2.5. Streamflow

The flow data are available for the West Fork Point Remove Creek near the Hattieville monitoring station from the US Geological Survey (USGS). This monitoring station is located in subwatershed 3 and covers approximately 20% of LCPR. The flow data were split between surface and baseflow using the baseflow filter program by [17].

2.2.6. Point sources

Point source data were obtained from the Arkansas Department of Environmental Quality (ADEQ) and was processed in the SWAT-compatible format. Point source data were available for flow, total suspended solids, organic nitrogen, organic and mineral phosphorus, nitrate nitrogen, ammonia nitrogen, and carbonaceous biochemical oxygen demand (CBOD). Locations for active point source facility that was incorporated in the SWAT model can be seen in Table 3.

No.SubbasinFacilityNPDES_IDLatitudeLongitude
15City of PottsvilleAR004801135.23−93.05
26City of DardanelleAR003342135.19−93.14
36Dardanelle water treatment plantARG64014935.21−93.15
46Tyson Foods Inc., DardanelleAR003671435.22−93.16
56Russellville Water and Sewer System, City CorporationAR002176835.25−93.12
66Freeman Brothers, Inc., d/b/a Bibler Brothers Lumber CompanyAR004447435.25−93.13
77SEECO, Inc., J and R Farms SE1AR005222135.4392.56
87Hamilton AggregatesARG50002635.44−92.54
98Dover Water WorksARG64014835.40−93.12
109Quality Rock/Jerusalem QuarryARG50003935.39−92.80
1110KT Rock LLCARG50003135.41−92.67
1211SEECO, Inc., Campbell Thomas SE1AR005214135.40−92.83
1313City of AtkinsAR003466535.25−92.92
1414Environmental Solutions and Services, Inc.AR005135735.09−92.71
1514Green Bay Packaging, Inc., Arkansas Kraft DivisionAR000183035.10−92.74
1616Rogers Group, Inc., Beryl QuarryAR004752035.07−92.25
1716Roy NunnARG55032235.07−92.37
1816Waste Water Management, Inc. d/b/a Oak Tree SubdivisionAR005079235.08−92.35
1916Fritts Construction, Inc., Hayden’s Place SubdivisionAR005025335.09−92.34
2016BHT Investment Company, Inc.AR004499735.09−92.33
2116Rolling Creek POAAR004253635.11−92.33
2216Genesis Water Treatment, Inc.AR005115235.11−92.34
2317Faulkner County Public Facility Board, d/b/a Preston Community WW UtilityAR005057135.03−92.41
2417Wilhelmina Cove property ownerAR004868234.93−91.11
2517City of Conway, Stone Dam CreekAR003335935.05−92.44
2617Coreslab Structures (ARK), Inc.AR005047435.06−92.43
2717MAPCO Express, Inc. #3059AR004507135.07−92.42
2817Flushing Meadows Water Treatment, Inc.AR004887935.06−92.37
2917Jesse Ferrel d/b/a Jesse Ferrel Rental DevelopmentAR004983235.09−92.37
3018City of MayflowerAR003720634.95−92.45
3118Carla KnightARG55043034.97−92.48
3219Construction Waste Management, Inc. Class IV LandfillAR005176434.93−92.44
3319Grassy Lake ApartmentsAR005033434.94−92.43
3420City of BigelowAR004999935.00−92.61
3520City of Conway, Tucker Creek WWTPAR004727935.07−92.50
3621Conway Corporation, Tupelo Bayou WWTPAR005195135.05−92.54
3722City of OppeloAR004764335.08−92.76
3824Faulkner County POID, Seven Point Lake ProjectAR005090335.02−92.18
3925Rogers Group, Inc.ARG50006635.24−92.65
4026Lentz Sand and Gravel, LLCARG50007235.12−92.76
4126City of Atkins, South WWTPAR003467335.22−92.93
4229Rogers Group, Inc., Toad Suck QuarryAR004710435.11−92.56
4329City of MorriltonARG16000135.13−92.70
4429City of MenifeeAR004936135.14−92.55
4529Gericorp, Inc.AR004862335.15−92.72

Table 3.

Active point source facility location incorporated into the SWAT model.

2.2.7. Cattle grazing, manure deposition, and poultry litter application

The detailed method for estimating pastures that should be receiving litter applications can be seen below.

Detailed methods for estimating pastures that received litter application:

  1. Create buffer of a random radius around the active poultry houses.

  2. Extract pasture areas under the buffer.

  3. Assuming a grazing density of 1 cow/0.8 ha of litter amended pasture, calculate the number of cows that can fit the buffer.

  4. Compare the calculated number of cows to the number of cows in the subwatershed.

  5. Repeat steps 1–4 to obtain the best agreement between estimated numbers of cows.

  6. Apply litter to pasture HRUs that fall under the best buffer radius.

The SWAT compatible data for cattle grazing, manure deposition, and poultry litter application can be seen in Table 4.

SubbasinCattle grazing rate (kg/day/ha)Cattle manure deposition rate (kg/day/ha)Litter application/grazing
114.385.59Yes
212.594.90Yes
39.163.57Yes
411.464.46Yes
56.112.38Yes
65.832.27Yes
713.185.13Yes
86.272.44Yes
911.434.45Yes
1011.464.46Yes
117.342.86Yes
1211.464.46Yes
136.112.38Yes
1410.514.09Yes
159.053.52Yes
1612.034.68No
1712.034.68No
1811.984.66No
1912.444.84No
206.442.51No
2112.034.68No
229.243.60Yes
2312.034.68No
2412.034.68Yes
2511.464.46Yes
267.843.05Yes
274.501.75Yes
289.153.56Yes
2910.704.16Yes

Table 4.

Cattle grazing, manure deposition, and poultry litter application data incorporated into the SWAT model.

2.2.8. Urban pasture management

The pasture management schedule relating to specific operation and crop can be seen in Table 5.

DateEndNo. of daysOperationCommentCrop
Cool-season grass (fescue)
1-AprFertilizerPoultry litter@1 ton/acre of auto-fertilizeBERM
1-MayPlantingWarm-season grass (Bermuda)BERM
15-May31-Oct170GrazingBERM
15-JunHay cutting85% removalBERM
15-JulHay cutting85% removalBERM
15-AugHay cutting85% removalBERM
15-SeptHay cutting85% removalBERM
15-OctHay cutting85% removalBERM
1-MarFertilizerPoultry litter@1 ton/acre of auto-fertilizeBERM
15-May30-Oct170GrazingBERM
15-JunHay cutting85% removalBERM
15-JulHay cutting85% removalBERM
15-AugHay cutting85% removalBERM
15-SeptHay cutting85% removalBERM
15-OctHay cutting85% removalBERM
1-AprFertilizerPoultry litter@1 ton/acre of auto-fertilizeBERM
Warm-season grass (Bermuda)
31-AugFertilizerPoultry litter@1 ton/acre of auto-fertilizeFESC
1-SeptPlantingCool-season grass (fescue)FESC
15-Mar1-Jun79GrazingFESC
15-MayHay cutting85% removalFESC
15-JunHay cutting85% removalFESC
1-SeptFertilizerPoultry litter@1 ton/acre of auto-fertilizeFESC
1-OctGrazingFESC
15-OctHay cutting85% removalFESC
21-FebFertilizerPoultry litter@1 ton/acre of auto-fertilizeFESC
15-Mar1-Jun79GrazingFESC
15-MayHay cutting85% removalFESC
15-JunHay cutting85% removalFESC
1-SeptFertilizerPoultry litter@1 ton/acre of auto-fertilizeFESC
1-Oct30-Nov61GrazingFESC
21-FebFertilizerPoultry litter@1 ton/acre of auto-fertilizeFESC

Table 5.

Pasture management schedule incorporated into the SWAT model.

2.2.9. Ponds and wetlands

SWAT input parameters relating to ponding were PND_FR, PND_PSA (ha), PND_PVOL (104 m3), PND_ESA, PND_EVOL, and PND_VOL. These ponding parameters can be seen in Table 6. SWAT input parameters relating to wetland were WET_FR, WET_NSA (ha), WET_NVOL 104 (m3), WET_MXSA (ha), WET_MXVOL 104 (m3), and WET_VOL 104(m3). These wetland parameters can be seen in Table 7.

SubwatershedPND_FRPND_PSA (ha)PND_PVOL (104 m3)PND_ESAPND_EVOLPND_VOL
10.0683030404030
20.00744664
30.290146146195195146
40.330194194258258194
50.0664545606045
60.0905555737355
70.138777710310377
80.0624343575743
90.0645757767657
100.0593434454534
110.0804949656549
120.0885454717154
130.0876565878765
140.1266565878765
150.0724141555541
160.1025454727254
170.0986060808060
180.0683434454534
190.200103103137137103
200.225848411211284
210.0674242565642
220.0973939525239
230.0965555737355
240.1113434454534
250.1286060797960
260.109828210910982
270.0877474989874
280.0532424333324
290.190126126168168126

Table 6.

Pond input parameters for each subwatershed.

SubwatershedWET_FRWET_NSA (ha)WET_NVOL 104 (m3)WET_MXSA (ha)WET_MXVOL 104 (m3)WET_VOL 104(m3)
10.00000.000.000.000.000.00
20.00000.000.000.000.000.00
30.024965.9732.99219.90109.956.60
40.015146.4323.22154.7877.394.64
50.00041.380.694.612.300.14
60.004012.626.3142.0621.031.26
70.00010.150.080.500.250.02
80.00000.000.000.000.000.00
90.00000.000.000.000.000.00
100.00000.000.000.000.000.00
110.00000.000.000.000.000.00
120.00000.000.000.000.000.00
130.00187.183.5923.9211.960.72
140.014639.9019.95133.0166.513.99
150.009327.8413.9292.7946.392.78
160.00030.960.483.201.600.10
170.00000.000.000.000.000.00
180.014237.5718.79125.2462.623.76
190.005815.537.7751.7825.891.55
200.00193.771.8912.576.280.38
210.005217.238.6257.4528.721.72
220.033170.0635.03233.53116.767.01
230.00175.042.5216.798.400.50
240.00406.333.1621.0910.540.63
250.00000.000.000.000.000.00
260.008131.8815.94106.2553.133.19
270.00020.810.412.701.350.08
280.006014.397.2047.9723.991.44
290.0364127.1363.56423.75211.8812.71

Table 7.

Wetland input parameters for each subwatershed.

2.3. Model setup

SWAT divides a watershed into subwatersheds and further subwatersheds into hydrological response units. User-defined approach for delineating subwatersheds was used. ArcSWAT was used to develop the SWAT2012 model with a revision number 635. A threshold of 0% for land use, 5% for soil, and 0% for slope was used to delineate HRUs resulting in 3402 HRUs. Some past studies reported the relationship between watershed response and HRU delineation approach [18, 19].

2.4. Calibration and validation

Before calibrating a model, sensitivity analysis is usually performed to reduce the number of parameters. Latin hypercube (LH) one-at-a-time (OAT) method [20] was used to identify the sensitive parameters that might affect the output results. A total of 22 flow parameters were tested, and the following 12 were found sensitive: SOL_AWC, CN2, ALPHA_BF, SOL_K, CH_N2, CH_K2, CANMX, RCHRG_DP, SURLAG, GW_DELAY, OV_N, and GW_REVAP.

The model calibration period was from 1987 to 2006 and the validation period was from 2007 to 2012. The first 3 years of calibration period were selected as a warm-up period so that the model parameters can be initialized. The calibration started with baseflow followed by surface flow adjusting related parameters affecting baseflow and surface flow. The SWAT Check tool [21] was used before calibration to make sure that the simulated outputs were within the reasonable ranges. The Load Estimator (LOADEST) tool [22] was used on a water quality dataset available from Sept 2011 to Dec. 2012 at Hattieville and Apr. 2012 to Dec. 2012 at Morrilton. The regression coefficients were found to be statistically significant (p < 0.05) at Hattieville and Morrilton for sediment, total phosphorus, and nitrate nitrogen. The performance of the model was determined mainly using the coefficient of determination (R2).

3. Results and discussion

3.1. Calibration and validation results

Various SWAT parameters that were calibrated along with their parameter ranges and final calibrated values can be seen in Table 8. The annual calibrated R2 for the total, surface, and baseflow was 0.83, 0.85, and 0.16. The validated R2 was 0.91, 0.93, and 0.60 for the total, surface, and baseflow. The monthly calibrated R2 was 0.73, 0.73, and 0.54 and validated R2 was 0.84, 0.78, and 0.76 for the total, surface, and baseflow, respectively. The calibration and validation scatter plots for total flow, surface flow, and baseflow can be seen in Figure 5. The validated R2 for water quality was 0.5–0.7 at Hattieville and 0.7–0.87 at Morrilton. The results are within acceptable limits of other modeling studies relating to limited data availability [24, 25].

File/parameterDefinitionMINMAXUnitsCalibrated valueNotes
.bsn
ESCOSoil evaporation compensation factor010.95Based on water balance
EPCOPlant uptake compensation factor011Based on water balance
.gw
GW_DELAYGroundwater delay05002Calibrated value
ALPHA_BFBaseflow alpha factor01Days0.0932Baseflow separation factor
GW_REVAPGroundwater “revap” coefficient0.020.20.072Calibrated value
REVAPMNThreshold depth of water in the shallow aquifer for “revap” to occur01000750Calibrated value
RCHRG_DPDeep aquifer percolation fraction010.06Calibrated value
GWQMNThreshold depth of water in the shallow aquifer required for return flow to occur05000mm800Calibrated value
.rte
CH_N2Manning’s “n” value for the main channel−0.010.30.014Calibrated value
CH_K2Effective hydraulic conductivity−0.01500mm/hr6
.hru
CANMX-ForestMaximum canopy storage0100mm6Wu et al., [23]
CANMX-AgMaximum canopy storage0100mm2.8
CANMX-PastureMaximum canopy storage0100mm4
CANMX-UrbanMaximum canopy storage0100mm0.1
SURLAGSurface runoff lag time124Days2Calibrated value
HRU_SLPAverage slope steepness01m/mReduce by 10%Based on identified high sediment yield on high-slope agricultural HRUs
.mgt
CN2SCS runoff curve number for moisture condition II3598CN + 1Calibrated value
.sol
SOL_AWCSoil available water capacity01mm/mmSOL_AWC × 1.13Calibrated value

Table 8.

SWAT model parameter ranges and the final calibrated values.

Figure 5.

Calibration [left] and validation [right] scatter plots for total flow, surface flow, and baseflow.

4. Conclusions

Modeling studies are gaining popularity due to rapidness of insight generation before actually performing field experiments. The initiative led by the Mississippi River Basin focused on analyzing the water quality benefits from intended best management practices with the help of modeling studies. However, merely simulating best management practices will not be able to provide reliable results unless the model has been set up correctly and robust. This chapter focused on the detailed discussion for setting up the model to a point where the model setup procedure can be replicated. The model was set up with all relevant information, and each data preparation step has been explained in detail. The model was calibrated and validated for flow at Hattieville. Due to limited water quality data, the model was validated for sediment, total phosphorus, and nitrate nitrogen at Hattieville and Morrilton. The results were satisfactory and within the ranges reported by previous studies. Results from this study can be used to evaluate the relative effectiveness of MRBI-recommended agricultural BMPs for analyzing pollutant load reductions and improving water quality in similar data-limited watersheds.

Conflict of interest

The authors declare no conflict of interest.

© 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution 3.0 License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Gurdeep Singh and Mansoor Leh (November 28th 2018). Setting Up a Computer Simulation Model in an Arkansas Watershed for the MRBI Program, Water and Sustainability, Prathna Thanjavur Chandrasekaran, IntechOpen, DOI: 10.5772/intechopen.80902. Available from:

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