Mathematical models of the quantity and quality of water in hydrographic basins enable simulation of a wide variety of processes, including the production of water and sediments, and the dynamics of point and nonpoint...Mathematical models of the quantity and quality of water in hydrographic basins enable simulation of a wide variety of processes, including the production of water and sediments, and the dynamics of point and nonpoint sources of pollution. These models have become increasingly complex, requiring large amounts of input data, which can increase the uncertainty of the results of simulations. For this reason, it is essential to perform calibration and validation procedures. The objective of this work was to conduct sensitivity analysis and calibration of a distributed hydrological model (SWAT) applied to the flows of water in the watershed of the Poxim River. Satisfactory performance of the model was indicated by the values obtained for the Nash-Sutcliffe efficiency coefficient (0.77), the percent bias (5.05), the root mean square error (0.48), and the ratio of the RMSE to the standard deviation of the observations (RSR) (0.49). The set of parameters identified here could be used for the simulation and evaluation of other scenarios.展开更多
文摘Mathematical models of the quantity and quality of water in hydrographic basins enable simulation of a wide variety of processes, including the production of water and sediments, and the dynamics of point and nonpoint sources of pollution. These models have become increasingly complex, requiring large amounts of input data, which can increase the uncertainty of the results of simulations. For this reason, it is essential to perform calibration and validation procedures. The objective of this work was to conduct sensitivity analysis and calibration of a distributed hydrological model (SWAT) applied to the flows of water in the watershed of the Poxim River. Satisfactory performance of the model was indicated by the values obtained for the Nash-Sutcliffe efficiency coefficient (0.77), the percent bias (5.05), the root mean square error (0.48), and the ratio of the RMSE to the standard deviation of the observations (RSR) (0.49). The set of parameters identified here could be used for the simulation and evaluation of other scenarios.
