02886naa a2200265 a 450000100080000000500110000800800410001910000110006024501210007126000090019250000200020152021130022165300100233465300110234465300270235565300150238265300220239765300150241965300190243465300220245365300250247565300090250065300200250977300910252910757332011-04-15       2008    bl uuuu     u00u1 u    #d1 aEpagri  aApplicability of a distributed watershed pollution model in a data-poor environment in Santa Catarina State, Brazil.  c2008  aISSN, 0100-0683  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 (&lt25 mm, 25-60 mm, &gt60 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.  aAgNPS  aBrasil  aExpert local knowledge  aMicrobacia  aModel calibration  aPig manure  aSanta Catarina  aScenario analysis  aSimulation modelling  aSoil  aWater pollution  tRevista Brasileira de Ci??ncia do Solo, Campinas, SPgv. 32, n. 4, p. 1699-1712, 2008.