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APPENDIX D
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TABLE OF CONTENTS

APPENDIX D.1. MONITORING DATA SETS USED IN EVALUATING THE DRINKING WATER MONITORING 

FREQUENCY, EXPOSURE ESTIMATIONS, AND UNCERTAINTY ANALYSES.................................................................3

APPENDIX D.2. GEOSTATISTICAL ANALYSIS AND CONDITIONAL STOCHASTIC SIMULATION APPLICATION........... 10

APPENDIX D.3..........EXPLORATION OF USING A WATERSHED MASS BALANCE APPROACH TO ESTIMATING DAILY 

ATRAZINE CONCENTRATIONS IN MONITORED AGRICULTURAL WATERSHED EFFLUENT ...................................... 57
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APPENDIX D.1. MONITORING DATA SETS USED IN EVALUATING THE DRINKING 

WATER MONITORING FREQUENCY, EXPOSURE ESTIMATIONS, AND 

UNCERTAINTY ANALYSES

D.1.1 NCWQR 1995 Maumee River Data Set

Source: NCWQR (2010)

Date Julian 

Day

Flow (cfs) Atrazine 

(ug/L)

Acetochlor 

(ug/L)

Chlorpyrifos 

(ug/L)

01/03/95 2 362.2 0.331 0.000 0.000

01/06/95 5 2859 0.388 0.000 0.000

01/09/95 8 2861.6 0.182 0.000 0.000

01/13/95 12 896.6 0.351 0.000 0.000

01/15/95 14 3924.7 0.334 0.000 0.000

01/16/95 15 6035.7 0.318 0.000 0.000

01/20/95 19 4460.6 0.286 0.000 0.000

01/20/95 19 5113 0.396 0.000 0.000

01/20/95 19 8955.8 0.298 0.000 0.000

01/21/95 20 14609 0.300 0.000 0.000

01/21/95 20 20612 0.302 0.000 0.000

01/21/95 20 24674 0.288 0.497 0.000

01/22/95 21 25640 0.316 0.000 0.000

01/22/95 21 23795 0.334 0.000 0.000

01/22/95 21 21372 0.351 0.000 0.000

01/23/95 22 19071 0.351 0.000 0.000

01/23/95 22 17348.9 0.317 0.000 0.000

01/25/95 24 7953.8 0.300 0.505 0.000

01/27/95 26 4914.9 0.337 0.000 0.176

01/30/95 29 3749.2 0.188 0.000 0.000

02/03/95 33 2189.8 0.246 0.000 0.000

02/06/95 36 1392.3 0.179 0.000 0.000

02/10/95 40 1245.8 0.249 0.000 0.000

02/13/95 43 1208.4 0.147 0.000 0.000

02/17/95 47 1028.2 0.192 0.000 0.000

02/24/95 54 3924.7 0.224 0.288 0.000

02/27/95 57 3461.4 0.149 0.000 0.000

02/28/95 58 5935.9 0.221 0.000 0.000

03/01/95 59 10332 0.236 0.000 0.000

03/02/95 60 9564 0.238 0.000 0.000

03/03/95 61 6741 0.238 0.000 0.000

03/04/95 62 5177.1 0.167 0.000 0.095

03/04/95 62 4345.2 0.237 0.000 0.000

03/05/95 63 3411.9 0.234 0.000 0.000
Page 4 of 96

Date Julian 

Day

Flow (cfs) Atrazine 

(ug/L)

Acetochlor 

(ug/L)

Chlorpyrifos 

(ug/L)

03/06/95 64 2901.1 0.232 0.000 0.000

03/07/95 65 3837.4 0.252 0.000 0.000

03/08/95 66 27764 0.285 0.000 0.000

03/09/95 67 34670 0.298 0.000 0.000

03/10/95 68 27476 0.284 0.000 0.000

03/11/95 69 21372 0.313 0.000 0.000

03/12/95 70 16034 0.253 0.000 0.000

03/13/95 71 13063.3 0.302 0.000 0.000

03/14/95 72 9831.6 0.219 0.000 0.000

03/16/95 74 5971 0.000 0.000 0.000

03/18/95 76 4042.2 0.265 0.000 0.000

03/20/95 78 3101.5 0.000 0.000 0.000

03/24/95 82 2332.7 0.235 0.000 0.000

03/27/95 85 1314.8 0.198 0.000 0.000

03/31/95 89 1207.4 0.203 0.281 0.000

04/09/95 98 3793.1 0.318 0.000 0.000

04/09/95 98 6120.7 0.189 0.000 0.000

04/09/95 98 8448.2 0.657 0.897 0.000

04/10/95 99 23302.1 0.700 0.798 0.000

04/11/95 100 46754 0.484 0.438 0.000

04/12/95 101 51240 0.578 0.484 0.000

04/13/95 102 0 0.524 0.429 0.000

04/15/95 104 24198 0.406 0.453 0.000

04/17/95 106 11302.2 0.000 0.346 0.103

04/20/95 109 6517.5 0.552 0.000 0.000

04/21/95 110 6741 0.460 0.413 0.000

04/22/95 111 11074 0.380 0.364 0.000

04/24/95 113 11968.7 0.442 0.000 0.000

04/28/95 117 4376.4 0.430 0.364 0.000

05/01/95 120 2399.2 0.404 0.306 0.000

05/05/95 124 1818.1 0.645 0.409 0.000

05/08/95 127 1410.9 0.388 0.000 0.000

05/12/95 131 3195.1 0.000 1.372 0.158

05/15/95 134 2201.6 0.205 0.000 0.000

05/17/95 136 1866.3 0.337 0.000 0.000

05/19/95 138 2484.8 0.833 0.577 0.000

05/21/95 140 7007 1.154 0.529 0.000

05/22/95 141 6332 1.032 1.085 0.000

05/23/95 142 4193.2 3.828 2.459 0.000

05/24/95 143 3924.7 6.042 2.704 0.000

05/25/95 144 11506 6.716 2.288 0.000
Page 5 of 96

Date Julian 

Day

Flow (cfs) Atrazine 

(ug/L)

Acetochlor 

(ug/L)

Chlorpyrifos 

(ug/L)

05/26/95 145 13764 4.984 3.414 0.000

05/27/95 146 10700 8.510 3.958 0.000

05/28/95 147 7712.4 10.740 3.891 0.000

05/29/95 148 9301.8 14.058 3.866 0.000

05/29/95 148 8771 12.306 3.790 0.000

05/30/95 149 8240.1 11.863 3.344 0.000

05/30/95 149 7473 11.122 3.141 0.000

05/30/95 149 7450.2 10.597 3.276 0.000

05/31/95 150 6817 9.857 3.079 0.000

06/01/95 151 5483.6 8.850 2.883 0.000

06/02/95 152 4163 6.336 2.390 0.000

06/03/95 153 3664.6 5.665 2.737 0.000

06/04/95 154 3357.7 4.701 0.314 0.000

06/06/95 156 3439 4.786 0.627 0.000

06/07/95 157 2742.2 4.448 0.552 0.000

06/08/95 158 2512.2 4.322 0.521 0.000

06/09/95 159 1818.1 3.891 0.469 0.000

06/10/95 160 3779.2 3.429 0.413 0.000

06/11/95 161 4345.2 5.094 0.519 0.000

06/12/95 162 5113 4.612 0.481 0.000

06/19/95 169 1730.2 7.417 0.344 0.000

06/23/95 173 817.8 7.930 0.544 0.000

06/26/95 176 692.3 7.606 0.576 0.000

06/28/95 178 14874 3.631 0.323 0.000

06/29/95 179 16650 4.062 0.385 0.000

06/29/95 179 16766 3.985 0.354 0.000

06/29/95 179 16708 2.699 0.000 0.000

06/30/95 180 16940 2.776 0.000 0.000

06/30/95 180 17172 3.401 0.419 0.000

07/01/95 181 15922 3.215 0.000 0.000

07/02/95 182 11890 3.160 0.337 0.000

07/03/95 183 9258.1 3.720 0.000 0.000

07/04/95 184 7632.6 4.463 0.368 0.000

07/06/95 186 4253.6 4.427 0.356 0.000

07/08/95 188 1794 4.141 0.328 0.125

07/14/95 194 745.1 1.250 0.378 0.000

07/17/95 197 554.7 3.874 0.000 0.000

07/21/95 201 516 2.992 0.000 0.000

07/24/95 204 408.3 2.912 0.000 0.000

07/29/95 209 1133.8 1.993 0.000 0.000

07/31/95 211 1009.7 1.262 0.000 0.000
Page 6 of 96

Date Julian 

Day

Flow (cfs) Atrazine 

(ug/L)

Acetochlor 

(ug/L)

Chlorpyrifos 

(ug/L)

08/04/95 215 832.3 1.944 0.000 0.000

08/07/95 218 2554.4 1.892 0.000 0.000

08/12/95 223 2539.6 1.462 0.000 0.000

08/14/95 225 1463 0.952 0.000 0.000

08/18/95 229 1486.4 0.970 0.000 0.000

08/21/95 232 3350.1 0.831 0.000 0.000

08/25/95 236 912.7 0.802 0.000 0.000

08/28/95 239 498.3 0.852 0.000 0.000

09/01/95 243 328.6 0.900 0.000 0.000

09/04/95 246 261.8 0.738 0.000 0.000

09/08/95 250 160.5 0.978 0.848 0.000

09/11/95 253 515.7 0.872 0.000 0.000

09/15/95 257 149.5 1.068 0.000 0.000

09/18/95 260 165.4 0.850 0.000 0.000

09/22/95 264 287.9 0.707 0.000 0.000

09/25/95 267 118.4 0.777 0.000 0.000

10/07/95 279 2889.9 0.515 0.000 0.000

10/09/95 281 3075.3 0.493 0.000 0.000

10/13/95 285 1063.4 0.399 0.000 0.000

10/16/95 288 730.6 0.303 0.000 0.000

10/20/95 292 327.7 0.350 0.000 0.000

10/23/95 295 136.5 0.334 0.000 0.000

10/27/95 299 354.3 0.387 0.000 0.000

10/30/95 302 594.1 0.388 0.000 0.000

10/30/95 302 603.1 0.289 0.000 0.000

11/03/95 306 2347.8 0.303 0.000 0.000

11/06/95 309 2573.1 0.265 0.000 0.000

11/11/95 314 4132.8 0.311 0.000 0.000

11/11/95 314 7045 0.417 0.000 0.000

11/12/95 315 7632.6 0.339 0.000 0.000

11/12/95 315 8035.6 0.333 0.000 0.000

11/13/95 316 9874.3 0.317 0.000 0.000

11/13/95 316 9831.6 0.172 0.000 0.000

11/14/95 317 7994.7 0.151 0.000 0.000

11/15/95 318 6115.4 0.167 0.000 0.000

11/16/95 319 5013.4 0.169 0.000 0.000

11/17/95 320 3357.7 0.168 0.000 0.000

11/20/95 323 3579.1 0.143 0.000 0.000

11/27/95 330 1638.6 0.176 0.000 0.000

12/04/95 337 912.7 0.338 0.000 0.000
Page 7 of 96

D.2.2 AEEMP 2007 MO-01

Source: See USEPA (2007 and 2009a) for site/study description and data set.

DATE Atrazine 

Grab 

Sample, 

ug/L

Atrazine 

AutoSample, 

ug/L

Atrazine 

Combined 

Samples, 

ug/L

Daily 

Precipitation 

(cm)

Avg Daily 

Flow (m3/s)

4/1/2007 0.051

4/2/2007 <0.05 <0.05 0.05 0.102 0.00086

4/3/2007 <0.05 0.05 1.727 1.08462

4/4/2007 <0.05 0.05 0.000 0.06207

4/5/2007 <0.05 0.05 0.000 0.00039

4/6/2007 <0.05 0.05 0.05 0.000 0.00027

4/7/2007 0.05 0.000 0.00024

4/8/2007 0.05 0.000 0.00024

4/9/2007 0.05 0.000 0.00023

4/10/2007 <0.05 <0.05 0.05 1.092 0.00023

4/11/2007 <0.05 0.05 1.448 0.41993

4/12/2007 <0.05 0.05 0.000 0.02044

4/13/2007 <0.05 0.05 0.000 0.00030

4/14/2007 <0.05 <0.05 0.05 0.356 0.00027

4/15/2007 0.05 0.000 0.00026

4/16/2007 0.05 0.000 0.00026

4/17/2007 0.05 0.000 0.00024

4/18/2007 <0.05 <0.05 0.05 0.000 0.00023

4/19/2007 0.05 0.000 0.00022

4/20/2007 0.05 0.000 0.00021

4/21/2007 <0.05 0.05 0.000 0.00021

4/22/2007 <0.05 0.05 0.152 0.00020

4/23/2007 0.05 0.000 0.00020

4/24/2007 0.05 1.956 0.00020

4/25/2007 0.05 0.279 0.07109

4/26/2007 7.18 29.72 29.72 0.965 0.47783

4/27/2007 23.01 23.01 0.000 0.00456

4/28/2007 22.00 22.00 0.000 0.00026

4/29/2007 23.80 23.80 0.000 0.00025

4/30/2007 23.21 15.23 23.21 0.000 0.00024

5/1/2007 23.21 0.254 0.00022

5/2/2007 23.21 0.229 0.00021

5/3/2007 23.21 0.813 0.00022

5/4/2007 0.79 16.46 16.46 1.702 0.56913

5/5/2007 14.26 14.26 0.000 0.00378

5/6/2007 8.14 8.14 1.829 0.43706
Page 8 of 96

DATE Atrazine 

Grab 

Sample, 

ug/L

Atrazine 

AutoSample, 

ug/L

Atrazine 

Combined 

Samples, 

ug/L

Daily 

Precipitation 

(cm)

Avg Daily 

Flow (m3/s)

5/7/2007 6.23 6.23 0.000 0.47543

5/8/2007 4.34 4.42 4.42 0.000 0.00049

5/9/2007 4.58 4.58 0.000 0.00027

5/10/2007 3.66 3.66 0.203 0.00024

5/11/2007 2.71 2.71 0.000 0.00022

5/12/2007 2.20 2.30 2.30 0.000 0.00021

5/13/2007 2.30 0.000 0.00019

5/14/2007 2.30 0.000 0.00019

5/15/2007 2.30 1.753 0.00019

5/16/2007 1.57 1.33 1.57 0.000 0.00018

5/17/2007 1.57 0.000 0.00018

5/18/2007 1.57 0.000 0.00017

5/19/2007 1.57 0.000 0.00017

5/20/2007 1.17 1.07 1.17 0.000 0.00017

5/21/2007 1.17 0.000 0.00016

5/22/2007 1.17 0.000 0.00016

5/23/2007 1.17 0.000 0.00016

5/24/2007 0.90 0.97 0.97 0.254 0.00016

5/25/2007 0.97 0.051 0.00016

5/26/2007 0.97 0.051 0.00017

5/27/2007 0.97 0.483 0.00017

5/28/2007 0.89 1.07 1.07 0.025 0.00017

5/29/2007 1.07 0.000 0.00016

5/30/2007 1.08 1.08 0.127 0.00016

5/31/2007 1.31 1.31 0.889 0.00018

6/1/2007 2.75 9.62 9.62 0.025 0.00023

6/2/2007 91.60 91.60 0.178 0.00017

6/3/2007 71.80 71.80 0.000 0.00016

6/4/2007 42.00 42.00 0.000 0.00016

6/5/2007 33.29 27.00 33.29 0.000 0.00015

6/6/2007 26.20 26.20 0.000 0.00015

6/7/2007 18.96 18.96 0.051 0.00014

6/8/2007 17.22 17.22 0.051 0.00014

6/9/2007 17.56 15.16 17.56 0.000 0.00014

6/10/2007 12.12 12.12 0.381 0.00014

6/11/2007 11.80 11.80 0.000 0.00015

6/12/2007 9.97 9.97 0.000 0.00014

6/13/2007 7.90 5.64 7.90 0.000 0.00014

6/14/2007 7.90 0.000 0.00014

6/15/2007 7.90 0.000 0.00014
Page 9 of 96

DATE Atrazine 

Grab 

Sample, 

ug/L

Atrazine 

AutoSample, 

ug/L

Atrazine 

Combined 

Samples, 

ug/L

Daily 

Precipitation 

(cm)

Avg Daily 

Flow (m3/s)

6/16/2007 7.90 0.000 0.00013

6/17/2007 2.98 2.42 2.98 0.000 0.00013

6/18/2007 2.98 1.549 0.00013

6/19/2007 2.98 0.051 0.00016

6/20/2007 2.98 0.000 0.00016

6/21/2007 2.58 2.28 2.58 0.000 0.00014

6/22/2007 2.58 0.356 0.00014

6/23/2007 2.58 0.559 0.00015

6/24/2007 2.58 0.000 0.00014

6/25/2007 1.54 2.35 2.35 0.203 0.00014

6/26/2007 2.35 0.000 0.00016

6/27/2007 1.31 1.31 0.483 0.00015

6/28/2007 1.31 1.245 0.00016

6/29/2007 1.48 1.85 1.85 0.000 0.00016

6/30/2007 1.52 1.52 0.000 0.00015

7/1/2007 1.04 1.04 0.000 0.00015

7/2/2007 1.18 1.18 0.000 0.00014

7/3/2007 1.33 0.72 1.33 0.000 0.00013

7/4/2007 1.33 0.000 0.00013

7/5/2007 1.33 0.000 0.00013

7/6/2007 1.33 0.000 0.00012

7/7/2007 0.70 0.72 0.72 0.000 0.00011

7/8/2007 0.72 0.000 0.00010

7/9/2007 1.04 1.04 0.000 0.00008

7/10/2007 1.04 2.108 0.00010

7/11/2007 0.50 0.67 0.67 0.000 0.00014

7/12/2007 0.67 0.000 0.00012

7/13/2007 0.67 0.000 0.00012

7/14/2007 0.67 1.092 0.00011

7/15/2007 0.48 0.57 0.57 0.000 0.00015

7/16/2007 0.57 0.203 0.00013

7/17/2007 0.57 0.000 0.00013

7/18/2007 0.57 0.000 0.00012

7/19/2007 ns 0.78 0.78 0.025 0.00011

7/20/2007 0.78 0.000 0.00010

7/21/2007 0.78 0.000 0.00008

7/22/2007 0.78 0.000 0.00006

7/23/2007 ns 0.78 0.000 0.00004

7/24/2007 0.78 0.000 0.00001

7/25/2007 0.78 0.000 0.00002
Page 10 of 96

DATE Atrazine 

Grab 

Sample, 

ug/L

Atrazine 

AutoSample, 

ug/L

Atrazine 

Combined 

Samples, 

ug/L

Daily 

Precipitation 

(cm)

Avg Daily 

Flow (m3/s)

7/26/2007 0.78 0.000 0.00002

7/27/2007 ns 0.78 0.686 0.00005

7/28/2007 0.78 0.000 0.00002

7/29/2007 0.78 0.000 0.00001

7/30/2007 0.78 0.000 0.00002

7/31/2007 ns 0.78 0.000 0.00002

8/1/2007 0.78 0.000 0.00002

8/2/2007 0.78 0.000 0.00002

8/3/2007 0.78 0.000 0.00002

8/4/2007 ns 0.78 0.000 0.00002

8/5/2007 0.78 0.000 0.00002

8/6/2007 0.78 0.102 0.00001

8/7/2007 0.78 0.102 0.00001

8/8/2007 ns 0.78 2.489 0.00007

8/9/2007 0.78 0.127 0.00006

8/10/2007 0.78 0.000 0.00003

8/11/2007 0.78 0.000 0.00001

8/12/2007 ns 0.78 0.432 0.00001

8/13/2007 0.78 1.372 0.00006

8/14/2007 0.27 0.27 0.000 0.00010

8/15/2007 0.27 0.000 0.00008

8/16/2007 ns 0.16 0.16 0.610 0.00008

8/17/2007 0.16 0.000 0.00008

8/18/2007 0.16 0.000 0.00006

8/19/2007 0.16 0.025 0.00002

8/20/2007 ns 0.16 0.178 0.00001

8/21/2007 0.38 0.38 3.327 0.00018

8/22/2007 0.38 0.889 0.00017

8/23/2007 0.38 0.000 0.00015

8/24/2007 0.21 0.26 0.26 2.896 0.00017

8/25/2007 0.26 0.152 0.00019

8/26/2007 0.26 0.000 0.00015

8/27/2007 0.26 0.000 0.00014

8/28/2007 0.19 0.22 0.22 0.000 0.00013

Appendix D.2. Geostatistical Analysis and Conditional Stochastic Simulation 

Application
Page 11 of 96

D.2.1 Introduction

Pesticide concentrations in surface water change over space and time as a result of 

numerous factors, including hydrology, meteorology, agronomic practices, pesticide 

application rates, application timing, application method, and environmental fate 

properties of the pesticide. The quantitative analysis of pesticide monitoring data is an 

important aspect in assessing exposure to humans and wildlife and needs to consider 

the influence of space and time. The analysis of changes in pesticide concentrations in 

surface water monitoring data with time requires an integration of factors noted above 

plus an understanding of the sampling strategy employed. There are few monitoring 

programs with daily monitoring of concentrations of pesticides and even fewer that have 

been conducted over several years. Heidelberg University’s National Center for Water 

Quality Research (NCWQR) surface water monitoring program is an example of an 

intensive monitoring program for pesticides (NCWQR, 2010). Surface water monitoring 

programs, however, generally have sampling frequencies ranging from biweekly to 

monthly (USGS NAWQA and Blomquist et al., 2001). The ability to predict a complete 

time series of pesticide concentrations in surface water from surface monitoring data 

with different sampling frequencies is especially important when trying to understand 

pesticide exposure where monitoring is infrequent.

Historically, in the pesticide registration process, deterministic models such as PRZM 

(Suárez, 2005) and EXAMS (Burns, 2002) have been used to estimate pesticide 

concentrations in surface water. More recently, the USGS Watershed Regression on 

Pesticides (WARP) model has been developed for estimating a distribution of atrazine 

concentrations in specific watersheds. This model requires watershed specific data on 

the atrazine use intensity in the watershed, meteorology (May and June precipitation 

and rainfall intensity factor), soil properties (soil erodibility factor), hydrology (percent of 

runoff resulting from Dunne overland flow), and watershed size (Stone and Gilliom, 

2009). Although the WARP model has been used for assessing vulnerability of 

watersheds (USEPA 2007, 2009a and 2009b), it has not been used in the pesticide 

registration process to predict concentrations of atrazine in untreated drinking water. 

The success of the use of modeling data for assessing monitoring data is dependent on 

the availability of temporally and spatially explicit watershed specific data (USEPA, 

2009a, 2010a and 2010b). 

Another approach recommended by FIFRA SAP is the use of statistical modeling 

approaches for predicting pesticide time series (FIFRA SAP, 2010b). Geostatistical 

procedures such as kriging have been recommended as an approach for interpolating 

pesticide monitoring data in a time series. This modeling approach is based on 

selection of a stationary random function model to describe the relationship of variance 
Page 12 of 96

as a function of distance or time (Isaaks and Srivastava, 1989). This relationship can be 

quantified using a variogram, correlogram, or a covariance function. This type of 

nearest-neighbor modeling approach assumes that properties (e.g., pesticide 

concentration) in close proximity in space or time are more similar than properties with 

wide separations in distance or time. Application of a random function model to 

pesticide time series analysis generally requires a condition of stationarity (Isaaks and 

Srivastava, 1989), which assumes that an estimate has an equal probability of occurring 

regardless of location in space and time. Although this condition may not be applicable 

for an annual time-series of pesticide concentration due to seasonal trends in 

meteorology, pesticide persistence, pesticide application timing, and hydrology, it may 

be applicable to specific time periods during the pesticide application period. 

Time point estimation of pesticide concentrations in a time series can be accomplished 

through nearest neighbor approaches, including local sample mean, polygons, 

triangulation, and inverse distance squared methods. Another point estimation 

approach is ordinary kriging (Isaaks and Srivastava, 1989). This technique provides 

point estimates based on the best linear unbiased estimator (B.L.U.E).

Uncertainty analysis of predicted pesticide concentrations in the time series can be 

evaluated using the absolute error estimate, standard deviation from kriging, and 

conditional stochastic simulation (Isaaks and Srivastava, 1989; Deutsch and Journel, 

1998). Errors in prediction can be associated with the number of samples, proximity of 

available samples and their temporal or spatial arrangement, and the nature of the 

phenomena. 

The objective of this appendix is to assess the application of ordinary kriging on the 

interpolation of pesticide concentrations in various times series with different sampling 

frequencies. Although this exercise is directed toward atrazine, the analysis will also 

consider the applicability of this approach to other pesticides (chlorpyrifos and 

acetochlor) with different time series profiles.

D.2.2 Material and Methods

The time series data considered in this analysis was taken from the Atrazine Ecological 

Exposure Monitoring Program (AEEMP) and the Heidelberg University National Center 

for Water Quality Research (NCWQR) surface water monitoring program (Table D-1). 

The data were selected to provide an evaluation of the technique for different years and 

hydrology. Although both monitoring programs have high sampling frequencies, they 

generally differ in watersheds, stream flow characteristics, and sample collection 

methodology. Grab samples were collected a the Missouri site (MO-01-2007) every 
Page 13 of 96

four days from April 5, 2007 to July 15, 2007 (Julian Days 93 to 197). Additionally, 

samples were taken with an autosampler to fill in days between the grab sample events. 

The case study analysis will focus on both grab samples and a combined time series 

from grab and autosamples. In contrast, high intensity sampling in the NCWQR was 

focused during the high runoff period from April to July (Julian Days 100 to 200). 

Table D- 1: Surface Water Monitoring Data For Case Studies

Program Station 

Name Year Sampling Frequency

Detection 

Limit

(ug/L)

Watershed 

Size

(km2)

Average

Daily

Flow

m3/sec

AEEMP MO-01-

2007

2007 4-day and daily 

autosamplers 0.05 30.1 0.02 

NCWQR Maumee 

River

1995 Daily 0 16,995 106.1431

NCWQR Sandusky 

River

1995 Daily 0 3,678 291

1- Annual average flow 

Descriptive statistics for the available monitoring data are shown in Table D-2. 

Table D- 2: Descriptive Statistics of Heidelberg University NCWQR (Maumee 1995) and 

Atrazine Ecological Exposure Monitoring Data (MO-01-2007).

Statistic

Maumee 1995 MO-01-2007

Atrazine Acetochlor Chlorpyrifos Atrazine

Mean 1.89 0.43 0.00421 5.02

Standard Error 0.23 0.07 0.00191 0.96

Median 0.40 0.00 0.00000 0.78

Standard Deviation 2.89 0.94 0.02389 11.76

Sample Variance 8.34 0.88 0.00057 138.22

Kurtosis 4.38 5.85 34.16 26.42

Skewness 2.16 2.63 5.84 4.58

Minimum 0.00 0.00 0.00000 0.05

Maximum 14.06 3.96 0.17600 91.60

To address the stationarity issue associated with seasonality of pesticide monitoring 

data, a subsection of the monitoring data was selected to encompass the earliest corn 

planting date to approximately 100 days post corn planting date. This time series period 

represents the period of time with the highest magnitude and frequency of atrazine 

detections. The time series sections from Julian Day 0 to the earliest corn planting date 

(approximately Day 101) and from Day 200 to Day 365 are expected to represent baseflow concentrations of atrazine. Figure D-1 illustrates two distinct populations of 

atrazine pesticide concentrations dominated by either runoff or base-flow conditions. In 

contrast, the random nature of the chlorpyrifos data does not represent a clear 
Page 14 of 96

difference in runoff and base-flow concentrations within an annual time series (Figure 

1). 

Figure D- 1: Times Series Analysis of Base-Flow and Runoff Dominated Processes for 

Atrazine (left) and Chlorpyrifos (right) Concentrations in Surface Water (Maumee, 1995).

Additionally, the missing monitoring data for the Maumee River was estimated using 

kriging. The MO-01-2007 data set did not require data estimation because a complete 

daily time series could be constructed from the grab and autosampler samples. The 

descriptive statistics of the actual time series used in the variogram analysis are shown 

in Table D-3.

Table D- 3: Descriptive Statistics of Atrazine Kriged Data for Maumee River 1995 (100-200 

days) and Actual Monitoring Data for MO-01-2007 (91-190 days)

Statistics Maumee River MO-01-2007

Mean 3.85 7.13

Standard Error 0.46 1.37

Median 3.83 1.57

Standard Deviation 3.38 13.81

Sample Variance 11.45 190.64

Kurtosis 0.60 17.76

Skewness 0.93 3.76

Minimum 0 0.05

Maximum 14.06 91.60

Some adjustments to standard geostatistical analysis is needed to address limitations of 

the tools used to make the analysis appropriate for modeling time series of pesticide 

concentrations (Figure D-2). An important consideration is the number of samples in 

the time series. In preliminary analyses, variogram modeling and kriging were difficult 

for data sets with less than 15 data points as there were poor fits to the variograms. 

Additionally, the standard deviations from kriging for predicted daily pesticide 
Page 15 of 96

concentrations were relatively large. In general, sampling frequencies of 14-day and 

28-day intervals provided the poorest variogram models. In the absence of more 

extensive monitoring data, a data infilling process is needed. Soft data may be used to 

allow additional refinement in prediction of pesticide concentrations in the time series. 

This infilling process needs to consider the descriptive statistics of the underlying data. 

It should also consider the temporal correlation of atrazine concentrations in the time 

series. The soft data, such as flow or acetochlor concentrations, can be used to predict 

atrazine concentrations using established regression equations. Additionally, 

regression models such as WARP can be used to provide an underlying distribution of 

percentiles in the time series, provided the underlying pesticide use and watershed 

characteristics are available. Merging the WARP results with existing monitoring data 

allows the estimation of a new distribution of values. One option is to fit the merged 

WARP and available monitoring data to a known distribution. Another option is to use 

the merged WARP percentiles and the available monitoring data without additional 

estimation. The merged data need to be assembled in the proper sequence in the time 

series. Matching percentiles of the merged data with percentiles of flow data may 

provide a reliable estimate for the sequence in the time series. Matching percentiles 

requires an a priori understanding of the relationship between flow and pesticide 

concentration. The autocorrelation of daily pesticide concentrations can be described 

using the variogram. This variogram is used to generate a kriged time series. 

Uncertainty bounds for estimated daily atrazine concentrations can then be generated 

using sequential stochastic simulation. 
Page 16 of 96

Figure D- 2: Proposed Analysis Strategy for Time Series Modeling of Pesticide 

Concentrations from Monitoring Data.

The software programs used in this analysis include GEOEAS (USEPA Version 1.2.1), 

EXCEL(2007), SIGMAPLOT(Version 10), SGSIM (SGSIM Version 2, Deutsch and 

Journel, 1998), and WARP (Stone and Gilliom, 2009). Below is a description of the 

procedure used to generate time series from monitoring data.

Construct a GEOEAS input file (ADDENDUM).

Use GEOEAS (STATS) to evaluate the descriptive statistics. Evaluate the need 

for log transformation to produce a normal distribution.

Use GEOEAS (PREVAR) to produce a covariance matrix of temporal lag pairs.

Use GEOEAS (VARIO) to model variogram. Fit the best variogram to predict the 

model, nugget, range, and sill. 

Use GEOEAS (KRIGE) to generate infill the time series using the BLUE. Kriging 

parameters included type of kriging, grid parameter (x,y,z), and search 

parameters. We used ordinary point kriging and the variogram model. Also, the 
Page 17 of 96

grid cell has been modified to allow a one dimensional kriging (APPENDIX). 

Output files for kriged time series were saved as *.grd file.

The kriged time series is then prepared for conditional, Gaussian sequential 

stochastic simulation (SGSIM). This procedure allows generation of equallyprobable realizations with same covariance temporal structure and descriptive 

statistics as the kriged time series The stochastic simulation was conditioned on 

the actual monitoring data used for variogram modeling and kriging. For our 

purposes, 1,000 realizations were generated for each time series. An example 

input and parameter file is shown the ADDENDUM.

D.2.3 Variogram Modeling and Kriging

Variogram analysis and kriging were conducted on the Maumee River 1995 and 

Sandusky 1995 data from the NCWQR and MO-01-2007 data from the AEEMP. These 

data were selected to evaluate the capability of the geostatistical tools for different 

pesticides and watersheds. Analyses for other sites and pesticides are shown in the 

tables and figures at the end of this document. In order to provide a base-line time 

series for error analysis as well as a complete time series for assessing sampling 

frequency, standard time series were adopted from the available monitoring data. The 

standard time series were developed by kriging (Maumee River 1995) or actual 

monitoring data (MO-01-2007). These standard time series are assumed to be 

representative of the true daily time series. 

Table D-4 shows the parameters for the fitted variogram models for the 1995 Maumee 

and MO-01-2007 data for different sampling frequencies. The variogram models were 

spherical or gaussian. Nugget models were identified for most sampling frequencies. 

The variance of the nugget ranged from 0.0 to 0.5 μg/L2, which corresponds to standard 

deviations of 0 to 0.7 μg/L. A nugget indicates a random process without temporal 

correlation . It may be associated with sampling error, sampling technique, or inherent 

variation in daily pesticide concentrations. The variance of the sill ranged from 2.9 to 

6.0 μg/L2, which corresponds to standard deviations of 1.73 to 2.44 μg/L. Although the 

extent of autocorrelation, as depicted by the range, was highly variable among the case 

study data sets, longer ranges (52-80 days) were found in the 1995 Maumee River 

data. This phenomenon may be associated with the larger watershed size and daily flow 

characteristics. Figure D-3 shows variograms for the 4-day and 7-day sampling 

frequency for the Maumee and MO-01 2007 data sets.
Page 18 of 96

Table D- 4: Variogram Models using the Maumee River 1995 Time Series (100-200 days) 

and Missouri Ecological Monitoring Site (MO-01 2007).

Data Sampling 

Frequency

N Nugget

(μg/L2)

Model1 Sill

(μg/L2)

Range

(Days)

Maumee95 Actual Data 100 0.10 Spherical 0.97 16

4-day 25 0.00 Spherical 3.70 52

7-day 15 0.00 Spherical 3.20 61

14-day 8 0.30 Gaussian 3.20 80

28-day 4 2.2 no temporal correlation

MO-01 2007

Complete 

Time Series

4-day 25 0.00 Spherical 3.60 27

7 – day 15 0.00 Spherical 3.80 27

14 –day 8 0.50 Spherical 3.80 32

28- day 4 3.80 no temporal correlation

MO-01 2007

Grab 

samples

4-day2 27 0.2 Spherical 4.0 27

7 – day3 15 0.2 Gaussian 2.9 25

8 – day2 15 0.5 Spherical 3.5 33

14 –day3 8 0.3 Spherical 6.0 30

16- day2 7 0.1 Linear 6.0 41

16- day2,4 6 0.2 Gaussian 3.6 45 1 Grab samples collected every 4 days from 04/01/07 to 07/19/07; to additional samples were collected on 

8/24/07 and 8/28/07 and are not included.

2 Grab samples: 4, 8, and 16 day sampling interval.

3 Estimated from kriged samples.

4 Highest data value removed to assess impact on variogram model

 1 Variogram models provide a description of variance as a function of lag distance or lag time intervals. The models 

used in this analysis include Gaussian and Spherical. The Spherical model is defined as the variance at 

lag(h)=C*(1.5*h/a-0.58(h/a)3

)+nugget. The Gaussian model is defined as the variance at lag(h)=C*(1-exp(-

(3h)2

/a2)+nugget. This model does not represent the Gaussian distribution model. The nugget model represents 

inherent variance or “white noise” at lag distances or lag times shorter than described in the variogram model. 

Parameters in the variogram model are h=lag time or lag distance, C= sill=variance for randon process (ug/L2

), 

a=range= in days or distance for correlation of variance. 
Page 19 of 96

Lag Time (Days)

0 10 20 30 40 50 60 70

Variance (ug/L2) 0

1

2

3

4

Spherical

Nugget=0 ug/L2

Sill=3.7 ug/L2

Range=52 days

a.)

Lag Time (Days)

0 20 40 60 80 100

Variance (ug/L2) 0

1

2

3

4

Spherical

Nugget=0.00 ug/L2

Sill=3.20 ug/L2

Range=61 days

b.)

Lag Time (Days)

0 10 20 30 40 50 60

Variance (ug/L2) 0

1

2

3

4

5

6

7

Spherical

Nugget=0.20 ug/L2

Sill=4.0 ug/L2

Range=27 days

c.)

Lag Time (Days)

0 20 40 60 80 100

0

1

2

3

4

5

6

Variance (ug/L2

)

Gaussain

Nugget=0.20 ug/L2

Sill=2.9 ug/L2

Range=25 days

d.)

Figure D- 3: Representative Variograms for 4-day and 7-day Sampling Frequency for the 

Maumee and MO-01 2007 data: a.) 4-day 1995 Maumee; b.)7-day 1995 Maumee; c.) 4-day 

Grab MO-01 2007; and d.) 7-day Grab MO-01 2007. 

The kriged time series for the Maumee River for the actual data, 4-day, and 7-day 

sampling frequencies exhibit similar descriptive statistics (mean, median, variance) for 

atrazine concentrations (Table D-5). One notable difference is the estimated maximum 

atrazine concentration. The 4-day sampling frequency captured the maximum daily 

atrazine concentration of 14.06 μg/L. In contrast, the 7-day and 14-day sampling 

interval missed the peak atrazine concentration. The 14-day and 28-day kriged time 

series were not predicted because of the inability to estimate a reliable variogram. 
Page 20 of 96

Table D- 5: Descriptive Statistics from Kriged 1995 Maumee River Time Series (100-200 

days).

Statistics Actual 4-day 7-day

Mean 3.85 3.46 3.55

Median 3.88 2.86 3.41

Minimum 0.00 0.03 0.23

Maximum 14.06 14.06 11.86

Standard Deviation 3.88 3.27 3.04

Sample Variance 11.45 10.72 9.26

Kurtosis 0.60 0.39 -0.50

Skewness 0.93 0.94 0.57

The estimation of missing data in the 4-day and 7-day kriged time series provide 

reasonable prediction of missing data in the time series (Figures D-4 and D-5). There 

is a clear linear relationship between the predicted atrazine concentration and actual 

atrazine concentrations. Additionally, the kriging standard deviations for the missing 

data points are low, which further suggest a reasonable prediction of estimated daily 

atrazine concentrations. 
Page 21 of 96

Actual Atrazine Concentration (ug/L)

0 2 4 6 8 10 12 14 16

Predicted Atrazine Concentration (ug/L)

0

2

4

6

8

10

12

14

16

Julian Date (Days)

80 100 120 140 160 180 200 220

Atrazine Concentration (ug/L)

0

2

4

6

8

10

12

14

16

Figure D- 4: Prediction Error in the Kriged Atrazine Time Series from 4-day Sampling of 

the Maumee River 1995 (Days 100-200).
Page 22 of 96

0 2 4 6 8 10 12 14 16

Predicted Atrazine Concentration (ug/L)

0

2

4

6

8

10

12

14

16

Actual Atrazine Concentration (ug/L)

Julian Date (Days)

80 100 120 140 160 180 200 220

Atrazine Concentration (ug/L)

0

2

4

6

8

10

12

14

Figure D- 5: Prediction Error in the Kriged Atrazine Time Series from 7-day Sampling of 

the Maumee River 1995 (Days 100-200).

The kriged time series for MO-0-2007 based on daily samples had higher mean, 

maximum, and variance when compared to the kriged time series for MO-01-2007 

based on the 4-day grab samples (Table D-6). This difference can be attributed to 

capturing the higher maximum concentration from the daily series. Unlike the Maumee 

kriged time series, the maximum atrazine concentration was highest for the 7-day 

sampling interval. The 4-day sampling frequency had a maximum daily atrazine 

concentration of 33.34 μg/L. In contrast, the 7-day and 14-day grab samples had a 

maximum daily atrazine concentration of 23 μg/L. The actual peak, however, was 91.6 

μg/L. This difference illustrates the difficulty of capturing the peak without continuous 

monitoring. 
Page 23 of 96

Table D- 6: Descriptive Statistics from Kriged MO-01-2007 Time Series (91-190 days)

Statistics

Daily Samples Grab Samples

Actual

4-day 7-day 4-day 7-day 14-day

Mean 6.46 6.63 4.59 4.34 5.21 7.13

Median 2.09 1.98 1.58 2.18 2.93 1.57

Minimum 0.05 0.54 0.05 -0.10 -1.25 0.05

Maximum 33.34 42.00 33.29 23.59 23.21 91.60

Standard Deviation 8.90 9.69 6.81 5.59 6.16 13.81

Sample Variance 79.26 93.80 46.41 31.28 37.93 190.64

Kurtosis 0.97 2.97 4.38 2.01 0.11 17.76

Skewness 1.50 1.88 2.17 1.75 1.01 3.76

Unlike the Maumee time series, the MO-01-2007 time series had numerous distinct 

atrazine peaks. Although the time series data show two prominent atrazine peaks, the 

magnitude and duration of peaks varied according to sampling frequency (Figures D-6 

– D-10). Regardless of sampling design, the kriged 4-day time series had similar 

statistics to the meaured daily time series. Although the 7-day time series captured 

both atrazine peaks, there was a lower magnitude in atrazine concentrations for the 

grab samples. This phenomenon may illustrate the importance of formulating the time 

series on data rather than kriged results. The kriged time series for the14- day sampling 

frequency did not adequately capture the second atrazine peak. In all cases, the kriging 

standard deviations for estimate daily atrazine concentrations were low. 

Julian Day

100 120 140 160 180

Atrazine Concentration (ug/L)

0

5

10

15

20

25

30

35

Figure D- 6: Prediction Error in the Atrazine Time Series from 4-day sampling of 

the MO-01-2007 (Days 91-190).
Page 24 of 96

Figure D- 7: Prediction Error in the Kriged Atrazine Time Series from 4-day Sampling of 

the MO-01-2007 (Days 91-190).

Julian Day

100 120 140 160 180

Atrazine Concentration (ug/L)

0

10

20

30

40

50

Figure D- 8: Prediction Error in the Atrazine Time Series from 7-day Sampling of the MO01-2007 (Days 91-190)

Julian Date (Days)

80 100 120 140 160 180 200

-5

0

5

10

15

20

25

30

35
Page 25 of 96

Julian Date (Days)

80 100 120 140 160 180 200

Atrazine Concentration (ug/L)

-5

0

5

10

15

20

25

Figure D- 9: Prediction Error in the Kriged Atrazine Time Series from 7-day Sampling of 

the MO-01-2007 (Days 91-190)

Figure D- 10: Prediction Error in the Kriged Atrazine Time Series from 14-day Sampling of 

the MO-01-2007 (Days 91-190)

D.2.4 Conditional Stochastic Simulations 

Conditional stochastic simulations were conducted to assess the uncertainty associated 

with estimated daily concentrations. This procedure allows generation of equallyprobable realizations of time series with the same temporal autocorrelation and 

Julian Date (Days)

80 100 120 140 160 180 200

-5

0

5

10

15

20

25
Page 26 of 96

descriptive statistics as the kriged time series. In our analysis, 1,000 realizations were 

generated for each time series. A subsampling of 1000 realizations of 4-day and 7-day 

sampling derived time series provide a reasonable description of temporal behavior and 

descriptive statistics of atrazine concentration for the Maumee 1995 site. (Tables D-7 –

D-12 and Figures D-11 – D-16). The conditional simulation for the MO-01-2007 site 

provides reasonable approximation of temporal occurrence of atrazine concentrations. 

The peak concentration, however, is vastly underestimated from the actual data. 

Table D- 7: Descriptive Statistics of 10 Realizations from Conditional Stochastic 

Sequential Simulations of the 4-day Kriged Atrazine Time Series (Days 100-200) from the 

Maumee River (1995).

Statistic

Realization

1 2 3 4 5 6 7 8 9 10 Actual

Mean 3.47 3.59 3.42 3.35 3.51 3.39 3.56 3.37 3.33 3.35 3.85

SD 3.18 3.31 3.11 3.16 3.29 2.96 3.19 3.05 3.15 3.09 3.38

Max 14.00 14.00 14.00 14.00 14.00 14.00 14.00 14.00 14.00 14.00 14.06

Median 3.40 3.41 3.05 3.26 3.13 3.37 3.41 3.14 3.23 3.18 3.88

Min 0.03 0.03 0.03 0.02 0.03 0.03 0.03 0.03 0.03 0.03 0.00

Variance 10.02 10.85 9.57 9.90 10.69 8.65 10.05 9.20 9.84 9.47 11.45

Kurtosis -0.45 0.20 -0.38 0.10 0.57 0.51 0.27 0.08 0.71 0.00 0.60

Julian Day

100 120 140 160 180 200 220

Atrazine Conc (ug/L)

0

2

4

6

8

10

12

14

16

Estimate Mean (SD) 

 Actual Data 

Figure D- 11: Representative Realizations of Kriged Atrazine Time Series from 4-day 

Sampling of the Maumee River 1995 (Days 100-200).
Page 27 of 96

Table D- 8: Descriptive Statistics of 10 Realizations from Conditional Stochastic 

Sequential Simulation of the Actual 4-day Data in Kriged Atrazine Time Series (Days 91-

190) from the MO-01-2007.

Statistic

Realization

1 2 3 4 5 6 7 8 9 10 Actual

Mean 7.23 8.38 6.66 5.87 6.65 7.10 7.10 6.19 6.43 6.45 7.13

SD 10.29 10.96 10.30 9.07 9.82 10.17 10.17 10.61 9.68 9.37 13.81

Max 33.29 46.50 42.14 33.29 33.29 38.47 38.47 67.39 42.56 33.29 91.60

Median 2.24 2.35 1.50 1.76 2.19 1.49 1.49 1.90 1.78 1.84 1.57

Min 0.01 0.05 0.04 0.02 0.02 0.02 0.02 0.02 0.02 0.04 0.05

Variance 105.87 120.17 106.12 82.21 96.46 103.39 103.39 112.65 93.79 87.76 190.64

Kurtosis 0.76 0.70 1.88 2.33 0.95 0.86 0.86 11.11 2.41 1.21 17.76

Julian Day

100 120 140 160 180

Atrazine Concentration (ug/L)

0

20

40

60

80

100

Estimated Mean (SD) 

Actual Data 

Figure D- 12: Representative Realizations of Atrazine Time Series from 4-day sampling of 

the M0-01-2007 (Days 91-190).
Page 28 of 96

Table D- 9: Descriptive Statistics of 10 Realizations from Conditional Stochastic 

Sequential Simulation of the Grab 4-day Data in Kriged Atrazine Time Series (Days 91-

190) from the MO-01-2007.

Statistic

Realization

1 2 3 4 5 6 7 8 9 10 Actual

Mean 5.24 6.64 4.74 3.58 4.26 3.88 4.77 4.24 4.07 4.36 7.13

SD 8.43 9.65 8.64 6.08 7.08 6.09 7.71 7.87 7.40 7.36 13.81

Max 35.3 34.7 54.9 33.2 33.2 33.2 33.2 50.2 41.06 37.26 91.6

Median 1.55 2.25 1.49 1.55 1.53 1.53 1.49 1.40 1.36 1.51 1.57

Min 0.01 0.04 0.04 0.01 0.01 0.01 0.02 0.02 0.01 0.03 0.05

Variance 71.0 93.1 74.6 36.9 50.1 37.1 59.3 61.9 54.7 54.2 190.6

Kurtosis 3.38 1.87

12.5

1 9.10 6.07 7.25 4.94

13.4

2 10.16 6.19 17.76

Julian Date (Days)

80 100 120 140 160 180 200

Atrazine Concentration (ug/L)

0

20

40

60

80

100

Estimated Mean (SD) 

Actual Data 

Figure D- 13: Representative Realizations of Kriged Atrazine Time Series from 4-day Grab 

Sampling of the M0-01-2007 (Days 91-190).
Page 29 of 96

Table D- 10: Descriptive Statistics of 10 Realizations from Conditional Stochastic 

Sequential Simulation of the 7-day Kriged Atrazine Time Series (Days 100-200) from the 

Maumee River (1995).

Statistic

Realization

1 2 3 4 5 6 7 8 9 10 Actual

Mean 3.64 3.76 3.67 3.34 3.57 3.31 3.02 3.53 3.40 3.69 3.85

SD 3.13 3.05 3.30 2.79 3.03 2.99 2.75 3.09 3.12 3.37 3.38

Max 11.06 11.06 11.06 11.06 11.06 11.06 11.06 11.06 11.06 11.06 14.06

Median 4.06 4.30 3.82 4.12 4.20 3.92 2.54 3.94 3.96 3.98 3.88

Min 0.19 0.21 0.20 0.23 0.23 0.23 0.23 0.23 0.21 0.20 0.00

Variance 9.73 9.18 10.78 7.68 9.09 8.88 4.74 9.45 9.66 11.24 11.45

Kurtosis -1.12 -0.43 -0.54 -0.54 -0.69 -1.00 -0.39 -0.94 -0.37 -0.81 0.60

Julian Day

100 120 140 160 180 200 220

Atrazine Conc (ug/L)

0

2

4

6

8

10

12

14

16

Estimate Mean (SD) 

 Actual Data 

Figure D- 14: Representative Realizations of Kriged Atrazine Time Series from 7-Day 

Sampling of the Maumee River 1995 (Days 100-200).
Page 30 of 96

Table D- 11: Descriptive Statistics of 10 Realizations from Conditional Stochastic 

Sequential Simulation of the 7-Day Sample Data in Atrazine Time Series (Days 91-190) 

from the MO-01-2007.

Statistic

Realization

Actual 1 2 3 4 5 6 7 8 9 10

Mean 7.79 7.98 7.27 6.47 7.93 7.47 5.94 8.31 7.33 7.72 7.13

SD 11.69 12.60 10.63 10.99 12.75 13.15 10.08 12.19 13.34 13.02 13.81

Max 56.67 65.57 45.77 50.79 72.39 67.50 42.00 57.35 65.08 58.18 91.60

Median 1.92 2.34 1.41 2.31 2.35 1.28 1.18 2.33 2.30 2.23 1.57

Min 0.01 0.02 0.04 0.03 0.01 0.02 0.05 0.01 0.00 0.02 0.05

Variance

136.6

4

158.8

6

113.0

4

120.7

2

162.5

1

172.8

5

101.5

5

148.6

3

177.9

8

169.5

8 190.64

Kurtosis 3.84 5.12 1.94 5.44 7.33 5.85 4.21 3.84 5.66 3.46 17.76

Julian Day

100 120 140 160 180

Atrazine Concentration (ug/L)

0

20

40

60

80

100

Estimated Mean (SD)

Actual Data 

Figure D- 15: Representative Realizations of Kriged Atrazine Time Series from 7-Day 

Sampling of the M0-01-2007 (Days 91-190).
Page 31 of 96

Table D- 12: Descriptive Statistics of 10 Realizations from Conditional Stochastic 

Sequential Simulation of the 7-Day Kriged Atrazine Time Series (Days 91-190) from the 

MO-01-2007 (1995).

Statistic

Realization

1 2 3 4 5 6 7 8 9 10 Actual

Mean 5.98 6.53 4.48 5.68 6.84 8.14 4.72 6.31 5.20 6.72 7.13

SD 10.95 12.07 5.59 14.60 14.73 20.88 9.68 13.21 9.27 14.40 13.81

Max 73.70 72.39 23.59 105.89 128.75 126.89 73.70 101.91 63.37 70.87 91.60

Median 1.87 2.21 2.25 1.99 1.84 1.87 1.73 1.99 1.91 1.73 1.57

Min 0.01 0.03 0.04 0.05 0.05 0.02 0.04 0.01 0.02 0.04 0.05

Variance 119.88 145.57 31.29 213.12 216.90 436.17 93.78 174.51 85.94 207.37 190.64

Kurtosis 21.70 15.71 2.82 30.07 48.11 19.25 29.89 29.98 17.13 12.20 17.76

Julian Date (Days)

80 100 120 140 160 180 200

Atrazine Concentration (ug/L)

0

20

40

60

80

100

Estimated Mean (SD) 

Actual Data

Figure D- 16: Representative Realizations of Kriged Atrazine Time Series from 7-Day 

Sampling of the M0-01-2007 (Days 91-190).

The impact of missing data is more pronounced in the 14-day and 28-day sampling 

intervals (Tables D-13 – D-17 and Figures D-17 – D-21). As expected, uncertainty 

bounds are higher for 14-day and 28-day sampling intervals when compared to the 4-

day and 7-day sampling intervals. Additionally, descriptive statistics and temporal 

patterns of daily atrazine concentrations were variable for the case study data. 

Although the temporal pattern for the 14-day sampling interval for MO-01-2007 did not 

exactly match the data, the conditional simulations were capable of identifying the 

temporal occurrence of the peaks. In contrast, the Maumee time series realizations for 

the 14-day and 28-day sampling frequencies for MO-01-2007 were not capable of 
Page 32 of 96

identifying the temporal occurrence of the atrazine peaks. This illustrates that the 

temporal correlation is no longer observable with the 28-day sampling frequency. 

Table D- 13: Descriptive Statistics of 10 Realizations from Conditional Stochastic 

Sequential Simulation of the 14-day Kriged Atrazine Time Series (Days 100-200) from the 

Maumee River (1995).

Statistic

Realization

1 2 3 4 5 6 7 8 9 10 Actual

Mean 3.28 4.96 2.74 2.67 3.06 3.11 3.76 2.06 3.36 4.24 3.85

SD 3.01 3.78 2.10 2.65 3.43 1.97 1.56 1.78 1.88 2.36 3.38

Max 12.75 13.86 6.68 11.18 12.55 6.52 6.81 4.70 9.80 10.07 14.06

Median 3.81 4.25 3.95 0.64 0.48 4.24 4.23 1.48 4.02 4.44 3.88

Min 0.02 0.21 0.15 0.15 0.02 0.08 0.42 0.06 0.37 0.34 0.00

Variance 9.00 14.17 4.36 6.97 11.62 3.86 2.41 3.12 3.50 5.53 11.45

Kurtosis 0.79 -0.04 -1.76 -0.09 -0.24 -1.43 0.37 -1.78 0.20 -0.36 0.60

Julian Day

100 120 140 160 180 200 220

Atrazine Conc (ug/L)

0

2

4

6

8

10

12

14

16

Estimate Mean (SD) 

 Actual Data 

Figure D- 17: Representative Realizations Kriged Atrazine Time Series from 14-day 

sampling of the Maumee River 1995 (Days 100-200).
Page 33 of 96

Table D- 14: Descriptive Statistics of 10 Realizations from Conditional Stochastic 

Sequential Simulation of the 14-Day Kriged Atrazine Time Series (Days 91-190) from the 

MO-01-2007.

Statistic

Realization

1 2 3 4 5 6 7 8 9 10 Actual

Mean 9.65 6.90 5.97 11.31 14.20 3.33 8.87 3.82 6.81 7.83 7.13

SD 16.95 11.02 12.82 19.20 22.94 5.33 16.48 6.15 9.83 12.29 13.81

Max 83.62 66.83 84.78 85.07 89.57 29.67 83.42 23.21 56.70 77.04 91.60

Median 2.31 2.30 1.21 2.30 2.34 1.13 2.12 1.06 2.31 2.33 1.57

Min 0.01 0.05 0.01 0.00 0.00 0.00 0.05 0.01 0.04 0.04 0.05

Variance

287.4

3

121.4

1

164.2

3

368.7

0

526.2

6 28.37

271.5

7 37.81 96.64

150.9

8 190.64

Kurtosis 7.39 13.68 21.03 4.03 3.77 8.64 9.37 2.78 6.99 12.54 17.76

Julian Day

100 120 140 160 180

Atrazine Concentration (ug/L)

0

20

40

60

80

100

Estimated Mean (SD) 

Actual Data

Figure D- 18: Representative Realizations of Atrazine Time Series from 14-day Sampling 

of the M0-01-2007 (Days 91-190).
Page 34 of 96

Table D- 15: Descriptive Statistics of 10 Realizations from Conditional Stochastic 

Sequential Simulation of the 14-Day Kriged Atrazine Time Series (Days 91-190) from the 

MO-01-2007.

Statistic

Realization

1 2 3 4 5 6 7 8 9 10 Actual

Mean 7.51 6.90 7.73 14.94 16.71 3.31 9.70 3.98 7.77 9.38 7.13

SD 13.04 10.44 21.31 31.86 35.19 5.19 21.76 6.76 13.69 19.13 13.81

Max 102.38 69.17 138.32 140.64 149.09 23.21 124.85 31.90 95.88 127.05 91.60

Median 1.80 1.78 1.22 1.65 1.88 1.07 1.44 0.95 1.62 1.76 1.57

Min 0.00 0.03 0.01 0.01 0.00 0.00 0.04 0.01 0.04 0.03 0.05

Variance 170.14

109.0

0 453.91

1015.0

1

1238.0

3 26.99 473.40 45.75 187.39 365.92 190.64

Kurtosis 28.14 14.20 24.28 5.68 7.75 4.05 16.68 3.36 19.91 18.94 17.76

Julian Date (Days)

80 100 120 140 160 180 200

Atrazine Concentration (ug/L)

0

20

40

60

80

100

120

140

Estimated Mean (SD) 

Actual Data

Figure D- 19: Representative Realizations of Kriged Atrazine Time Series from 14-day 

Sampling of the M0-01-2007 (Days 91-190).
Page 35 of 96

Table D- 16: Descriptive Statistics of 10 Realizations from Conditional Stochastic 

Sequential Simulation of the 28-Day Kriged Atrazine Time Series (Days 100-200) from the 

Maumee River (1995).

Statistic

Realization

1 2 3 4 5 6 7 8 9 10 Actual

Mean 4.84 4.94 5.11 5.58 5.87 5.65 6.04 6.23 6.17 6.16 3.85

SD 3.96 4.06 3.58 3.34 3.75 3.45 3.80 4.10 4.14 4.50 3,38

Max 13.23 13.84 13.44 13.19 13.13 12.84 13.43 13.77 13.61 13.98 14.06

Median 3.50 3.80 4.18 4.41 4.43 4.57 4.48 4.72 4.60 4.18 3.88

Min 0.05 0.02 0.07 0.13 0.31 0.33 0.04 0.28 0.04 0.08 0.00

Variance 15.51 16.32 12.72 11.07 13.95 11.77 14.31 16.66 16.94 20.06 11.45

Kurtosis -0.74 -0.48 -0.19 -0.61 -0.88 -0.77 -1.01 -1.28 -1.21 -1.39 0.60

Julian Day

100 120 140 160 180 200 220

Atrazine Conc (ug/L)

0

2

4

6

8

10

12

14

16

Estimate Mean (SD) 

 Actual Data 

Figure D- 20: Representative Realizations Kriged Atrazine Time Series from 28-day 

Sampling of the Maumee River 1995 (Days 100-200) [Variogram from flow data was used 

in conditional simulation].
Page 36 of 96

Table D- 17: Descriptive Statistics of 10 Realizations from Conditional Stochastic 

Sequential Simulation of the 28-Day Kriged Atrazine Time Series (Days 90-190) from the 

MO-01-2007 (Days 90-190).

Statistic

Realization

1 2 3 4 5 6 7 8 9 10 Actual

Mean 10.73 18.11 12.10 9.34 14.08 14.16 10.32 4.98 7.35 8.91 7.13

SD 22.13 27.75 23.15 20.52 27.36 23.82 22.08 13.08 19.84 19.22 13.81

Max 89.64 89.77 88.87 88.88 89.90 88.37 88.49 79.59 89.68 83.09 91.60

Median 1.21 1.73 0.94 1.06 1.10 1.54 0.78 0.39 0.58 0.90 1.57

Min 0.00 0.00 0.00 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.05

Variance 489.92 770.28 535.93 421.12 748.33 567.20 487.74 171.18 393.64 369.48 190.64

Kurtosis 5.15 0.55 3.69 6.33 2.27 2.83 4.94 16.51 9.71 6.08 17.76

Julian Day

100 120 140 160 180

Atrazine Concentration (ug/L)

0

20

40

60

80

100

Estimated Mean (SD) 

Actual Data

Figure D- 21: Representative Realizations Atrazine Time Series from 28-day sampling of 

the MO-01-2007 (Days 90-190).

D.2.5 Estimation of Time Series Data Using Soft Data and Available 

Monitoring Data

An analysis of the 14-day and 28-day sampling frequencies illustrate a high uncertainty 

in predicting daily atrazine concentrations in an annual time series. This result is not 

unexpected because of the low number of samples used in estimation of temporal 

correlation of atrazine concentrations. In the absence of measured data, the 

incorporation of additional information (soft data) for predicting a time series of pesticide 
Page 37 of 96

concentrations is another approach for estimating daily atrazine pesticide 

concentrations. Potential approaches for estimating daily atrazine concentrations in a 

time series are as follows:

• Regression Approach- Use of a covariate to estimate atrazine concentrations.

• WARP Modeling/Regression Approach - Merging available monitoring data 

with model estimates (i.e, WARP) to estimate a distribution of concentration in a 

time series followed by a regression approach for assigning WARP percentiles 

into the appropriate sequence in a time series.

Either method can be easily applied to allow integration into the proposed geostatistical 

tools, including kriging and sequential stochastic simulation. 

D.2.5.1 Regression Approach

One possible regression approach is the use of a covariate to estimate daily atrazine 

concentrations. Covariates such as nitrate, flow, or another pesticide may provide 

available independent variable for estimating daily atrazine concentrations. Potential 

covariates in the MO-01-2007 and Maumee 1995 data-sets are daily flow. Additionally, 

acetochlor and rainfall data are available in the Maumee 1995 and MO-01-2007 datasets, respectively. 

Linear regression equations were developed to explore the relationship of daily atrazine 

concentration and mean daily flow and acetochlor concentrations. Table D-18 provides 

linear regression statistics of the relationship between atrazine and acetochlor 

concentrations at the Maumee River sampling station in 1995. The most notable result 

is that the regression equations for the 14-day and 28-day samplings are not 

significant. In contrast, the 4-day and 7-day sampling frequencies yielded significant 

regression equations (p<0.05). This analysis illustrates the loss in power in analyzing 

pesticide monitoring data with infrequent sampling (>14 days). The coefficient of 

determination (R2

)

 indicates that between 35 to 49% of the variability in atrazine 

concentrations at the Maumee River 1995 sampling site can be accounted for using 

acetochlor concentrations. This is not a surprising result because acetochlor and 

atrazine are corn herbicides with comparable application dates. The NCWQR dataset, 

however, is unique because it provides high frequency sampling of multiple pesticides. 

Unfortunately, most pesticide monitoring programs, such as NAWQA, take samples at 

biweekly or monthly intervals. One potential option is to explore the use of nutrient 

concentrations such as nitrate (NO3

-

) or flow as a covariate in estimating atrazine 

concentrations.
Page 38 of 96

Table D- 18: Linear Regression Equation of Atrazine Dependence on Acetochlor 

Concentration in the Maumee River 1995 University of Heidelberg Dataset.

Sampling 

Frequency N Intercept Slope R2

F Test

Probability

Complete 

Time Series 100 1.90 (0.32) 2.03 (0.28) 0.3555 5.94E-11*

4-day 25 1.46 (0.66) 2.85 (0.60) 0.4906 9.66E-5*

7- day 15 1.87 (0.91) 2.26 (0.80) 0.3785 0.0146*

14-day 8 3.11 (1.36) 0.25 (1.42) 0.0051 0.866

28-day 4 2.50 (7.06) -0.12 (13.66) 3.83E-5 0.99

( ) = Standard error

*-Significant (P<0.05)

Julian Day

100 120 140 160 180 200

Atrazine Concentration (ug/L)

0

2

4

6

8

10

12

14

16

 Daily Samples 

4-day Samples 

 7-day samples 

Actual 

Figure D- 22: Estimated Atrazine Concentration Time Series Using Acetochlor 

Concentration as a Covariate at the Maumee River (1995).

Flow data may also be useful for estimating atrazine concentrations in flowing water 

bodies. An analysis was conducted to explore the relationship of flow and atrazine 

concentrations at the 1995 Maumee River sampling site and the AEEMP MO-O1-2007 

site (Tables D-19 and D-20; Figures D-23 and D-24). Although both watersheds are 

dominated by agricultural crops, they represent different hydrological systems because 

of differences in average daily flow rate and watershed size. Although there is not a 

strong dependence of atrazine concentration on flow at various time intervals from 100 

to 200 days at the Maumee River site in 1995, there was a significant regression 

(P<0.05) for the sampling interval of 120- 200 days. 
Page 39 of 96

Table D- 19: Linear Regression Equation of Atrazine Dependence on Flow in the Maumee 

River 1995 University of Heidelberg Data.

Time Interval 

(days) N Intercept Slope R2

F Test

Probability

100-200 100 3.74 -6E-5 0.0303 0.0828

110-200 90 3.205 9.24E-5 0.0138 0.2662

120-200 80 3.122 2.18E-3 0.0741 0.0139*

130-200 70 3.821 1.59E-4 0.0462 0.0717

140-200 60 4.871 7.52E-5 0.0148 0.3487

150-200 50 5.191 -1.6E-5 0.0009 0.8295

160-200 40 5.140 -8.9E-5 0.0439 0.1883

170-200 30 4.946 -9.3E-5 0.0539 0.2087

180-200 20 3.046 5.04E-5 0.0855 0.1981

*-Significant (P<0.05)

100-200 days

Flow

0 10000 20000 30000 40000 50000 60000

Atrazine Conc (ug/L)

0

2

4

6

8

10

12

14

16

120-200 days

Flow (cfs)

0 2000400060008000100001200014000160001800020000

Atrazine Conc (ug/L)

-4

-2

0

2

4

6

8

10

12

14

16

160-200 days

Flow (cfs)

0 2000400060008000100001200014000160001800020000

Atrazine (ug/L)

0

2

4

6

8

10

Figure D- 23: Regression Equations for the Dependence of Atrazine Concentration on 

Average Daily Flow at the 1995 Maumee River Sampling Site.
Page 40 of 96

The MO-O1-2007 site shows a similar trend in atrazine dependence of atrazine 

concentration on flow (Table D-20; Figure D-24). The regression equation was only 

significant at the 160-200 and 170-200 day time intervals. 

Table D- 20: Linear Regression Equation of Atrazine Dependence on Flow in the 

Atrazine Ecological Monitoring Site (MO-01-2007).

Time Interval 

(days) N Intercept Slope R2

F Test

Probability

100-200 100 -0.6024 -0.1100 0.0150 0.2233

110-200 90 0.0083 -0.0684 0.0061 0.4617

120-200 80 1.3516 0.0416 0.0042 0.5626

130-200 70 -8.3079 -0.0563 0.0013 0.7643

140-200 60 6.3615 0.6193 0.0099 0.4447

150-200 50 14.3448 1.5113 0.0418 0.1498

160-200 40 27.7944 2.9575 0.1712 0.0072*

170-200 30 26.2956 2.8679 0.2452 0.0046*

180-200 20 -0.6024 -0.1100 0.0150 0.2233

 *-Significant (P<0.05)

100-200 days

Ln Flow

-10 -8 -6 -4 -2 0 2

ln Atrazine Conc -8

-6

-4

-2

0

2

4

160-200 days

ln Flow

-9.6 -9.4 -9.2 -9.0 -8.8 -8.6 -8.4 -8.2

ln Atrazine Conc (ug/L) -4

-2

0

2

4

6

Figure D- 24: Regression Equations for the Dependence of Atrazine Concentration on 

Average Daily Flow at the Atrazine Ecological Site (MO-01-2007).

The fact that the dependence of the atrazine concentration on flow applies only for 

certain periods is not surprising because atrazine application timing is a critical piece of 

information in determining the correct time interval. Both PRZM/EXAMS modeling and 
Page 41 of 96

monitoring indicate that the highest concentrations in a time series are generally found 

in runoff immediately after pesticide application (USEPA, 2007, 2009a). Additionally, 

high average daily flow events in the absence of atrazine, such as prior to atrazine 

application or after extensive degradation of atrazine, are expected to impact the slope 

of the regression equation. Unfortunately, exploiting any relationship of flow and 

atrazine concentration requires knowledge of the atrazine application dates within a 

watershed. Since atrazine application dates should parallel corn planting dates in most 

areas, any variable influencing corn planting dates, such as air temperature, soil 

temperature, or soil moisture conditions, may be an appropriate predictor for estimating 

atrazine application timing. 

Another option is to approximate the end date where there is a significant relationship 

between flow and atrazine concentration. The end date may be approximated by 

using the soil aerobic metabolism half-life of the pesticide. The soil metabolism half-life 

for atrazine is approximately 60 days. In our analysis, we used a 100-day interval to 

represent runoff dominated atrazine concentrations in the time series. Selection of the 

appropriate time interval may be complicated by pesticide use on multiple crops (i.e., 

sorghum and corn) in a watershed.

D.2.5.2 WARP Modeling and Monitoring Data using Flow as a Covariate 

The use of WARP predicted atrazine concentrations in a time series requires a 

comprehensive data integration process. The WARP model has been extensively 

reviewed (FIFRA SAP, 2007, 2009, Stone and Gilliom,2009). Sequencing the modelpredicted atrazine concentrations into a time series is a critical aspect in the time series 

prediction. For our analysis, we merged WARP predicted atrazine concentrations and 

the 28-day interval monitoring data from the Mamuee River data (1995) into a new 

distribution of atrazine concentrations. This process was conducted to provide ample 

data for variogram analysis and conditional simulation. Although the WARP model 

cannot discriminate runoff-dominated or base-flow dominated atrazine concentrations, 

the annual time series distribution from WARP is expected to capture a range of 

atrazine concentrations in the runoff-dominated period (Figure D-25). More importantly, 

the explanatory variables in the WARP regression model, with the exception of pesticide 

application use rate and treated area in the watershed, provide a description of the 

runoff potential for atrazine. Therefore, the WARP model predictions of atrazine 

concentrations are expected to predominately represent the runoff dominated atrazine 

concentrations. 
Page 42 of 96

Julian Day

0 100 200 300

Rank Percentile of Atrazine Concentration

0.0

0.2

0.4

0.6

0.8

1.0

Figure D- 25: Time Series of Percentiles of Atrazine Concentrations for 1995 Maumee 

River.

As a proof-of-concept evaluation, WARP modeling was conducted using available 

watershed properties reported for the Ohio sites in the AEEMP as surrogates for the 

Maumee River watershed. Two AEEMP Ohio sites were selected to bracket WARP 

predictions for the Maumee River watershed. For the CWS included in the AMP, WARP 

input parameters specific to the CWS watershed were used. The WARP regression 

model parameters are shown in Table D-21. 

Table D- 21: WARP Model Parameters for Prediction of Percentiles in the 1995 Maumee 

River Data.

WARP Parameter OHIO 01-2004 OHIO 03-2005

Use Intensity (kg ai/km2 watershed) 82 82

Precip May and June (mm) 311.66 93

K Factor 0.38 0.38

R Factor 150 150

Dunne Overflow (%) 2.00 2.00

The WARP predicted atrazine concentrations were merged with 28-day monitoring data 

from the Maumee River data to form a new distribution of atrazine concentrations. This 

distribution of atrazine concentrations is expected to represent atrazine concentrations 

during the runoff –dominated period. Descriptive statistics of the merged data and the 

estimated data are shown in Table D-22. 
Page 43 of 96

Table D- 22: Descriptive Statistics for the 28 day Merged WARP Atrazine Concentrations 

for the Maumee River 1995.

Statistic

Merged WARP

OHIO 01-2004

Merged WARP

OHIO 03-2005 

Mean 9.28 2.82

Median 1.38 0.60

Standard Deviation 20.43 5.04

Sample Variance 417.24 25.38

Kurtosis 11.40 9.87

Skewness 3.30 3.00

Minimum 0.27 0.15

Maximum 77.42 19.19

The next step in the time series simulation process is sequencing the predicted atrazine 

concentrations. One procedure for time sequencing atrazine concentrations is relating 

flow to atrazine concentrations. As previously discussed, a significant regression 

equation exists for atrazine and flow in the 120-200 day samples in the Maumee River 

data. The flow was weighted to ensure that flow events immediately after pesticide 

application have more influence on atrazine runoff. The weighting factor for flow was 

derived using the first-order degradation model [A=A0e-k*time; where A0= 1 lb/A, k= 

degradation rate (0.01152 days-1

) and time (days)]. The actual daily flow data from Day 

120 to Day 200 were multipled by the daily flow weighting factor. A comparison of 

actual flow and weighed flow is shown in Figure D-26. 

Julian Day

120 140 160 180 200

Flow (cfs)

0

2000

4000

6000

8000

10000

12000

14000

16000

18000

20000

Actual Flow

Weighted Flow

Figure D- 26: Time Series of Actual Daily Flow and Weighed Daily Flow on First-Order 

Atrazine Degradation.
Page 44 of 96

Using this information, the flow and atrazine data were separately ranked according to 

percentiles in the distribution. The weighted-flow and atrazine concentrations were 

paired to create a time series. This procedure assumes a 1:1 relationship in the relative 

percentile of flow and atrazine concentration (Table D-23). For example, the 93rd

percentile atrazine concentration was linked to the 93rd percentile weighted flow rate. 

The matched atrazine concentrations and weighted-flow rates were sequenced in the 

chronological order of the weighted flow rates. These time series were used for 

variogram analysis, kriging, and conditional stochastic simulation (Table D-24). The 

variograms provided a similar description of the temporal correlation for the different 

merged WARP data (OH-01-2004 and OH-03-2005) (Figure D-27). This is not an 

unexpected observation because the temporal structure of simulated time series was 

dependent on the temporal correlation of weighed-flow values in the time series. 

Table D- 23: Matched Percentiles of Flow and Merged WARP Predictions and 28-Day 

Atrazine Concentrations from Julian Day 120 to Day 200 for Maumee River (1995).

Julian 

Day

Weighted 

Flow (cfs) Percentile

Merged 

WARP

OH-01-

2004

Merged 

WARP

OH-03-

2005

131 1559.629 38% 0.48 0.29

132 2519.694 63% 4.18 1.75

134 2729.214 69% 4.78 2.31

136 1808.896 46% 0.56 0.48

143 4843.374 85% 10.84 4.79

147 10080.82 100% 77.42 19.19

158 2305.225 54% 2.19 0.71

169 1426.829 31% 0.35 0.28

173 454.9293 15% 0.3 0.18

178 358.9314 8% 0.29 0.16

180 7248.331 93% 20.26 4.83

186 3552.483 78% 6.92 4.18

190 786.0219 23% 0.33 0.21

198 237.2983 0% 0.27 0.15

Table D- 24: Variogram Model for Predicted Atrazine Concentration in the Maumee River 

1995 Time Series (120-200 days).

Data Time Series

Samples

N Nugget Model Sill Range

(Days)

WARP OH-01-

2004 Maumee 28-day 14 0.9 Spherical 3.90 15

WARP OH-03- Maumee 28-day 14 0.9 Spherical 2.40 17
Page 45 of 96

2005

Lag Time (Days)

0 10 20 30 40 50

Variance (ug/L2

) 0

1

2

3

4

5

Spherical 

Nugget=0.9 ug/L2

Sill=3.90 ug/L2

Range=15 days

Lag Time (Days)

0 10 20 30 40 50

Variance (ug/L2

) 0

1

2

3

4

Spherical

Nugget=0.9 ug/L2

Sill=2.40 ug/L2

Range=17 days

Figure D- 27: Variograms for the Merged 28-day Monitoring Data with WARP Estimated 

OH-01-2004 (top) and WARP Estimated OH-03-2005 (bottom) Time Series.

Conditional simulations of the merged WARP data were highly dependent on the WARP 

model predictions (Figures D-27 and D-28; Tables D-25 and D-26). The merged 

WARP data predicted for OH-01-2004 had a much higher predicted maximum atrazine 

concentration than OH-03-2005. This difference is mainly attributed to the difference in 

May and June precipitation. However, the two simulated time series clearly illustrate 

that the temporal occurrence of the atrazine peak mimics the actual time series. This 

situation suggests that weighted flow may be a reasonable predictor of temporal 

occurrence daily atrazine concentrations. 
Page 46 of 96

Table D- 25: Descriptive Statistics from 10 Realizations of Estimated Times Series from 

Merged 28-Day Monitoring Data and WARP Data (OHIO 01-2004 Watershed) for 1995 

Maumee River (Days 120-200).

Statisti

c

Realization

1 2 3 4 5 6 7 8 9 10

Actu

al

Mean 10.7 9.9 9.3 14.6 9.9 11.4 14.1 12.5 15.4 10.4 3.85

Median 2.0 1.1 4.2 4.7 0.9 2.0 2.9 1.4 2.7 2.5 1.57

Min 0.0 0.2 0.2 0.3 0.3 0.0 0.3 0.1 0.1 0.3 0.0

Max 77.4 77.4 77.4 77.4 77.4 77.4 77.4 77.4 77.4 77.4 14.06

SD 21.8 19.3 15.9 22.7 20.3 21.9 24.5 24.2 24.7 19.9 3.38

Varianc

e

475.

1

370.

8

251.

7

514.

8

412.

8

480.

5

600.

5

585.

8

611.

9

394.

7 11.45

Julian Day

120 140 160 180 200

Atrazine Concentration (ug/L)

0

20

40

60

80

100

Figure D- 28: Error Analysis from 10 Realizations of Estimated Times Series from Merged 

28 day Monitoring Data and WARP Modeling for the OHIO 01-2004 Watershed.
Page 47 of 96

Table D- 26: Descriptive Statistics from 10 Realizations of the Estimated Time Series from 

28-Day Sampling Data and WARP Modeling (OHIO 03-2005 Watershed) for 1995 Maumee 

River (Days 120-200).

Statisti

c

Realization Actu

1 2 3 4 5 6 7 8 9 10 al

Mean 3.05 2.94 3.13 4.50 2.95 3.35 4.11 3.37 3.96 3.19 3.85

Median 0.71 0.60 1.86 2.21 0.59 0.71 1.38 0.53 1.02 1.12 1.57

Min 0.01 0.15 0.13 0.15 0.15 0.02 0.15 0.06 0.08 0.15 0.0

Max

19.1

9

19.1

9

19.1

9

19.1

9

19.1

9

19.1

9

19.1

9

19.1

9

19.1

9

19.1

9 14.06

SD 5.23 4.81 4.21 5.81 5.10 5.36 6.08 5.55 5.68 4.78 3.38

Varianc

e

27.3

4

23.1

8

17.7

0

33.7

4

25.9

9

28.7

4

36.9

2

30.8

2

32.2

9

22.8

2 11.45

Julian Day

120 140 160 180 200

Atrazine Concentration (ug/L)

0

5

10

15

20

25

Figure D- 29: Error Analysis from 10 Realizations of Estimated Time Series from 28-day 

Monitoring Data and WARP Modeling for the OHIO 03-2005 Watershed.
Page 48 of 96

D.2.6 Summary 

Geostatistical techniques such as variography and kriging have provided a reasonable 

approximation of interpolated atrazine concentrations for selected time series for 

atrazine and acetochlor. These techniques failed for the analysis of chlorpyrifos 

monitoring data. This failure is likely associated with the randon nature of occurrence 

with short duration peaks. Because of the concern for stationarity in the time series 

modeling , the application of geostatistical techniques was limited to the atrazine runoffdominated period (Julian Day 90-200). Generally, monitoring data with sampling 

frequencies of 7 days or less were required to provide the best estimate of temporal 

occurrence and magnitude of atrazine concentrations. Based on our analysis, a 

minimum of 15 samples of known atrazine concentrations in a time series were required 

for reliable estimation of variogram (covariance structure) and kriging. Fewer samples in 

the time series (<15 samples) caused more uncertainty in both variogram modeling and 

kriging estimation. This is not unexpected because the temporal correlation of atrazine 

concentration may be shorter than the longer sampling frequencies. Additionally, there 

are fewer data to provide a reliable covariance matrix. One consequence of this 

estimation approach is that geostatistical techniques are only as good as the monitoring 

data. For example, the MO-01-2007 site had a daily peak atrazine concentration of 

91.60 ug/L. This peak concentration was not captured in the monitoring data for 4, 7, 

14, or 28-day sampling frequencies. Therefore, the peak concentration was consistently 

missed in the geostatistical analysis. 

Conditional stochastic simulation provides a reasonable approach to address 

uncertainty in atrazine concentrations in the time series. A critical issue in the 

conditional simulation process is extrapolation limits for estimation of upper end 

concentrations. A priori knowledge of reasonable upper limits on atrazine 

concentrations is needed for estimating reliable peak concentrations. In our case, we 

conditioned the stochastic simulation using atrazine monitoring data from 4, 7, 14, and 

28 day sampling frequencies. We generated a 1000 realizations of the time series in 

order provide a distribution of daily atrazine concentrations. 

In the absence of extensive monitoring data, regression techniques may be useful in 

predicting a time series of atrazine concentrations. Covariates such as flow and 

acetochlor concentrations may provide useful information for predicting atrazine 

concentrations. Another estimation approach may be the use of merging modeling 

(WARP) and monitoring data to construct a base-line time series for variography and 

conditional stochastic simulation. These methods were shown to provide reasonable 

approximations of the temporal occurrence and magnitude of atrazine concentrations.
Page 49 of 96

D.2.7 Additional Tables and Figures for Analyses of Other Pesticides and 

Sites

D.2.7.1 Maumee River 1995-Acetochlor 

Table D- 27: Variogram Models using the Maumee River 1995 Time Series for Acetochlor 

with Different Sampling Frequencies

Data Parameter N Nugget Model Sill Range

(Days)

Mauumee95 Actual Data 0.05 Gaussian 0.97 12

4-day 25 0.210 Linear 0.90 40

7-day 15 0.6 Linear 1.80 49

14-day 8 0.75 Gaussian 2.00 55

28-day 4 NA NA NA NA

Table D- 28: Descriptive Statistics from Kriged Maumee River 1995 Time Series for 

Acetochlor.

Statistic Actual 4 day 7 day

Mean 0.71 0.69 0.73

Standard Error 0.09 0.07 0.06

Median 0.40 0.44 0.47

Standard Deviation 0.93 0.73 0.59

Sample Variance 0.86 0.53 0.35

Kurtosis 4.52 5.39 3.50

Skewness 2.36 2.43 1.79

Range 3.96 3.87 3.33

Minimum 0.00 0.00 0.01

Maximum 3.96 3.87 3.34
Page 50 of 96

Julian Date (Days)

80 100 120 140 160 180 200 220

Acetochlor Concentration (ug/L)

0

1

2

3

4

5

Maumy River 1995 Kriged Time Series 

 4-Day Sampling Frequency

Actual Acetochlor Concentration (ug/L)

0 1 2 3 4 5

Predicted Acetochlor Concentration (ug/L)

0

1

2

3

4

5

Figure D- 30: Prediction Error in the Kriged Acetochlor Time Series from 4-day sampling 

of the Maumee River 1995 (Days 100-200).
Page 51 of 96

Maumy River 1995 7-day sampling frequency

0 1 2 3 4 5

Predicted Acetochlor Concentration (ug/L)

0

1

2

3

4

Julian Date

80 100 120 140 160 180 200 220

Acetochlor Conc (ug/L)

-2

-1

0

1

2

3

4

Actual Acetochlor Concentration (ug/L)

Figure D- 31: Prediction Error in the Kriged Acetochlor Time Series from 7-day sampling 

of the Maumee River 1995 (Days 100-200).
Page 52 of 96

D.2.7.2 Sandusky River 1995- Atrazine

Table D- 29: Variogram Models using the Sandusky River 1995 Time Series (100-200 

days) with Different Sampling Frequencies

Data Parameter N Nugget Model Sill Range

(Days)

San95 Actual Data 0.10 Gaussian 2.90 50

4-day 25 0.05 Gaussian 2.50 46

7-day 15 0.00 Gaussian 2.50 60

14-day 8 0.00 Gaussian 2.50 60

Table D- 30: Descriptive Statistics from Kriged Sandusky River 1995 Time Series for 

Atrazine (100-200 days)

Statistic Actual 4-day 7-day

Mean 4.380582 4.390118 4.031238

Standard Error 0.391665 0.370324 0.377063

Median 3.217868 3.251112 3.066691

Standard Deviation 3.916654 3.703244 3.770625

Sample Variance 15.34018 13.71402 14.21761

Kurtosis -0.05869 -0.91427 -0.24019

Skewness 0.930649 0.586317 0.982755

Range 15.464 14.7 12.81051

Minimum 0 0 0

Maximum 15.464 14.7 12.81051
Page 53 of 96

Sandusky River 1995 Atrazine

4 day sampling frequency

Actual Atrazine Concentration (ug/L)

0 2 4 6 8 10 12 14 16 18

Predicted Atrazine Concentration (ug/L)

0

2

4

6

8

10

12

14

16

Julian Date (Days)

80 100 120 140 160 180 200 220

Atrazine Concentration (ug/L)

-2

0

2

4

6

8

10

12

14

16

Figure D- 32: Prediction Error in the Kriged Acetochlor Time Series from 4-day sampling 

of the Sandusky River 1995 (Days 100-200).
Page 54 of 96

Sandusky River 1995 Atrazine

7 day sampling interval

0 2 4 6 8 10 12 14 16

Predicted Atrazine Concentration (ug/L)

0

2

4

6

8

10

12

14

Julian Date (days)

80 100 120 140 160 180 200 220

Atrazine Concentration (ug/L)

0

2

4

6

8

10

12

14

16

Actual Data

Predicted Data

Actual Atrazine Concentration (ug/L)

Figure D- 33: Prediction Error in the Kriged Acetochlor Time Series from 7-day sampling 

of the Sandusky River 1995 (Days 100-200).
Page 55 of 96

ADDENDUM

DATA INPUT FILE FOR GEOEASE

95MW28.dat

3

x

y

atrazine

131 10 0.48

132 10 4.18

134 10 4.78

136 10 0.56

143 10 10.84

147 10 77.42

158 10 2.19

169 10 0.35

173 10 0.3

178 10 0.29

180 10 20.26

186 10 6.92

190 10 0.33

198 10 0.27

GRID MATRIX EXAMPLE FOR 1-D KRIGING (VARIO in GEOEASE) 

Variable X 

Origin 120 10

Y

Spacing 1 1

Number 80 1

DATA INPUT FILE FOR SGSIM

95mwA28.dat 

5

x 

y 

atrazine 

KSvalue

weight 

11 1 0.29 0 1

12 1 1.75 0 1

14 1 2.31 0 1

16 1 0.48 0 1

23 1 4.79 0 1

27 1 19.19 0 1

38 1 0.71 0 1

49 1 0.28 0 1
Page 56 of 96

53 1 0.18 0 1

58 1 0.16 0 1

60 1 4.83 0 1

66 1 4.18 0 1

70 1 0.21 0 1

78 1 0.15 0 1

COMMAND FILE FOR SGSIM

 Parameters for SGSIM

 ********************

START OF PARAMETERS:

mwa28.dat \file with data

1 2 0 3 5 0 \ columns for X,Y,Z,vr,wt,sec.var.

-1.0e21 1.0e21 \ trimming limits

1 \transform the data (0=no, 1=yes)

X.trn \ file for output trans table

0 \ consider ref. dist (0=no, 1=yes)

histsmth.out \ file with ref. dist distribution

1 2 \ columns for vr and wt

0.0 19.19 \ zmin,zmax(tail extrapolation)

1 0.0 \ lower tail option, parameter

1 19.19 \ upper tail option, parameter

3 \debugging level: 0,1,2,3

x.dbg \file for debugging output

mwa28.out \file for simulation output

1000 \number of realizations to generate

80 1.0 1.0 \nx,xmn,xsiz

1 1.0 1.0 \ny,ymn,ysiz

1 1.0 1.0 \nz,zmn,zsiz

69069 \random number seed

0 16 \min and max original data for sim

12 \number of simulated nodes to use

0 \assign data to nodes (0=no, 1=yes)

0 3 \multiple grid search (0=no, 1=yes),num

0 \maximum data per octant (0=not used)

10.0 10.0 10.0 \maximum search radii (hmax,hmin,vert)

0.0 0.0 0.0 \angles for search ellipsoid

1 0.60 1.0 \ktype: 0=SK,1=OK,2=LVM,3=EXDR,4=COLC

ydata.dat \ file with LVM, EXDR, or COLC variable

4 \ column for secondary variable

1 0.3 \nst, nugget effect

1 0.7 0.0 0.0 0.0 \it,cc,ang1,ang2,ang3

 17.0 17.0 17.0 \a_hmax, a_hmin, a_vert
Page 57 of 96

APPENDIX D.3 EXPLORATION OF USING A WATERSHED MASS BALANCE 

APPROACH TO ESTIMATING DAILY ATRAZINE CONCENTRATIONS IN 

MONITORED AGRICULTURAL WATERSHED EFFLUENT

D.3.1 Abstract

A simple watershed mass balance model was calibrated to stream-water atrazine data 

from small agricultural watersheds from the Midwestern U.S., an area believed to 

produce high exposures to atrazine in water. These watersheds were instrumented to 

monitor flow at the watershed outfall and rainfall within the watershed. Atrazine was 

measured in the discharge from the watershed using four-day interval grab samples 

(and for selected watershed and year combinations, daily samples collected by autosamplers) throughout the mid-spring application timeframe through the growing season 

and into the early fall for multiple years. The simple mass balance models estimate the 

contribution of atrazine to the watershed from each individual storm event greater than 

0.4 cm rainfall based on the load of atrazine discharged from the watershed. Discharge 

was calculated by iteratively fitting daily predictions of atrazine concentration to the lessfrequently measured atrazine concentrations. Based on a set of six watershed and year 

combination model calibrations that maintained mass-balance conditions over the 

monitoring period and fit atrazine discharge data with r2 ≥ 0.85, the fraction of atrazine 

discharged over the monitoring period was on the order of 1 to 3.5 % of atrazine 

applied. However within this set of watersheds, it was the watershed and year 

combinations with the lowest percentage of applied atrazine discharged that produced 

high atrazine concentrations for extended periods. Those watershed and year 

combinations with the highest % of atrazine discharged expelled the atrazine in high 

flow, but short duration events, while the watershed and year combinations with the 

lowest % of atrazine discharged expelled the atrazine in lower flow, but longer duration 

events that tended to merge multiple discharge events into much longer periods of high 

concentration. Many other watershed and year combination model calibrations were 

unable to maintain mass balance over the period of monitoring. This was attributed to 

the coarseness of the model temporal resolution (daily time-step) and an apparent 

problem with the magnitude of the measured flows for some watershed and year 

combinations, at least in U.S. EPA’s compilation of the AEEMP data set.

D.3.2 Introduction

This paper explores the concept of using a mechanistic model to describe atrazine 

concentrations in surface drinking water sources. This paper is part of a collection of 

papers exploring different modeling approaches (simple statistical, geostatistical, and 
Page 58 of 96

mechanistic models) to describe atrazine concentrations. The intent here is to use a 

very simple mechanistic model to facilitate a discussion of the potential benefits and 

problems associated with any mechanistic model rather than to advocate any particular 

model or mechanistic modeling approach. The mechanistic model presented is 

intentionally designed to be simple and relatively easy to calibrate in hopes of facilitating 

a higher-level, conceptual discussion of the benefits and concerns of a mechanistic 

approach to monitoring atrazine concentrations in drinking water rather than focusing on 

model specification and calibration details.

D.3.3 Methods

Several individual watershed and year combination data sets were selected for 

modeling from the much larger Atrazine Ecological Exposure Monitoring Program 

(AEEMP) data set (see USEPA, 2007 and 2009a for more information on the monitoring 

program). Many watershed and year combinations had incomplete flow monitoring data 

sets. Because it would be difficult to interpret model results for portions of the growing 

season, only watershed and year combination data sets with complete flow data records 

for the monitoring season were selected for modeling. Additionally, several watershed 

and year combination data sets were avoided because negative values appeared in 

their flow data. In order to avoid these problematic data sets, the data sets to be 

modeled were selected by the investigator’s subjective professional judgments and 

limited in number by time/resource constraints.

The simple watershed atrazine mass-balance model operates on a daily time step 

because that is the temporal resolution of the data set that U.S. EPA was provided 

(grab and auto-sampler samples were only recorded to a date rather than a time of day 

and watershed discharge was provided as average daily flow). It is implemented in 

Microsoft Excel software and calibrated using the iterative fitting methods available in 

the Solver routine of Excel. 

The simple watershed atrazine mass-balance model essentially describes variations in 

atrazine concentration as a series of storm-event associated peaks that taper off over 

time. Figure D.3-1 depicts two pathways that might produce such peaks. For the runoff 

pathway, atrazine is applied over the soil surface/crop and is transported over the soil 

surface to the surface waterbody (stream) draining the watershed. A peak would occur 

when runoff enters the stream and diminish as that runoff water is discharged and/or 

diluted by groundwater inputs to the stream. For the tile-drainage pathway, atrazine is 

applied over the soil surface/crop and is transported through the near subsurface soil 

(Region A in Figure D.3-1) by leaching with each rainfall event. Eventually (in days to 

weeks), a storm event finally pushes (leaches) the atrazine into Region B (referred to in 
Page 59 of 96

this document as “break-through”), which represents the area around a tile drain 

(pictured, C) or preferential flow pathway that discharges (D) the atrazine to the stream 

draining the watershed. The peak would subside as the slug of water from Region B is 

discharged and/or diluted by groundwater inputs to the stream.

Runoff Pathway Tile-drainage Pathway

Figure D.3- 34. Cross-sectional views of a stream channel in a tile-drained agricultural 

watershed depicting runoff and tile drainage pathways by which atrazine applied to crops 

might reach a stream draining the watershed.

A cursory examination of the atrazine data to be modeled indicates that atrazine 

concentration peaks are almost always associated with storm events (major 

precipitation events that precede an increase flow in the stream), but not all storm 

events result in a large atrazine concentration peak. Data from some watershed and 

year combinations show a single atrazine peak, while other watershed and year 

combinations show multiple atrazine peaks. The model cannot attribute the peaks to 

different pathways, but is general enough to describe both pathways. If it is assumed 

that either or both pathways are responsible for the majority of atrazine occurring in 

these streams, the model should be able to describe it.

Because the simple watershed mass-balance model is a relatively crude proof of 

concept model, not all of the pathways depicted in Figure D.3-1 are fully described in 

this model. Specifically, modeling the distribution of atrazine applications over time to 

the crop/soil surface and the subsequent transport through Region A of Figure D.3-1 

would have added considerable complexity and would have required assumptions 

regarding the speed of atrazine leaching and depth/distance traveled by the atrazine. 

Therefore, the simple watershed atrazine mass-balance model only follows the atrazine 

from the time a runoff event begins or a slug of atrazine crosses from Region A to 

Region B in Figure D.3-1 until it exits the watershed in the stream discharge. This is 

described mathematically as:

Runoff

Stream Stream

D

A

B

C
Page 60 of 96

if 1and 0

if 1and 0

0

if 1

1

1 1

.

> − + ≤

− + > − + >

=

=

−

− −

t t t

t t t t t t

Init

t

t WAB AE AI

WAB AE AI t WAB AE AI

t

WAB

β

where: WABt is the watershed atrazine balance at any time (day), t. If t =1 (the first day 

of flow monitoring for that specific site), an initial watershed atrazine balance is 

estimated, βInit. based on an iterative fitting of the atrazine discharge data. Or if t > 1 

(any subsequent day), the watershed atrazine balance at the current time step (day) is 

the watershed atrazine balance from the previous time step minus any atrazine 

exported (discharged from the watershed), AEt, plus any atrazine imported (added) to 

the watershed, AIt. 

Potentially the units of WABt could simply be expressed as mass (e.g., kg of atrazine). 

However, using units of mass creates difficulties in comparing across different 

watersheds sizes (e.g., larger watersheds would have larger watershed atrazine 

balances, exports, and imports). Therefore, these values are expressed on a per 

hectare basis, resulting in units of kg/ha for the watershed atrazine balances, exports, 

and imports.

The atrazine exported, AEt, only accounts for the discharge from the watershed outfall 

(no degradation, volatilization, leaching to deep groundwater considered). It is modeled 

as:

AE ADFY WAB TS t = β Exp. × t × t−1 ×

where: βExp. is a fitted parameter describing the fraction of WAB exported in units of kg 

litre-1

; ADFYt is average daily flow yield in units of litre day-1 ha-1; WABt-1 is the 

watershed atrazine balance (kg/ha) from the previous day, t; and TS is the time step, 1 

day.

The atrazine imported, AIt, describes the runoff of atrazine or transfer of atrazine Region 

A of the soil in Figure D.3-1 to Region B with each storm and/or subsequent leaching 

event. Therefore, a series of fitted parameters, βi, are used to describe the mass of 

atrazine in runoff or entering Region B of Figure D.3-1 in units of kg/ha.

RF cm

RF cm

AI

i t

t

t if 0.4

0 if 0.4

≥

< = β
Page 61 of 96

Potentially, each storm event could be modeled with its own βi parameter. This would 

result in a large number of parameter estimates (βInit., βExp., and all the βi terms) relative 

to the number of atrazine concentration measurements. Therefore, in order to reduce 

the number of βi parameters an arbitrarily chosen threshold of 0.4 cm of rainfall was 

selected as the minimum rainfall needed to cause runoff or leaching of atrazine. The 

justification for a threshold is that small rainfall events would simply replace water lost 

by evapotranspiration and therefore would be unlikely to promote much runoff or 

leaching.

The daily predicted concentration, PCt (μg/L), is calculated as:

9 = ×10

t

t

t ADFY

AE PC

where: 109 is a conversion factor from kg to μg.

The model is calibrated to the observed atrazine concentrations by minimizing the mean 

squared deviations (error) between observed and predicted atrazine concentrations, 

MSE: 

SampleCount

SE

MSE ∑ t =

where: SampleCount is the number of atrazine samples (does not include field or 

laboratory duplicates) and the squared error on any day t (SEt) is calculated as follows:

( ( ) ( ))

( ( ) ( )) t t

t t t t

t t t t

n t n t

n t n t t

OC DL

OC DL PC DL

OC DL PC DL

PC OC

PC OC

t

SE

>

< >

< <

+ − +

+ − + =

if

if and

if and

log 1 log 1

log 1 log 1

0

0 if no sample on day 

2

2

This method of calculating error avoids biasing model parameter estimates that occurs 

when particular values are assumed for less than detection limit (DL) values (e.g., 1⁄2 the 

detection limit). This method indicates agreement (0 error) if both the observed and 

predicted concentrations are below the detection limit (condition 2 in the equation 

above), assigns error in proportion to the deviation between the detection limit and the 

predicted concentration when the measured concentration is below the detection limit 

and the predicted concentration is above the detection limit (condition 3), or assigns 
Page 62 of 96

error in proportion to the deviation between the observed and predicted concentration 

when the measured concentration is above the detection limit (condition 4).

D.3.4 Results and Discussion

Several anomalies were noted in the flow records from the AEEMP atrazine monitoring 

data set. Many watershed year combinations could not be modeled because of breaks 

in the flow records. Additionally, some flow data from particular watershed and year 

combinations (mainly from Nebraska) contained long stretches of negative values which 

could not be meaningfully interpreted. 

It appeared that the quality of the flow data deteriorated as the length data record 

increased. Figure D.3-2 depicts the changes in average daily flow for the MO-02 

watershed from the first year the site was monitored, 2004, to 2008. In years 2004 

through 2006, the average daily flow values varied within a relatively narrow band 

between 0.28 and 7.75 m3

/s, compared to 0.00017 to 3.15 m3/s for years 2007 and 

2008. Only 13 days from 2007 and 2008 (1 in 2007 and 12 in 2008) had measured 

average daily flow values that fell within the total range of measured average daily flow 

values from 2004 through 2006.

Figure D.3- 35. Average daily flow measurements from the MO-02 watershed from the 

2004 to 2008 monitoring periods.

The watershed atrazine mass-balance model was fit to 11 watersheds that varied with 

respect to watershed size (11.6 to 387.6 mi2) and atrazine use intensity (11.1 to 53.2 % 

of land in corn and sorghum). Because monitoring does not occur year-round, the 

model was fit to each watershed and year combination’s data separately. To determine 

0.0001

0.001

0.01

0.1

1

10

1/1/2004 1/1/2005 1/1/2006 1/1/2007 1/1/2008 1/1/2009

Average Daily Flow (m3/s)

Date
Page 63 of 96

if the model’s parameter estimates were similar for different years of data from the same 

watershed, multiple years from four watersheds (IA-01, IL-04, MO-01, MO-02) were 

modeled. To determine if the model’s parameter estimates were similar for grab and 

auto-sampler samples, models were fit separately for grab and auto-sampler samples 

as well as together (combined) for four watershed and year combinations (MO-01 2007, 

MO-02 2007 and 2008, and MO-04b 2008). For four watershed and year combinations 

(IA-02 2004, IL-04 2007, IN-11 2005, and MO-012004), there were too few autosampler samples to perform a separate auto-sampler calibration only. Therefore these 

watershed and year combinations were calibrated as a combined (models calibrated 

with grab and auto-sampler samples together) fitting of both sample types. 

Model Performance Criteria

Table D.3-1 summarizes the mass balance described by the calibrated models and 

provides some measures of model performance. Two criteria were used to judge how 

well the model worked with the available atrazine and flow data. The first criteria is the 

model fit to the atrazine concentration data as measured by r

2 (last two columns in 

Table D.3-1). Calibrations achieving an r

2 of ≥0.85 were considered acceptable. This 

criteria was chosen in order to select a small number of watersheds for which the 

parameter estimates would be assured to be relatively accurate.
Page 64 of 96

Table D.3- 31. Comparison of the watershed atrazine mass balance model performance on six of the atrazine ecological 

monitoring watersheds.

Watershed

Year 

(Sample 

Type1)

Watershed Characteristics Watershed Atrazine Mass-balance Summary in kg Model Performance

Area in 

Mi2 (ha)

Fraction in 

Corn 

and/or 

Sorghum 

(%)

Estimated 

Atrazine 

Application 

(kg)

Initial 

mass in 

Watershed 

(date)

Atrazine 

Export

Atrazine 

Discharge

Final Mass 

in 

Watershed 

(date)

Mass 

Imbalance

Fraction 

of WABt

Exported 

(liter-1

day-1 ha1

)

Export as 

a Fraction 

of App. 

Estimate 

(%)

Concentration 

Fit (r

2

)

Grab

Auto 

Sampler

IA-01 2004 G 21.3 

(5,517)

42.6 3705 0.34 (4/5) 47.47 46.88 0.93 (8/27) 0 1.35E-6 1.28 0.98 NA

2005 G 50.4 3987 0.10 (4/2) 10.08 10.65 0.22 (8/31) -0.69 2.36E-6 0.253 0.42 NA

IA-02 2004 C 21.7 

(5,623) 40.2 6114 0.029 (4/12) 4.108 4.137 0.00044 (8/20) 0 2.60E-5 0.067 0.47 0.82

IL-01 2004 G 21.8 

(5,633) 23.8 3166 0.54 (4/2) 114.2 112.3 2.5 (8/27) 0 1.06E-6 3.61 0.95 NA

IL-02 2004 G 33.4 

(8,644) 48.8 9974 0.23 (4/2) 23.26 22.00 1.5 (8/27) 0 9.08E-6 2.07 0.91 NA

IL-04 2006 G 20.6 

(5,323)

45.9 4871 0.066 (4/2) 61.03 60.83 0.27 (8/30) 0 5.75E-6 1.25 0.74 NA

2007 C 49.6 6195 0.024 (4/3) 8.683 8.671 0.037 (9/6) 0 1.81E-5 0.140 0.77 0.60

IL-05 2004 G 54.2 

(14,025) 53.2 17,606 1.1 (4/7) 51.12 51.59 0.59 (8/19) 0 2.33E-6 0.290 0.38 NA

IN-11 2005 C 9.03 

(2339) 27.7 1708 0.89 (4/7) 21.50 24.50 0.014 (8/22) -2.6 1.64E-5 1.26 0.98 0.99

MO-01

2004 C

11.6 

(3010)

37.6 5021 0.013 (4/2) 7.362 7.290 0.086 

(8/15) 0 3.39E-5 0.147 0.51 .02

2007 G

29.0 3874

0 (4/2) 0.1360 10.22 2E-5 (8/28) -10 2.37E-2 0.0035 0.81 NA

2007 A 1E-05 (4/2) 0.088 1.404 2E-5 (8/28) -1.3 2.40E-2 0.0023 NA 0.18

2007 C 9E-06 (4/2) 0.1645 0.8551 2E-5 (8/28) -0.69 2.57E-2 0.0042 0.84 0.19

MO-02

2004 G

28.2 

(7294)

27.9 8394 0.40 (4/21) 79.63 78.67 1.4 (8/15) 0 1.30E-5 0.949 0.85 NA

2005 G 24.8 7490 1.6 (4/2) 65.11 65.37 1.3 (8/17) 0 6.24E-6 0.869 0.92 NA

2006 G 32.9 9631 0.49 (4/2) 88.10 87.85 0.83 (8/17) 0 7.77E-6 0.915 0.97 NA

2007 G

25.0 7512

0.030 (4/2) 1.602 1.458 0.17 (8/6) 0 1.20E-4 0.021 0.73 NA

2007 A 0.033 (4/2) 1.343 1.274 0.10 (8/6) 0 1.03E-4 0.018 NA 0.55

2007 C 0.034 (4/2) 1.347 1.278 0.18 (8/6) 0 1.04E-4 0.018 0.55 0.47

2008 G 16.8 5024

0.0068 

(4/1) 5.668 5.749 0.016 

(10/1) -0.080 1.28E-4 0.11 0.78 NA

2008 A 0.0033 2.649 2.843 0.003 -0.19 2.69E-4 0.053 NA 0.64
Page 65 of 96

Watershed

Year 

(Sample 

Type1)

Watershed Characteristics Watershed Atrazine Mass-balance Summary in kg Model Performance

Area in 

Mi2 (ha)

Fraction in 

Corn 

and/or 

Sorghum 

(%)

Estimated 

Atrazine 

Application 

(kg)

Initial 

mass in 

Watershed 

(date)

Atrazine 

Export

Atrazine 

Discharge

Final Mass 

in 

Watershed 

(date)

Mass 

Imbalance

Fraction 

of WABt

Exported 

(liter-1

day-1 ha1

)

Export as 

a Fraction 

of App. 

Estimate 

(%)

Concentration 

Fit (r

2

)

Grab

Auto 

Sampler

(4/1) (10/1)

2008 C 0.0035 

(4/1) 2.882 3.083 0.003 (10/1) -0.20 2.90E-4 0.057 0.55 0.64

MO-04b 

2008 G

387.6 

(100,394) 11.1 49,531

0.32 (4/2) 596.8 599.7 0.039 

(10/1) -2.6 1.17E-5 1.20 0.93 NA

2008 A 0.16 (4/2) 475.3 486.5 0.16 (10/1) -11 3.14E-5 0.96 NA 0.75

2008 C 0.26 (4/2) 517.3 519.5 0.32 (10/1) -2.3 1.41E-5 1.04 0.88 0.79

NE-02 2005 G 65.0 

(16,841) 31.70 14,012 0.44 (4/2) 180.1 180.0 0.49 (8/26) 0 5.79E-6 1.28 0.71 NA

1 Monitoring sample type (G = grab; A = auto sampler; or C = combined analysis of both grab and auto sampler samples).
Page 66 of 96

The second criteria is mass imbalance (the 10th column in the table) which should be 

zero indicating that mass balance was maintained over the course of the monitoring 

period (initial mass + inputs – outputs = final mass). Eleven of the 25 model calibrations 

presented in Table D.3-1 resulted in negative mass imbalances (no positive mass 

imbalances) which occurred when a time-step removed more atrazine than was 

available in the watershed (negative atrazine balance). 

Model Time-step Considerations

The imbalance problem is likely due primarily to the coarseness of the model temporal 

resolution (time-step). Between each 1 day time-step, both the flow and concentration 

can change dramatically in the same direction (higher flows often result in higher 

concentrations). The predicted load (the product of predicted concentration and flow) in 

the stream-flow is removed from the previous day’s watershed atrazine balance each 

day to calculate the current day’s watershed atrazine balance. The rate at which the 

atrazine is removed is some fraction of previous day’s watershed atrazine balance 

(WABt-1) per liter discharged from the watershed. Theoretically, earlier liters discharged 

during the same day would remove more atrazine than subsequent liters because each 

liter removes atrazine from the WABt making it unavailable for withdraw by subsequent 

liters. Handled in this way, the WABt would never be depleted (it would asymptotically 

approach zero). However, WABt is constant during a time-step. Therefore, each liter 

discharged contains the same amount of atrazine as any other liter discharged on that 

day. So, when the flow changes greatly between time-steps, it is possible for the WABt

to be depleted. Converting the model to smaller time-steps such as 15 minute intervals 

(the flow monitoring interval normally used by USGS) would split each of the current 

model’s time-steps into 96 (24 hr × 60 min/hr / 15 min) 15 minute time-steps. This would 

greatly alleviate the potential for this problem because the previous time-step’s WABt

changes every 15 minutes, which better approximates a continuously changing WABt.

Table D.3-2 provides some estimates of the magnitude of the time-step issue by 

providing the maximum monitored daily load compared to the model-predicted total 

atrazine discharged during the monitoring period. Notice specifically, the IA-01 2004 

calibration (a calibration that met both of the aforementioned criteria) discharged 36.4% 

of the total atrazine discharged during the entire monitoring period on a single day.

Table D.3- 32. Comparison of maximum monitored daily load.

Watershed

Year 

(Sample 

Type1)

Total Atrazine 

Discharge (kg) 

during Monitoring 

Period (days)

Maximum Monitored Daily Load

Concentration 

(ug/L)

Average 

Daily Flow 

(m3/sec)

Maximum 

Load (kg of 

Atrazine)

% of Total 

Monitored 

Atrazine 

Discharge

IA-01 2004 G 46.9 (144) 10.03 19.7 17.1 36.4

2005 G 10.7 (151) 1.21 48.3 5.1 47.4
Page 67 of 96

Watershed

Year 

(Sample 

Type1)

Total Atrazine 

Discharge (kg) 

during Monitoring 

Period (days)

Maximum Monitored Daily Load

Concentration 

(ug/L)

Average 

Daily Flow 

(m3/sec)

Maximum 

Load (kg of 

Atrazine)

% of Total 

Monitored 

Atrazine 

Discharge

IA-02 2004 C 4.1 (130) 5.37 2.3 1.0 25.3

IL-01 2004 G 112.3 (147) 4.29 30.9 11.4 10.2

IL-02 2004 C 22.0 (147) 3.66 6.0 1.9 8.6

IL-04 2006 G 60.8 (150) 9.49 9.06 7.4 12.2

2007 C 8.7 (156) 12.69 2.7 3.0 34.0

IL-05 2004 G 51.6 (134) 22.13 11.2 21.5 41.6

IN-11 2005 C 25.0 (137) 237.5 0.926 19.0 76.1

MO-01

2004 C 7.3 (135) 64.07 0.537 3.0 40.8

2007 G 10.1 (148) 7.18 0.48 0.3 12.0

2007 A 1.4 (148) 29.72 0.48 1.2 87.4

2007 C 0.86 (148) 29.72 0.48 1.2 143.5

MO-02

2004 G 78.7 (148) 23.93 1.67 3.5 4.4

2005 G 65.4 (154) 28.07 1.09 2.6 4.1

2006 G 87.9 (137) 43.17 0.83 3.1 3.5

2007 G 1.5 (126) 16.18 0.56 0.79 54.0

2007 A 1.3 (126) 13.80 0.56 0.67 52.7

2007 C 1.3 (126) 16.18 0.56 0.79 61.6

2008 G 5.7 (183) 56.6 0.27 1.3 22.7

2008 A 2.8 (183) 28.95 0.27 0.67 23.5

2008 C 3.1 (183) 56.6 0.27 1.3 42.4

MO-04b

2008 G 599.7 (182) 3.15 176.4 48.0 8.0

2008 A 486.5 (182) 17.26 26.3 39.2 8.1

2008 C 519.5 (182) 3.15 176.4 48.0 9.2

NE-02 2005 G 180.0 (146) 12.62 22.1 24.1 13.4

1 Monitoring sample type (G = grab; A = auto sampler; or C = combined analysis of both 

grab and auto sampler samples).

Characteristics of Successful Model Calibrations

Only six watershed and year combinations (IA-01 2004, IL-01 2004, IL-02 2004, and 

MO-02 2004, 2005 and 2006) met both criteria (indicated in bold in Tables D.3-1 and 

D.3-2). All of these watershed and year combinations were from small (21.3 to 33.4 mi2) 

watersheds and were calibrated to grab samples collected from relatively early in the 

sampling program. The Iowa and Illinois watershed and year combinations that met 

both criteria were from 2004. The MO-02 watershed years that met both criteria were 

from the early years (2004 through 2006) before the dramatic changes in average daily 

flow at this watershed (depicted in Figure D.3-2). Auto-samplers were used to a much 

greater extent in the later sampling years and yielded some calibrations with relatively 

good fits to the atrazine data (high r

2

s). Therefore, the lack of calibrations to autosampler data that met the model selection criteria is thought to be due to the 
Page 68 of 96

aforementioned flow issue, rather than an indication of any problem with the autosampler data.

The six watershed and year combinations that met both criteria tended to have rather 

small βExp. parameter estimates (fraction of WABt Exported; column 11 of Table D.3-1) 

of approximately 1 × 10-6. This compares to a range from all calibrations in Table D.3-1 

of 1.06 × 10-6 to 2.37 × 10-2.

These six watersheds varied with respect to the amount of atrazine exported as a 

fraction of the estimated atrazine applied to the watershed (column 12 of Table D.3-1) 

The Iowa and Illinois watershed and year combinations that met both criteria ranged 

from 1.28 to 3.61 %, while the three years from MO-02 that met both criteria were tightly 

clustered in a range from 0.869 to0.949 %. This measure is intended to indicate how 

well the atrazine applied in a watershed is retained within that watershed. 

By this measure, the MO-02 watershed and year combinations might be expected to 

yield generally low atrazine concentrations with infrequent peaks. However, the MO-02 

watershed and year combinations yield high concentrations that persist for long periods. 

This appears to be due to the Iowa and Illinois sites releasing their atrazine loads 

quickly in flash-flood events, while the Missouri watershed and year combinations 

appear to release high atrazine concentration water slowly during and between less 

“flashy” events.

Determination of Optimal Sampling Intervals

The original motivations for this research were to develop a mechanistic model, 

calibrate it to a watershed with daily auto-sampler data, and then subsample that daily 

data to simulate less frequent sampling intervals to estimate an “optimal” sampling 

interval (lowest sampling intensity that provides a reasonable approximation of the 

original data set). Unfortunately, three of the four calibrations to auto-sampler data had 

a non-zero mass imbalance, while the fourth (MO-02 2007) had a poor model fit (r

2

= 

0.55). Additionally, this fourth watershed and year combination is based on the 2007 

flow data depicted in Figure D.3-2, which is suspected of being problematic. Therefore, 

the effort to estimate optimal sampling intervals from this mechanistic modeling 

approach was abandoned. Potentially, there are watershed and year combinations in 

the AEEMP data set that have acceptable flow data, but this modeling effort ran out of 

time and resources before these combinations were found.
Page 69 of 96

Scaling Issues with Larger Drinking Water Watersheds

In order to calibrate a model similar to that presented here, sufficient data would be 

needed to estimate each storm event peak (each βi parameter). Potentially, the 

sampling could be optimized based on when the storm occurs and collected by autosamplers rather than with a field crew on a fixed schedule (every 4, 7, 14 days, etc). 

Notice that many samples in the attached graphs are collected at low flows long after 

the last rain event has occurred. If a model were calibrated to a site based on an initial 

year of intensive data collection, the model could be used to indicate when it is likely 

that samples would and would not need to be collected. This would likely require that 

data collection and model recalibration with model prediction availability occurring 

rapidly throughout the season of concern.

Another consideration is spatial variability in storm occurrence. Most of the relatively 

small watersheds modeled in the AEEMP appear to show atrazine being discharged 

over many storm events. While it may be possible that multiple individual peaks could 

be caused by rain events that affect all portions of the watershed equally with the low 

interceding concentrations caused by dilution with less-contaminated baseflow, it seems 

more likely that the multiple peaks are caused by small-scale intense storms causing 

runoff or break-through of the leaching atrazine in different places within the watershed. 

Since some drinking water source watersheds are much larger than the watersheds 

considered here, spatial variability in storm occurrence would be more of a problem.

Potentially, a good method for investigating this issue would be to acquire raster images 

of rainfall totals from historical radar weather data. This rainfall data over the 

watersheds of interest could be used to determine if the multiple peaks are generated 

from small intense storms occurring over specific sections of the watershed at different 

times or whether the rainfall totals are more-or-less evenly distributed over the 

watersheds just prior to each atrazine peak. Such information would be usefully for 

evaluating what storm event conditions and agricultural practices led to high atrazine 

concentrations and/or durations and which did not.

Finally, a word of caution about the general applicability of this type of model to other 

existing or planned atrazine monitoring data sets. The watershed and year 

combinations modeled here are quite intensively sampled (4 day interval for the grab 

samples). Other data sets are typically sampled less intensively (weekly, monthly, or 

quarterly). Less frequent sampling would often miss large peaks, which can transport 

large fractions of the load of atrazine discharged during a season (e.g., 36% discharged 

in single storm event for the IL-01 2004 data in Table D.3-2). 
Page 70 of 96

While this research did potentially provide some insights into what would likely be 

needed to successfully use a mechanistic modeling approach to aid in the interpretation 

atrazine monitoring data, it was unable to estimate what sampling intensity was needed 

to support such a mechanistic modeling and monitoring approach.

Graphs depicting individual watershed and year combination calibrations analyzed for 

the watershed mass balance approach are shown in Figures D.3-3 through D.3-54. 
Page 71 of 96

Figure D.3- 36. Comparison of observed and predicted atrazine concentrations for the IA01 watershed in 2004.

Figure D.3- 37. Comparison of atrazine runoff or break-through during rain events in 

excess of 0.4 cm over the period of monitoring for the IA-01 watershed in 2004.

IA-01 2004

0.00

2.00

4.00

6.00

8.00

10.00

12.00

3/14 4/3 4/23 5/13 6/2 6/22 7/12 8/1 8/21 9/10

Date

Atrazine (μg/L)

0

100000

200000

300000

400000

500000

600000

Daily Average Flow Yield (L/day/ha)

Grab Sample Conc, ug/L

Storm Events > 0.4 cm

Predicted Conc ug/L

Avg Daily Flow Yeild (L/day ha)

0

0.001

0.002

0.003

0.004

0.005

0.006

0.007

0.008

0.009

Rain1Rain2Rain3Rain4Rain5Rain6Rain7Rain8Rain9

Rain10Rain11Rain12Rain13Rain14Rain15Rain16Rain17Rain18Rain19Rain20Rain21Rain22

Event

Atrazine Runoff/Break-through (kg/ha)
Page 72 of 96

Figure D.3- 38. Comparison of observed and predicted atrazine concentrations for the IA01 watershed in 2005. (Note that the predicted concentrations fall to zero in the late 

June/early July.)

Figure D.3- 39. Comparison of atrazine runoff or break-through during rain events in 

excess of 0.4 cm over the period of monitoring for the IA-01 watershed in 2005.

IA-01 2005

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

3/9 3/29 4/18 5/8 5/28 6/17 7/7 7/27 8/16 9/5 9/25

Date

Atrazine (μg/L)

0

100000

200000

300000

400000

500000

600000

700000

800000

Daily Average Flow Yield (L/day/ha)

Grab Sample Conc, ug/L

Storm Events > 0.4 cm

Predicted Conc ug/L

Avg Daily Flow Yeild (L/day ha)

0

0.0001

0.0002

0.0003

0.0004

0.0005

0.0006

Rain1Rain2Rain3Rain4Rain5Rain6Rain7Rain8Rain9

Rain10Rain11Rain12Rain13Rain14Rain15Rain16Rain17Rain18Rain19Rain20Rain21Rain22

Event

Atrazine Runoff/Break-through (kg/ha)
Page 73 of 96

Figure D.3- 40. Comparison of observed and predicted atrazine concentrations for the IA02 watershed in 2004.

Figure D.3- 41. Comparison of atrazine runoff or break-through during rain events in 

excess of 0.4 cm over the period of monitoring for the IA-02 watershed in 2004.

IA-02 2004

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

8.00

3/14 4/3 4/23 5/13 6/2 6/22 7/12 8/1 8/21 9/10

Date

Atrazine (ug/L)

0

5000

10000

15000

20000

25000

30000

35000

40000

Daily Flow Yield (L/day/ha)

Grab Samples

Auto Sampler

Predicted

Flow Yield

0

0.00005

0.0001

0.00015

0.0002

0.00025

Rain1Rain2Rain3Rain4Rain5Rain6Rain7Rain8Rain9

Rain10Rain11Rain12Rain13Rain14Rain15Rain16Rain17Rain18

Event

Atrazine Runoff/Break-through (kg/ha)
Page 74 of 96

Figure D.3- 42. Comparison of observed and predicted atrazine concentrations for the IL01 watershed in 2004.

Figure D.3- 43. Comparison of atrazine runoff or break-through during rain events in 

excess of 0.4 cm over the period of monitoring for the IL-01 watershed in 2004.

IL-01 2004

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

16.00

3/14 4/3 4/23 5/13 6/2 6/22 7/12 8/1 8/21 9/10

Date

Atrazine (μg/L)

0

50000

100000

150000

200000

250000

300000

350000

400000

450000

500000

Daily Average Flow Yield (L/day/ha)

Grab Sample Conc, ug/L

Storm Events > 0.4 cm

Predicted Conc ug/L

Avg Daily Flow Yeild (L/day ha)

0

0.002

0.004

0.006

0.008

0.01

0.012

0.014

Rain1Rain2Rain3Rain4Rain5Rain6Rain7Rain8Rain9

Rain10Rain11Rain12Rain13Rain14Rain15Rain16Rain17Rain18Rain19Rain20Rain21Rain22

Event

Atrazine Runoff/Break-through (kg/ha)
Page 75 of 96

Figure D.3- 44. Comparison of observed and predicted atrazine concentrations for the IL02 watershed in 2004.

Figure D.3- 45. Comparison of atrazine runoff or break-through during rain events in 

excess of 0.4 cm over the period of monitoring for the IL-02 watershed in 2004.

IL-02 2004

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

8.00

3/14 4/3 4/23 5/13 6/2 6/22 7/12 8/1 8/21 9/10

Date

Atrazine (μg/L)

0

10000

20000

30000

40000

50000

60000

70000

Daily Average Flow Yield (L/day/ha)

Grab Sample Conc, ug/L

Storm Events > 0.4 cm

Predicted Conc ug/L

Avg Daily Flow Yeild (L/day ha)

0

0.0001

0.0002

0.0003

0.0004

0.0005

0.0006

0.0007

0.0008

Rain1Rain2Rain3Rain4Rain5Rain6Rain7Rain8Rain9

Rain10Rain11Rain12Rain13Rain14Rain15Rain16Rain17Rain18Rain19Rain20Rain21Rain22

Event

Atrazine Runoff/Break-through (kg/ha)
Page 76 of 96

Figure D.3- 46. Comparison of observed and predicted atrazine concentrations for the IL04 watershed in 2006.

Figure D.3- 47. Comparison of atrazine runoff or break-through during rain events in 

excess of 0.4 cm over the period of monitoring for the IL-04 watershed in 2006.

IL-04 2006

0.00

5.00

10.00

15.00

20.00

25.00

30.00

35.00

40.00

45.00

3/24 4/13 5/3 5/23 6/12 7/2 7/22 8/11 8/31 9/20

Date

Atrazine (μg/L)

0

100000

200000

300000

400000

500000

600000

Daily Average Flow Yield (L/day/ha)

Grab Sample Conc, ug/L

Storm Events > 0.4 cm

Predicted Conc ug/L

Avg Daily Flow Yeild (L/day ha)

0

0.001

0.002

0.003

0.004

0.005

0.006

0.007

0.008

Rain1Rain2Rain3Rain4Rain5Rain6Rain7Rain8Rain9

Rain10Rain11Rain12Rain13Rain14Rain15Rain16Rain17Rain18Rain19

Event

Atrazine Runoff/Break-through (kg/ha)
Page 77 of 96

Figure D.3- 48. Comparison of observed and predicted atrazine concentrations for the IL04 watershed in 2007.

Figure D.3- 49. Comparison of atrazine runoff or break-through during rain events in 

excess of 0.4 cm over the period of monitoring for the IL-04 watershed in 2007.

IL-04 2007

0

5

10

15

20

25

30

3/19 4/8 4/28 5/18 6/7 6/27 7/17 8/6 8/26 9/15

Date

Atrazine (ug/L)

0

5000

10000

15000

20000

25000

30000

35000

40000

45000

50000

Daily Flow Yield (L/day/ha)

Grab Sample

Auto Sampler

Storm Events > 0.4 cm

Predicted Conc ug/L

Daily Flow Yield

0

0.0002

0.0004

0.0006

0.0008

0.001

0.0012

0.0014

0.0016

Rain1Rain2Rain3Rain4Rain5Rain6Rain7Rain8Rain9

Rain10Rain11Rain12Rain13Rain14

Event

Atrazine Runoff/Break-through (kg/ha)
Page 78 of 96

Figure D.3- 50. Comparison of observed and predicted atrazine concentrations for the IL05 watershed in 2004.

Figure D.3- 51. Comparison of atrazine runoff or break-through during rain events in 

excess of 0.4 cm over the period of monitoring for the IL-02 watershed in 2004.

IL-05 2004

0.00

5.00

10.00

15.00

20.00

25.00

3/14 4/3 4/23 5/13 6/2 6/22 7/12 8/1 8/21 9/10

Date

Atrazine (ug/L)

0

50000

100000

150000

200000

250000

300000

350000

Daily Flow Yield (L/day/ha)

Grab Sample

Storm Events > 0.4 cm

Predicted Conc ug/L

Daily Flow Yield

0

0.0002

0.0004

0.0006

0.0008

0.001

0.0012

0.0014

0.0016

0.0018

0.002

Rain1Rain2Rain3Rain4Rain5Rain6Rain7Rain8Rain9

Rain10Rain11Rain12Rain13Rain14Rain15Rain16Rain17Rain18Rain19

Event

Atrazine Runoff/Break-through (kg/ha)
Page 79 of 96

Figure D.3- 52. Comparison of observed and predicted atrazine concentrations for the IN11 watershed in 2005.

Figure D.3- 53. Comparison of atrazine runoff or break-through during rain events in 

excess of 0.4 cm over the period of monitoring for the IN-11 watershed in 2005.

IN-11 2005

0.00

50.00

100.00

150.00

200.00

250.00

3/9 3/29 4/18 5/8 5/28 6/17 7/7 7/27 8/16 9/5

Date

Atrazine (ug/L)

0

20000

40000

60000

80000

100000

120000

140000

Daily Flow Yield (L/day/ha)

Grab Sample

Storm Events > 0.4 cm

Predicted Conc ug/L

Auto Sample Conc, ug/L

Daily Flow Yield

0

0.001

0.002

0.003

0.004

0.005

0.006

0.007

0.008

0.009

Rain1Rain3Rain5Rain7Rain9

Rain11Rain13Rain15Rain17Rain19Rain21Rain23

Event

Atrazine Runoff/Break-through (kg/ha)
Page 80 of 96

Figure D.3- 54. Comparison of observed and predicted atrazine concentrations for the 

MO-01 watershed in 2004.

Figure D.3- 55. Comparison of atrazine runoff or break-through during rain events in 

excess of 0.4 cm over the period of monitoring for the MO-01 watershed in 2004.

MO-01 2004

0

10

20

30

40

50

60

70

3/14 4/3 4/23 5/13 6/2 6/22 7/12 8/1 8/21 9/10

Date

Atrazine (μg/L)

0

2000

4000

6000

8000

10000

12000

14000

16000

18000

Daily Average Flow Yield (L/day/ha)

Auto Sample Conc, ug/L

Storm Events > 0.4 cm

Predicted Conc ug/L

Grab Sample Conc, ug/L

Avg Daily Flow Yeild (L/day ha)

0

0.0002

0.0004

0.0006

0.0008

0.001

0.0012

0.0014

0.0016

0.0018

0.002

Rain1Rain3Rain5Rain7Rain9

Rain11Rain13Rain15Rain17Rain19Rain21Rain23Rain25Rain27

Event

Atrazine Runoff/Break-through (kg/ha)
Page 81 of 96

Figure D.3- 56. Comparison of observed and predicted atrazine concentrations for the 

MO-01 watershed in 2007.

Figure D.3- 57. Comparison of atrazine runoff or break-through during rain events in 

excess of 0.4 cm over the period of monitoring for the MO-01 watershed in 2007.

MO-01 2007

0

50

100

150

200

250

3/19 4/8 4/28 5/18 6/7 6/27 7/17 8/6 8/26 9/15

Date

Atrazine (μg/L)

0

5000

10000

15000

20000

25000

30000

35000

Daily Average Flow Yield (L/day/ha)

Grab Sample Conc, ug/L

Storm Events > 0.4 cm

Predicted Conc ug/L

Avg Daily Flow Yeild (L/day ha)

0

0.000005

0.00001

0.000015

0.00002

0.000025

Rain1Rain2Rain3Rain4Rain5Rain6Rain7Rain8Rain9

Rain10Rain11Rain12Rain13Rain14Rain15Rain16Rain17Rain18Rain19Rain20Rain21

Event

Atrazine Runoff/Break-through (kg/ha)
Page 82 of 96

Figure D.3- 58. Comparison of observed and predicted atrazine concentrations for the 

MO-01 watershed in 2007.

Figure D.3- 59. Comparison of atrazine runoff or break-through during rain events in 

excess of 0.4 cm over the period of monitoring for the MO-01 watershed in 2007.

MO-01 2007 Auto-samples

0

20

40

60

80

100

120

140

160

180

200

3/19 4/8 4/28 5/18 6/7 6/27 7/17 8/6 8/26 9/15

Date

Atrazine (μg/L)

0

5000

10000

15000

20000

25000

30000

35000

Daily Average Flow Yield (L/day/ha)

Auto Sample Conc, ug/L

Storm Events > 0.4 cm

Predicted Conc ug/L

Avg Daily Flow Yeild (L/day ha)

0

0.000002

0.000004

0.000006

0.000008

0.00001

0.000012

0.000014

0.000016

0.000018

Rain1Rain2Rain3Rain4Rain5Rain6Rain7Rain8Rain9

Rain10Rain11Rain12Rain13Rain14Rain15Rain16Rain17Rain18Rain19Rain20Rain21

Event

Atrazine Runoff/Break-through (kg/ha)
Page 83 of 96

Figure D.3- 60. Comparison of observed and predicted atrazine concentrations for the IL05 watershed in 2004.

Figure D.3- 61. Comparison of atrazine runoff or break-through during rain events in 

excess of 0.4 cm over the period of monitoring for the IL-02 watershed in 2004.

MO-01 2007 Combined

0

20

40

60

80

100

120

140

160

180

200

3/19 4/8 4/28 5/18 6/7 6/27 7/17 8/6 8/26 9/15

Date

Atrazine (μg/L)

0

5000

10000

15000

20000

25000

30000

35000

Daily Average Flow Yield (L/day/ha)

Auto Sample Conc, ug/L

Storm Events > 0.4 cm

Predicted Conc ug/L

Grab Sample Conc, ug/L

Avg Daily Flow Yeild (L/day ha)

0

0.000005

0.00001

0.000015

0.00002

0.000025

0.00003

Rain1Rain2Rain3Rain4Rain5Rain6Rain7Rain8Rain9

Rain10Rain11Rain12Rain13Rain14Rain15Rain16Rain17Rain18Rain19Rain20Rain21

Event

Atrazine Runoff/Break-through (kg/ha)
Page 84 of 96

Figure D.3- 62. Comparison of observed and predicted atrazine concentrations for the 

MO-02 watershed in 2004.

Figure D.3- 63. Comparison of atrazine runoff or break-through during rain events in 

excess of 0.4 cm over the period of monitoring for the MO-02 watershed in 2004.

MO-02 2004 Grab

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

3/14 4/3 4/23 5/13 6/2 6/22 7/12 8/1 8/21 9/10

Date

Atrazine (μg/L)

0

5000

10000

15000

20000

25000

30000

35000

Daily Average Flow Yield (L/day/ha)

Grab Sample Conc, ug/L

Storm Events > 0.4 cm

Predicted Conc ug/L

Avg Daily Flow Yeild (L/day ha)

0

0.0005

0.001

0.0015

0.002

0.0025

0.003

0.0035

0.004

0.0045

Rain1Rain2Rain3Rain4Rain5Rain6Rain7Rain8Rain9

Rain10Rain11Rain12Rain13Rain14Rain15Rain16Rain17

Event

Atrazine Runoff/Break-through (kg/ha)
Page 85 of 96

Figure D.3- 64. Comparison of observed and predicted atrazine concentrations for the 

MO-02 watershed in 2005.

Figure D.3- 65. Comparison of atrazine runoff or break-through during rain events in 

excess of 0.4 cm over the period of monitoring for the MO-02 watershed in 2005.

MO-02 2005 Grab

0.00

5.00

10.00

15.00

20.00

25.00

30.00

3/9 3/29 4/18 5/8 5/28 6/17 7/7 7/27 8/16 9/5 9/25

Date

Atrazine (μg/L)

0

5000

10000

15000

20000

25000

30000

35000

Daily Average Flow Yield (L/day/ha)

Grab Sample Conc, ug/L

Storm Events > 0.4 cm

Predicted Conc ug/L

Avg Daily Flow Yeild (L/day ha)

0

0.0005

0.001

0.0015

0.002

0.0025

0.003

0.0035

0.004

Rain1Rain2Rain3Rain4Rain5Rain6Rain7Rain8Rain9

Rain10Rain11Rain12Rain13Rain14

Event

Atrazine Runoff/Break-through (kg/ha)
Page 86 of 96

Figure D.3- 66. Comparison of observed and predicted atrazine concentrations for the 

MO-02 watershed in 2006.

Figure D.3- 67. Comparison of atrazine runoff or break-through during rain events in 

excess of 0.4 cm over the period of monitoring for the MO-02 watershed in 2006.

MO-02 2006 Grab

0.00

5.00

10.00

15.00

20.00

25.00

30.00

35.00

40.00

45.00

50.00

3/24 4/13 5/3 5/23 6/12 7/2 7/22 8/11 8/31

Date

Atrazine (μg/L)

0

10000

20000

30000

40000

50000

60000

70000

80000

90000

100000

Daily Average Flow Yield (L/day/ha)

Grab Sample Conc, ug/L

Storm Events > 0.4 cm

Predicted Conc ug/L

Avg Daily Flow Yeild (L/day ha)

0

0.001

0.002

0.003

0.004

0.005

0.006

0.007

Rain1Rain2Rain3Rain4Rain5Rain6Rain7Rain8Rain9

Rain10Rain11Rain12Rain13Rain14

Event

Atrazine Runoff/Break-through (kg/ha)
Page 87 of 96

Figure D.3- 68. Comparison of observed and predicted atrazine concentrations for the 

MO-02 watershed in 2007.

Figure D.3- 69. Comparison of atrazine runoff or break-through during rain events in 

excess of 0.4 cm over the period of monitoring for the MO-02 watershed in 2007.

MO-02 2007 Grab

0

5

10

15

20

25

3/19 4/8 4/28 5/18 6/7 6/27 7/17 8/6 8/26 9/15

Date

Atrazine (μg/L)

0

1000

2000

3000

4000

5000

6000

7000

8000

Daily Average Flow Yield (L/day/ha)

Grab Sample Conc, ug/L

Storm Events > 0.4 cm

Predicted Conc ug/L

Avg Daily Flow Yeild (L/day ha)

0

0.00002

0.00004

0.00006

0.00008

0.0001

0.00012

0.00014

0.00016

0.00018

Rain1Rain2Rain3Rain4Rain5Rain6Rain7Rain8Rain9

Rain10Rain11Rain12Rain13Rain14Rain15

Event

Atrazine Runoff/Break-through (kg/ha)
Page 88 of 96

Figure D.3- 70. Comparison of observed and predicted atrazine concentrations for the 

MO-02 watershed in 2007.

Figure D.3- 71. Comparison of atrazine runoff or break-through during rain events in 

excess of 0.4 cm over the period of monitoring for the MO-02 watershed in 2007.

MO-02 2007 Auto-samples

0

2

4

6

8

10

12

14

16

18

20

3/19 4/8 4/28 5/18 6/7 6/27 7/17 8/6 8/26 9/15

Date

Atrazine (μg/L)

0

1000

2000

3000

4000

5000

6000

7000

8000

Daily Average Flow Yield (L/day/ha)

Auto Sample Conc, ug/L

Storm Events > 0.4 cm

Predicted Conc ug/L

Avg Daily Flow Yeild (L/day ha)

0

0.00002

0.00004

0.00006

0.00008

0.0001

0.00012

0.00014

0.00016

0.00018

0.0002

Rain1Rain2Rain3Rain4Rain5Rain6Rain7Rain8Rain9

Rain10Rain11Rain12Rain13Rain14Rain15

Event

Atrazine Runoff/Break-through (kg/ha)
Page 89 of 96

Figure D.3- 72. Comparison of observed and predicted atrazine concentrations for the 

MO-02 watershed in 2007.

Figure D.3- 73. Comparison of atrazine runoff or break-through during rain events in 

excess of 0.4 cm over the period of monitoring for the MO-02 watershed in 2007.

MO-02 2007 Combined

0

2

4

6

8

10

12

14

16

18

20

3/9 4/28 6/17 8/6 9/25 11/14

Date

Atrazine (μg/L)

0

1000

2000

3000

4000

5000

6000

7000

8000

Daily Average Flow Yield (L/day/ha)

Auto Sample Conc, ug/L

Storm Events > 0.4 cm

Predicted Conc ug/L

Grab Sample Conc, ug/L

Avg Daily Flow Yeild (L/day ha)

0

0.00002

0.00004

0.00006

0.00008

0.0001

0.00012

0.00014

0.00016

0.00018

0.0002

Rain1Rain2Rain3Rain4Rain5Rain6Rain7Rain8Rain9

Rain10Rain11Rain12Rain13Rain14Rain15

Event

Atrazine Runoff/Break-through (kg/ha)
Page 90 of 96

Figure D.3- 74. Comparison of observed and predicted atrazine concentrations for the 

MO-02 watershed in 2008.

Figure D.3- 75. Comparison of atrazine runoff or break-through during rain events in 

excess of 0.4 cm over the period of monitoring for the MO-02 watershed in 2008.

MO-02 2008 Grab

0

10

20

30

40

50

60

70

3/13 4/2 4/22 5/12 6/1 6/21 7/11 7/31 8/20 9/9 9/29

Date

Atrazine (μg/L)

0

5000

10000

15000

20000

25000

30000

35000

40000

Daily Average Flow Yield (L/day/ha)

Grab Sample Conc, ug/L

Storm Events > 0.4 cm

Predicted Conc ug/L

Avg Daily Flow Yeild (L/day ha)

0

0.00005

0.0001

0.00015

0.0002

0.00025

0.0003

0.00035

0.0004

0.00045

0.0005

Rain1Rain3Rain5Rain7Rain9

Rain11Rain13Rain15Rain17Rain19Rain21Rain23Rain25Rain27Rain29Rain31Rain33Rain35

Event

Atrazine Runoff/Break-through (kg/ha)
Page 91 of 96

Figure D.3- 76. Comparison of observed and predicted atrazine concentrations for the 

MO-02 watershed in 2008.

Figure D.3- 77. Comparison of atrazine runoff or break-through during rain events in 

excess of 0.4 cm over the period of monitoring for the MO-02 watershed in 2008.

MO-02 2008 Auto-samples

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

3/13 4/2 4/22 5/12 6/1 6/21 7/11 7/31 8/20 9/9 9/29

Date

Atrazine (μg/L)

0

5000

10000

15000

20000

25000

30000

35000

40000

Daily Average Flow Yield (L/day/ha)

Auto Sample Conc, ug/L

Storm Events > 0.4 cm

Predicted Conc ug/L

Avg Daily Flow Yeild (L/day ha)

0

0.00005

0.0001

0.00015

0.0002

0.00025

Rain1Rain3Rain5Rain7Rain9

Rain11Rain13Rain15Rain17Rain19Rain21Rain23Rain25Rain27Rain29Rain31Rain33Rain35

Event

Atrazine Runoff/Break-through (kg/ha)
Page 92 of 96

Figure D.3- 78. Comparison of observed and predicted atrazine concentrations for the 

MO-02 watershed in 2008.

Figure D.3- 79. Comparison of atrazine runoff or break-through during rain events in 

excess of 0.4 cm over the period of monitoring for the MO-02 watershed in 2008.

MO-02 2008 Combined

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

2/22 4/12 6/1 7/21 9/9 10/29

Date

Atrazine (μg/L)

0

5000

10000

15000

20000

25000

30000

35000

40000

Daily Average Flow Yield (L/day/ha)

Auto Sample Conc, ug/L

Storm Events > 0.4 cm

Predicted Conc ug/L

Grab Sample Conc, ug/L

Avg Daily Flow Yeild (L/day ha)

0

0.00005

0.0001

0.00015

0.0002

0.00025

0.0003

Rain1Rain3Rain5Rain7Rain9

Rain11Rain13Rain15Rain17Rain19Rain21Rain23Rain25Rain27Rain29Rain31Rain33Rain35

Event

Atrazine Runoff/Break-through (kg/ha)
Page 93 of 96

Figure D.3- 80. Comparison of observed and predicted atrazine concentrations for the 

MO-04b watershed in 2008.

Figure D.3- 81. Comparison of atrazine runoff or break-through during rain events in 

excess of 0.4 cm over the period of monitoring for the MO-04b watershed in 2008.

MO-04b 2008 Grab

0.00

10.00

20.00

30.00

40.00

50.00

60.00

2/22 4/12 6/1 7/21 9/9 10/29

Date

Atrazine (μg/L)

0

20000

40000

60000

80000

100000

120000

140000

160000

180000

Daily Average Flow Yield (L/day/ha)

Grab Sample Conc, ug/L

Storm Events > 0.4 cm

Predicted Conc ug/L

Avg Daily Flow Yeild (L/day ha)

0

0.0005

0.001

0.0015

0.002

0.0025

0.003

0.0035

0.004

0.0045

Rain1Rain3Rain5Rain7Rain9

Rain11Rain13Rain15Rain17Rain19Rain21Rain23Rain25Rain27Rain31Rain33

Event

Atrazine Runoff/Break-through (kg/ha)
Page 94 of 96

Figure D.3- 82. Comparison of observed and predicted atrazine concentrations for the 

MO-04b watershed in 2008.

Figure D.3- 83. Comparison of atrazine runoff or break-through during rain events in 

excess of 0.4 cm over the period of monitoring for the MO-04b watershed in 2008.

MO-04b 2008 Auto-samples

0

20

40

60

80

100

120

140

2/22 4/12 6/1 7/21 9/9 10/29

Date

Atrazine (μg/L)

0

20000

40000

60000

80000

100000

120000

140000

160000

180000

Daily Average Flow Yield (L/day/ha)

Auto Sample Conc, ug/L

Storm Events > 0.4 cm

Predicted Conc ug/L

Avg Daily Flow Yeild (L/day ha)

0

0.0005

0.001

0.0015

0.002

0.0025

0.003

0.0035

0.004

0.0045

Rain1Rain3Rain5Rain7Rain9

Rain11Rain13Rain15Rain17Rain19Rain21Rain23Rain25Rain27Rain31Rain33

Event

Atrazine Runoff/Break-through (kg/ha)
Page 95 of 96

Figure D.3- 84. Comparison of observed and predicted atrazine concentrations for the 

MO-04b watershed in 2008.

Figure D.3- 85. Comparison of atrazine runoff or break-through during rain events in 

excess of 0.4 cm over the period of monitoring for the MO-04b watershed in 2008.

MO-04b 2008 Combined

0

10

20

30

40

50

60

70

2/22 4/12 6/1 7/21 9/9 10/29

Date

Atrazine (μg/L)

0

20000

40000

60000

80000

100000

120000

140000

160000

180000

Daily Average Flow Yield (L/day/ha)

Auto Sample Conc, ug/L

Storm Events > 0.4 cm

Predicted Conc ug/L

Avg Daily Flow Yeild (L/day ha)

0

0.0005

0.001

0.0015

0.002

0.0025

0.003

0.0035

0.004

0.0045

Rain1Rain3Rain5Rain7Rain9

Rain11Rain13Rain15Rain17Rain19Rain21Rain23Rain25Rain27Rain31Rain33

Event

Atrazine Runoff/Break-through (kg/ha)
Page 96 of 96

Figure D.3- 86. Comparison of observed and predicted atrazine concentrations for the 

NE-02 watershed in 2005.

Figure D.3- 87. Comparison of atrazine runoff or break-through during rain events in 

excess of 0.4 cm over the period of monitoring for the NE-02 watershed in 2005.

NE-02 2005 Grab

0.00

5.00

10.00

15.00

20.00

25.00

30.00

35.00

40.00

3/9 3/29 4/18 5/8 5/28 6/17 7/7 7/27 8/16 9/5

Date

Atrazine (μg/L)

0

50000

100000

150000

200000

250000

300000

350000

Daily Average Flow Yield (L/day/ha)

Grab Sample Conc, ug/L

Storm Events > 0.4 cm

Predicted Conc ug/L

Avg Daily Flow Yeild (L/day ha)

0

0.001

0.002

0.003

0.004

0.005

0.006

Rain1Rain2Rain3Rain4Rain5Rain6Rain7Rain8Rain9

Rain10Rain11Rain12Rain13Rain14Rain15Rain16Rain17Rain18Rain19Rain20

Event

Atrazine Runoff/Break-through (kg/ha)