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Keywords:

  • sensitivity;
  • surface water hydrology;
  • remote sensing;
  • simulation;
  • AGWA;
  • KINEROS2

Abstract:  A stochastic, spatially explicit method for assessing the impact of land cover classification error on distributed hydrologic modeling is presented. One-hundred land cover realizations were created by systematically altering the North American Landscape Characterization land cover data according to the dataset’s misclassification matrix. The matrix indicates the probability of errors of omission in land cover classes and is used to assess the uncertainty in hydrologic runoff simulation resulting from parameter estimation based on land cover. These land cover realizations were used in the GIS-based Automated Geospatial Watershed Assessment tool in conjunction with topography and soils data to generate input to the physically-based Kinematic Runoff and Erosion model. Uncertainties in modeled runoff volumes resulting from these land cover realizations were evaluated in the Upper San Pedro River basin for 40 watersheds ranging in size from 10 to 100 km2 under two rainfall events of differing magnitudes and intensities. Simulation results show that model sensitivity to classification error varies directly with respect to watershed scale, inversely to rainfall magnitude and are mitigated or magnified by landscape variability depending on landscape composition.