Prediction of rainfall-runoff process, peak discharges, and finally flood hydrograph is essential for flood risk management and river engineering projects. In most previous studies in this field, the precipitation rates have been entered into the models without considering seasonal and monthly distribution. In this study, the daily precipitation data of 144 climatology stations in Iran were used to evaluate the seasonal and monthly pattern of flood-causing precipitation. Then, by determining the rainy seasons and seasonal fit of precipitation with a probabilistic model and using regional precipitation, a semi-distributed conceptual model of rainfall-runoff (MORDOR-SD) was trained and validated using the observed discharge data. Flood prediction was performed using climatic data, modeling of hydrological conditions, and extreme flow data with high performance. According to the results, the Nash–Sutcliffe and Kling–Gupta coefficients were 0.69 and 0.82 for the mean daily streamflow, 0.98 and 0.98 for the seasonal streamflow, 0.98 and 0.94 for the maximum discharges, and 0.57 and 0.78 for low flows, respectively. Moreover, the maximum daily discharges in different return periods were estimated using the results of the MORDOR-SD model, considering the probability distribution function of the probabilistic model of central precipitation (MEWP), the probabilistic model of adjacent precipitation, and probability distribution function of the previous precipitation. Finally, the extreme flows were predicted and compared using different methods including the SCHADEX, regional flood analysis, GRADEX, and AGREGEE. The results showed that the methods GRADEX, AGREGEE, and SCHADEX have the highest performance in predicting extreme floods, respectively.

降雨径流过程、峰值流量以及最终洪水过程线的预测对于洪水风险管理和河流工程项目至关重要。在该领域的大多数先前研究中，降水率已被输入到模型中，而没有考虑季节和月份分布。在这项研究中，利用伊朗144个气候站的每日降水数据来评估导致洪水的降水的季节和月份模式。然后，通过概率模型确定雨季和降水季节拟合并使用区域降水，使用观测流量数据训练和验证降雨径流半分布式概念模型（MORDOR-SD）。利用气候数据、水文条件建模和高性能极端流量数据进行洪水预测。根据结果​​，Nash-Sutcliffe 系数和 Kling-Gupta 系数分别为：日平均流量为 0.69 和 0.82，季节流量为 0.98 和 0.98，最大流量为 0.98 和 0.94，低流量为 0.57 和 0.78。此外，利用MORDOR-SD模型的结果，考虑中心降水概率模型（MEWP）的概率分布函数、邻近降水概率模型和之前的降水。最后，利用SCHADEX、区域洪水分析、GRADEX和AGREEGEE等不同方法对极端流量进行了预测和比较。结果表明，GRADEX、AGREEGEE 和 SCHADEX 方法在预测极端洪水方面分别具有最高的性能。