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

中文翻译：

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提高基于气象分类的水文预测效率：以GR4J模型为例

在不同的气象条件下，水文参数的分布是不同的。然而，如何在预定义的气象条件下确定最合适的参数具有挑战性。针对这一问题，建立了一种基于气象分类的水文预测方法，该方法利用标准化径流指数（SRI）值来确定干旱年、正常年和湿润年三个类别。然后对这些类别采用三种不同的模拟方案。在每一类中，将具有相似SRI值的两年水文数据分为一组；然后以一年的数据作为校准期，另一年的数据用于测试。Génie Rural à 4 paramètres Journalier (GR4J) 降雨径流模型，具有四个参数x ₁、x ₂ 、x ₃和x ₄被选为实验模型。广义似然不确定性估计 (GLUE) 方法用于避免参数等价性。澳大利亚的三个盆地被用作案例研究。正如预期的那样，结果表明 GR4J 模型的四个参数的分布在不同的气象条件下存在显着差异。在所有气象条件下，基于气象分类的测试期预测效率均高于传统模型。这表明降雨径流模型应该用相似的SRI年份而不是所有年份进行校准。该研究为提高流域水文预测效率提供了一种新方法。