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Evaluation of the Root Zone Water Quality Model Using Field‐Measured Data from the Missouri MSEA
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| Zeitschriftentitel: | Agronomy Journal |
|---|---|
| Personen und Körperschaften: | , , |
| In: | Agronomy Journal, 91, 1999, 2, S. 183-192 |
| Format: | E-Article |
| Sprache: | Englisch |
| veröffentlicht: |
Wiley
|
| Schlagwörter: |
| author_facet |
Ghidey, Fessehaie Alberts, E. Eugene Kitchen, Newell R. Ghidey, Fessehaie Alberts, E. Eugene Kitchen, Newell R. |
|---|---|
| author |
Ghidey, Fessehaie Alberts, E. Eugene Kitchen, Newell R. |
| spellingShingle |
Ghidey, Fessehaie Alberts, E. Eugene Kitchen, Newell R. Agronomy Journal Evaluation of the Root Zone Water Quality Model Using Field‐Measured Data from the Missouri MSEA Agronomy and Crop Science |
| author_sort |
ghidey, fessehaie |
| spelling |
Ghidey, Fessehaie Alberts, E. Eugene Kitchen, Newell R. 0002-1962 1435-0645 Wiley Agronomy and Crop Science http://dx.doi.org/10.2134/agronj1999.00021962009100020004x <jats:title>Abstract</jats:title><jats:p>The USDA‐ARS Root Zone Water Quality Model (RZWQM) is a comprehensive computer model developed to simulate water, chemical, and biological processes in the root zone of agricultural management systems. The model is capable of evaluating the effects of various cropping and management practices on surface and ground water quality. In this study, the performance of RZWQM Version 3.2 was evaluated for a claypan soil, particularly surface runoff and chemical loss to surface runoff predictions. The model was calibrated and evaluated using data collected from the Missouri Management Systems Evaluation Area (MSEA) and the Kingdom City runoff plots. Soil water predictions of the model compared well with those measured, particularly at the 15−, 60−, 75−, and 90−cm soil depths. In most cases, corn (<jats:italic>Zea mays</jats:italic> L.) and soybean [<jats:italic>Glycine max</jats:italic> (L.) Merr.] yield predictions were within 15 and 20%, respectively, of those measured. Using the macropore option (constant cracking) greatly improved the prediction of chemical losses to seepage. Annual runoff simulated for corn and soybean under conventional and no‐till systems was adequately predicted. The model underpredicted large runoff events and overpredicted runoff events that occurred after long dry periods when soil cracking was a dominant factor. The model overpredicted NO<jats:sub>3</jats:sub>−N concentrations in runoff but underpredicted concentrations in near‐surface soils. Predicted and measured atrazine [6‐chloro‐<jats:italic>N</jats:italic>‐ethyl‐<jats:italic>N</jats:italic>′‐(1‐methylethyl)‐1,3,5‐triazine‐2,4‐diamine] and alachlor [2‐chloro‐<jats:italic>N</jats:italic>‐(2,6‐diethylphenyl)‐<jats:italic>N</jats:italic>‐(methoxymethyl)acetamide) concentrations in surface runoff compared well, particularly when the computed runoff was close to that measured. In this study, the model was run using the option of constant cracking in the soil. To improve the predictions of agrichemical losses to runoff and seepage, RZWQM should include the capability to predict variable soil cracking based on soil moisture.</jats:p> Evaluation of the Root Zone Water Quality Model Using Field‐Measured Data from the Missouri MSEA Agronomy Journal |
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1999 |
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| title |
Evaluation of the Root Zone Water Quality Model Using Field‐Measured Data from the Missouri MSEA |
| title_unstemmed |
Evaluation of the Root Zone Water Quality Model Using Field‐Measured Data from the Missouri MSEA |
| title_full |
Evaluation of the Root Zone Water Quality Model Using Field‐Measured Data from the Missouri MSEA |
| title_fullStr |
Evaluation of the Root Zone Water Quality Model Using Field‐Measured Data from the Missouri MSEA |
| title_full_unstemmed |
Evaluation of the Root Zone Water Quality Model Using Field‐Measured Data from the Missouri MSEA |
| title_short |
Evaluation of the Root Zone Water Quality Model Using Field‐Measured Data from the Missouri MSEA |
| title_sort |
evaluation of the root zone water quality model using field‐measured data from the missouri msea |
| topic |
Agronomy and Crop Science |
| url |
http://dx.doi.org/10.2134/agronj1999.00021962009100020004x |
| publishDate |
1999 |
| physical |
183-192 |
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<jats:title>Abstract</jats:title><jats:p>The USDA‐ARS Root Zone Water Quality Model (RZWQM) is a comprehensive computer model developed to simulate water, chemical, and biological processes in the root zone of agricultural management systems. The model is capable of evaluating the effects of various cropping and management practices on surface and ground water quality. In this study, the performance of RZWQM Version 3.2 was evaluated for a claypan soil, particularly surface runoff and chemical loss to surface runoff predictions. The model was calibrated and evaluated using data collected from the Missouri Management Systems Evaluation Area (MSEA) and the Kingdom City runoff plots. Soil water predictions of the model compared well with those measured, particularly at the 15−, 60−, 75−, and 90−cm soil depths. In most cases, corn (<jats:italic>Zea mays</jats:italic> L.) and soybean [<jats:italic>Glycine max</jats:italic> (L.) Merr.] yield predictions were within 15 and 20%, respectively, of those measured. Using the macropore option (constant cracking) greatly improved the prediction of chemical losses to seepage. Annual runoff simulated for corn and soybean under conventional and no‐till systems was adequately predicted. The model underpredicted large runoff events and overpredicted runoff events that occurred after long dry periods when soil cracking was a dominant factor. The model overpredicted NO<jats:sub>3</jats:sub>−N concentrations in runoff but underpredicted concentrations in near‐surface soils. Predicted and measured atrazine [6‐chloro‐<jats:italic>N</jats:italic>‐ethyl‐<jats:italic>N</jats:italic>′‐(1‐methylethyl)‐1,3,5‐triazine‐2,4‐diamine] and alachlor [2‐chloro‐<jats:italic>N</jats:italic>‐(2,6‐diethylphenyl)‐<jats:italic>N</jats:italic>‐(methoxymethyl)acetamide) concentrations in surface runoff compared well, particularly when the computed runoff was close to that measured. In this study, the model was run using the option of constant cracking in the soil. To improve the predictions of agrichemical losses to runoff and seepage, RZWQM should include the capability to predict variable soil cracking based on soil moisture.</jats:p> |
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| author | Ghidey, Fessehaie, Alberts, E. Eugene, Kitchen, Newell R. |
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| description | <jats:title>Abstract</jats:title><jats:p>The USDA‐ARS Root Zone Water Quality Model (RZWQM) is a comprehensive computer model developed to simulate water, chemical, and biological processes in the root zone of agricultural management systems. The model is capable of evaluating the effects of various cropping and management practices on surface and ground water quality. In this study, the performance of RZWQM Version 3.2 was evaluated for a claypan soil, particularly surface runoff and chemical loss to surface runoff predictions. The model was calibrated and evaluated using data collected from the Missouri Management Systems Evaluation Area (MSEA) and the Kingdom City runoff plots. Soil water predictions of the model compared well with those measured, particularly at the 15−, 60−, 75−, and 90−cm soil depths. In most cases, corn (<jats:italic>Zea mays</jats:italic> L.) and soybean [<jats:italic>Glycine max</jats:italic> (L.) Merr.] yield predictions were within 15 and 20%, respectively, of those measured. Using the macropore option (constant cracking) greatly improved the prediction of chemical losses to seepage. Annual runoff simulated for corn and soybean under conventional and no‐till systems was adequately predicted. The model underpredicted large runoff events and overpredicted runoff events that occurred after long dry periods when soil cracking was a dominant factor. The model overpredicted NO<jats:sub>3</jats:sub>−N concentrations in runoff but underpredicted concentrations in near‐surface soils. Predicted and measured atrazine [6‐chloro‐<jats:italic>N</jats:italic>‐ethyl‐<jats:italic>N</jats:italic>′‐(1‐methylethyl)‐1,3,5‐triazine‐2,4‐diamine] and alachlor [2‐chloro‐<jats:italic>N</jats:italic>‐(2,6‐diethylphenyl)‐<jats:italic>N</jats:italic>‐(methoxymethyl)acetamide) concentrations in surface runoff compared well, particularly when the computed runoff was close to that measured. In this study, the model was run using the option of constant cracking in the soil. To improve the predictions of agrichemical losses to runoff and seepage, RZWQM should include the capability to predict variable soil cracking based on soil moisture.</jats:p> |
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| spelling | Ghidey, Fessehaie Alberts, E. Eugene Kitchen, Newell R. 0002-1962 1435-0645 Wiley Agronomy and Crop Science http://dx.doi.org/10.2134/agronj1999.00021962009100020004x <jats:title>Abstract</jats:title><jats:p>The USDA‐ARS Root Zone Water Quality Model (RZWQM) is a comprehensive computer model developed to simulate water, chemical, and biological processes in the root zone of agricultural management systems. The model is capable of evaluating the effects of various cropping and management practices on surface and ground water quality. In this study, the performance of RZWQM Version 3.2 was evaluated for a claypan soil, particularly surface runoff and chemical loss to surface runoff predictions. The model was calibrated and evaluated using data collected from the Missouri Management Systems Evaluation Area (MSEA) and the Kingdom City runoff plots. Soil water predictions of the model compared well with those measured, particularly at the 15−, 60−, 75−, and 90−cm soil depths. In most cases, corn (<jats:italic>Zea mays</jats:italic> L.) and soybean [<jats:italic>Glycine max</jats:italic> (L.) Merr.] yield predictions were within 15 and 20%, respectively, of those measured. Using the macropore option (constant cracking) greatly improved the prediction of chemical losses to seepage. Annual runoff simulated for corn and soybean under conventional and no‐till systems was adequately predicted. The model underpredicted large runoff events and overpredicted runoff events that occurred after long dry periods when soil cracking was a dominant factor. The model overpredicted NO<jats:sub>3</jats:sub>−N concentrations in runoff but underpredicted concentrations in near‐surface soils. Predicted and measured atrazine [6‐chloro‐<jats:italic>N</jats:italic>‐ethyl‐<jats:italic>N</jats:italic>′‐(1‐methylethyl)‐1,3,5‐triazine‐2,4‐diamine] and alachlor [2‐chloro‐<jats:italic>N</jats:italic>‐(2,6‐diethylphenyl)‐<jats:italic>N</jats:italic>‐(methoxymethyl)acetamide) concentrations in surface runoff compared well, particularly when the computed runoff was close to that measured. In this study, the model was run using the option of constant cracking in the soil. To improve the predictions of agrichemical losses to runoff and seepage, RZWQM should include the capability to predict variable soil cracking based on soil moisture.</jats:p> Evaluation of the Root Zone Water Quality Model Using Field‐Measured Data from the Missouri MSEA Agronomy Journal |
| spellingShingle | Ghidey, Fessehaie, Alberts, E. Eugene, Kitchen, Newell R., Agronomy Journal, Evaluation of the Root Zone Water Quality Model Using Field‐Measured Data from the Missouri MSEA, Agronomy and Crop Science |
| title | Evaluation of the Root Zone Water Quality Model Using Field‐Measured Data from the Missouri MSEA |
| title_full | Evaluation of the Root Zone Water Quality Model Using Field‐Measured Data from the Missouri MSEA |
| title_fullStr | Evaluation of the Root Zone Water Quality Model Using Field‐Measured Data from the Missouri MSEA |
| title_full_unstemmed | Evaluation of the Root Zone Water Quality Model Using Field‐Measured Data from the Missouri MSEA |
| title_short | Evaluation of the Root Zone Water Quality Model Using Field‐Measured Data from the Missouri MSEA |
| title_sort | evaluation of the root zone water quality model using field‐measured data from the missouri msea |
| title_unstemmed | Evaluation of the Root Zone Water Quality Model Using Field‐Measured Data from the Missouri MSEA |
| topic | Agronomy and Crop Science |
| url | http://dx.doi.org/10.2134/agronj1999.00021962009100020004x |