Algeria has experienced catastrophic floods over the second half of the twentieth century, causing many deaths and extensive material damage. This study was conducted to find a suitable event-based rainfall-runoff (RR) model for semi-arid conditions, where continuous data are not available in all regional basins. The study compared, based on data availability, the SCS-CN model based on the antecedent moisture conditions (AMC) and four modified SCS-CN models incorporating antecedent moisture amounts (AMA) in order to find the best model to reproduce the flood hydrographs in two catchments. The modified models were predominant over the SCS-CN method. Nonetheless, the Singh et al. (Water Resour Manag 29:4111–4127, 2015. https://doi.org/10.1007/s11269-015-1048-1) model (M4) and the Verma et al. (Environ Earth Sci 76:736, 2017a. https://doi.org/10.1007/s12665-017-7062-2) model (M5) were superior and demonstrated more stable structures. Coupled with the Hayami routing model, the models showed promising results and were able to reproduce the observed hydrographs’ shape. However, it was impossible to choose the preferred model since they each excelled as to a criterion. Therefore, the corresponding outputs were combined using the simple average (SA) method and the weighted average (WA) method. We found that the WA method showed better results in the two catchments and allowed a more accurate prediction according to the performance criteria.

阿尔及利亚在20世纪下半叶经历了灾难性的洪灾，造成许多人死亡和广泛的物质损失。进行这项研究的目的是为半干旱条件找到合适的基于事件的降雨径流（RR）模型，该模型无法在所有区域流域获得连续数据。这项研究根据数据的可用性，比较了基于先验湿度条件（AMC）的SCS-CN模型和结合了先验水分量（AMA）的四个经过修改的SCS-CN模型，以便找到最佳的模型来再现洪灾水文图。两个流域。修改后的模型比SCS-CN方法更重要。尽管如此，辛格等。（Water Resour Manag 29：4111–4127，2015. https://doi.org/10.1007/s11269-015-1048-1）模型（M4）和Verma等人。（Environ Earth Sci 76：736，2017a.https：//doi.org/10。1007 / s12665-017-7062-2）模型（M5）优越，并显示出更稳定的结构。结合Hayami路由模型，这些模型显示出令人鼓舞的结果，并且能够重现观察到的水文图形的形状。但是，选择首选模型是不可能的，因为它们在标准方面均表现出色。因此，使用简单平均（SA）方法和加权平均（WA）方法将相应的输出组合在一起。我们发现WA方法在两个流域显示出更好的结果，并根据性能标准进行了更准确的预测。选择首选模型是不可能的，因为它们在标准方面都表现出色。因此，使用简单平均（SA）方法和加权平均（WA）方法将相应的输出组合在一起。我们发现WA方法在两个流域显示出更好的结果，并根据性能标准进行了更准确的预测。选择首选模型是不可能的，因为它们在标准方面都表现出色。因此，使用简单平均（SA）方法和加权平均（WA）方法将相应的输出组合在一起。我们发现WA方法在两个流域显示出更好的结果，并根据性能标准进行了更准确的预测。