Abstract The event runoff coefficient (Rc) and the recession coefficient (tc) are of theoretical importance for understanding catchment response and of practical importance in hydrological design. We analyse 57 event periods in the period 2013 to 2015 in the 66 ha Austrian Hydrological Open Air Laboratory (HOAL), where the seven subcatchments are stratified by runoff generation types into wetlands, tile drainage and natural drainage. Three machine learning algorithms (Random forest (RF), Gradient Boost Decision Tree (GBDT) and Support vector machine (SVM)) are used to estimate Rc and tc from 22 event based explanatory variables representing precipitation, soil moisture, groundwater level and season. The model performance of the SVM algorithm in estimating Rc and tc is generally higher than that of the other two methods, measured by the coefficient of determination R2, and the performance for Rc is higher than that for tc. The relative importance of the explanatory variables for the predictions, assessed by a heatmap, suggests that Rc of the tile drainage systems is more strongly controlled by the weather conditions than by the catchment state, while the opposite is true for natural drainage systems. Overall, model performance strongly depends on the runoff generation type.

中文翻译：

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三种回归方法确定的不同径流产生机制的事件径流系数和衰退系数的控制

摘要 事件径流系数（Rc）和衰退系数（tc）对于理解流域响应具有重要的理论意义，在水文设计中具有实际意义。我们在 66 公顷的奥地利水文露天实验室 (HOAL) 中分析了 2013 年至 2015 年期间的 57 个事件期，其中七个子汇水面积按径流生成类型分为湿地、瓦片排水和自然排水。三种机器学习算法（随机森林 (RF)、梯度提升决策树 (GBDT) 和支持向量机 (SVM)）用于从 22 个基于事件的解释变量中估计 Rc 和 tc，这些解释变量代表降水、土壤湿度、地下水位和季节。SVM算法在估计Rc和tc方面的模型性能普遍高于其他两种方法，由决定系数 R2 衡量，Rc 的性能高于 tc。由热图评估的预测解释变量的相对重要性表明，瓦片排水系统的 Rc 受天气条件的控制比流域状态更强烈，而自然排水系统则相反。总体而言，模型性能在很大程度上取决于径流生成类型。