Improving Efficiency of Hydrological Prediction Based on Meteorological Classification: A Case Study of GR4J Model

Distribution of hydrological parameters is varied under contrasting meteorological conditions. However, how to determine the most suitable on a predefined condition challenging. To address this issue, prediction method based classification established, which conducted by using standardized runoff index (SRI) value identify three categories, i.e., dry, normal and wet years. Three different simulation schemes are then adopted for these categories. In each category, two years data with similar SRI values divided into set; then, one-year used as calibration period while other year testing. The Génie Rural à 4 paramètres Journalier (GR4J) rainfall-runoff model, four x1, x2, x3 x4, was selected an experimental model. generalized likelihood uncertainty estimation (GLUE) avoid parameter equifinality. basins in Australia were case studies. As expected, results show that distribution GR4J model significantly efficiency testing greater than traditional all It indicated should be calibrated rather This study provides new improve basin.