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| 15. |  | SILVA, E. B.; BACIC, I. L. Z. Análise espacial do conflito de uso das terras em SIG: assentamento Eldorado dos Carajás, lebon Régis, SC. In: SIMPÓSIO BRASILEIRO DE CIÊNCIAS GEODÉSICAS E TECNOLOGIAS DA GEOINFORMAÇÃO, 3., 2010, Recife, PE. Anais... Recife, PE: UFPE, 2010.| Biblioteca(s): Epagri-Sede. |
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| 20. | | BACIC, I. L. Z.; ROSSITER, D. G.; MANNAERTS, C. M. Applicability of a distributed watershed pollution model in a data-poor environment in Santa Catarina State, Brazil. Revista Brasileira de Ciência do Solo, Campinas, SP, v. 32, n. 4, p. 1699-1712, 2008. ISSN, 0100-0683| Biblioteca(s): Epagri-Sede. |
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Registro Completo
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Biblioteca(s): |
Epagri-Sede. |
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Data corrente: |
15/04/2011 |
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Data da última atualização: |
15/04/2011 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
-- - -- |
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Autoria: |
BACIC, I. L. Z.; ROSSITER, D. G.; MANNAERTS, C. M. |
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Afiliação: |
Epagri |
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Título: |
Applicability of a distributed watershed pollution model in a data-poor environment in Santa Catarina State, Brazil. |
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Ano de publicação: |
2008 |
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Fonte/Imprenta: |
Revista Brasileira de Ciência do Solo, Campinas, SP, v. 32, n. 4, p. 1699-1712, 2008. |
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Idioma: |
Inglês |
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Notas: |
ISSN, 0100-0683 |
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Conteúdo: |
Intensification of agricultural production without sound management and regulation can lead to severe environmental problems, such as in western Santa Catarina state, Brazil, where intensive swine production causes large accumulations of manure and consequently water pollution. Natural resources scientists are asked by decision-makers for advice on management and regulatory decisions. Distributed environmental models are useful tools, since they can be used to explore consequences of various management practices. However, in many areas of the world quantitative data for model calibration and validation are lacking. The data-intensive distributed environmental model AgNPS was applied in a data-poor environment, the upper catchment (2,520 ha) of the Ariranhazinho river, near Seara town in Santa Catarina state. Steps included data preparation, cell size selection, sensitivity analysis, model calibration and application to different management scenarios. The model was calibrated by making a best guess for model parameters and then performing a pragmatic sensitivity analysis. The parameters were adjusted so that model outputs (runoff volume, peak runoff rate and sediment concentration) closely matched the sparse observed data. A modelling grid cell resolution of 150 m gave appropriate results while being computationally feasible. The rainfall runoff response of the AgNPS model could be calibrated using three rainfall ranges separately (<25 mm, 25-60 mm, >60 mm). Predicted sediment concentrations were consistently six to ten times higher than actual, probably because of sediment trapping by vegetated channel banks. Predicted N and P concentrations in stream water ranged from just below to well above regulatory norms. Expert knowledge of the area, in addition to experience reported in literature, was able to compensate in part for limited calibration data. Using the model, it was possible to compare scenarios (actual, recommended and excessive manure applications, and point source pollution from swine operations) using a relative ranking rather than quantitative predictions. MenosIntensification of agricultural production without sound management and regulation can lead to severe environmental problems, such as in western Santa Catarina state, Brazil, where intensive swine production causes large accumulations of manure and consequently water pollution. Natural resources scientists are asked by decision-makers for advice on management and regulatory decisions. Distributed environmental models are useful tools, since they can be used to explore consequences of various management practices. However, in many areas of the world quantitative data for model calibration and validation are lacking. The data-intensive distributed environmental model AgNPS was applied in a data-poor environment, the upper catchment (2,520 ha) of the Ariranhazinho river, near Seara town in Santa Catarina state. Steps included data preparation, cell size selection, sensitivity analysis, model calibration and application to different management scenarios. The model was calibrated by making a best guess for model parameters and then performing a pragmatic sensitivity analysis. The parameters were adjusted so that model outputs (runoff volume, peak runoff rate and sediment concentration) closely matched the sparse observed data. A modelling grid cell resolution of 150 m gave appropriate results while being computationally feasible. The rainfall runoff response of the AgNPS model could be calibrated using three rainfall ranges separately (<25 mm, 25-60 mm, >60 mm). Predicted sedimen... Mostrar Tudo |
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Palavras-Chave: |
AgNPS; Brasil; Expert local knowledge; Microbacia; Model calibration; Pig manure; Santa Catarina; Scenario analysis; Simulation modelling; Soil; Water pollution. |
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Categoria do assunto: |
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Marc: |
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008 2008 bl uuuu u00u1 u #d
100 1 $aEpagri
245 $aApplicability of a distributed watershed pollution model in a data-poor environment in Santa Catarina State, Brazil.
260 $c2008
500 $aISSN, 0100-0683
520 $aIntensification of agricultural production without sound management and regulation can lead to severe environmental problems, such as in western Santa Catarina state, Brazil, where intensive swine production causes large accumulations of manure and consequently water pollution. Natural resources scientists are asked by decision-makers for advice on management and regulatory decisions. Distributed environmental models are useful tools, since they can be used to explore consequences of various management practices. However, in many areas of the world quantitative data for model calibration and validation are lacking. The data-intensive distributed environmental model AgNPS was applied in a data-poor environment, the upper catchment (2,520 ha) of the Ariranhazinho river, near Seara town in Santa Catarina state. Steps included data preparation, cell size selection, sensitivity analysis, model calibration and application to different management scenarios. The model was calibrated by making a best guess for model parameters and then performing a pragmatic sensitivity analysis. The parameters were adjusted so that model outputs (runoff volume, peak runoff rate and sediment concentration) closely matched the sparse observed data. A modelling grid cell resolution of 150 m gave appropriate results while being computationally feasible. The rainfall runoff response of the AgNPS model could be calibrated using three rainfall ranges separately (<25 mm, 25-60 mm, >60 mm). Predicted sediment concentrations were consistently six to ten times higher than actual, probably because of sediment trapping by vegetated channel banks. Predicted N and P concentrations in stream water ranged from just below to well above regulatory norms. Expert knowledge of the area, in addition to experience reported in literature, was able to compensate in part for limited calibration data. Using the model, it was possible to compare scenarios (actual, recommended and excessive manure applications, and point source pollution from swine operations) using a relative ranking rather than quantitative predictions.
653 $aAgNPS
653 $aBrasil
653 $aExpert local knowledge
653 $aMicrobacia
653 $aModel calibration
653 $aPig manure
653 $aSanta Catarina
653 $aScenario analysis
653 $aSimulation modelling
653 $aSoil
653 $aWater pollution
773 $tRevista Brasileira de Ciência do Solo, Campinas, SP$gv. 32, n. 4, p. 1699-1712, 2008.
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