The Effect of Hydrologic Model and Data Complexity on Water Quantity and Quality Prediction Accuracy

Abstract

Hydrologic modeling is central to the solution of many flooding and water quality issues. As the complexity of these issues increases, model complexity increases. The purpose of this research was to determine the effects of model and data complexity on hydrologic model prediction accuracy. A complex hydrologic model was developed and then simplified based on structural complexity and the change in accuracy was assessed. Analyses of data complexity were also conducted. The results showed that complex models containing excessive low sensitivity parameters did not significantly improve prediction accuracy. However, a lack of complete representation of the physical processes of the hydrologic cycle did affect prediction accuracy. Data analyses revealed that misalignments between rainfall and runoff gauges may cause poor prediction of peaks and grab samples may adequately represent the mean value but not the distribution of a population. Guidelines were developed to improve future development and application of hydrologic models.