Few studies have explored the impact of drivers on urban flood at multi-time scales, and few studies have assessed the urban flood risk based on accurate description and quantification of the intrinsic correlation between urban flood and its drivers. Therefore, this study develops a multiscale-multivariate prediction model for flood volume (FV) using wavelet transform, and a flood risk assessment model based on the joint distribution of FV and its key drivers using the Copula method. The downtown area of Zhengzhou City, a typical city in North China, is taken as the study area. The results reveal that land use change is the key driver for variation of urban rainstorm flood, and rapid urbanization led to the variation observed in 2005. The impact of land use change on FV primarily manifests at the long cycle scale, and the multiscale-multivariate prediction model demonstrates effective simulation (NSE = 0.957, R² = 0.958, MAPE = 13.47%) and prediction capabilities (relative errors are all below 20%). Taking the FV exceeding the threshold corresponding to the frequency of 37.5% as an example, the maximum conditional risk probability under nine combinations of key drivers is 88.34%, while the minimum probability is only 6.83%, intuitively indicating the impact of rainstorm and urbanization on this risk. These findings can provide technical references for urban flood forecasting, urban water resources management and urban development planning.

很少有研究探讨多时间尺度的驱动因素对城市洪水的影响，也很少有研究基于准确描述和量化城市洪水与其驱动因素之间的内在相关性来评估城市洪水风险。因此，本研究利用小波变换开发了洪水量（FV）的多尺度多元预测模型，并利用Copula方法开发了基于FV及其关键驱动因素联合分布的洪水风险评估模型。以华北典型城市郑州市城区为研究区域。结果表明，土地利用变化是城市暴雨洪水变化的关键驱动因素，快速城市化导致了2005年的变化。土地利用变化对FV的影响主要表现在长周期尺度，多尺度多变量的影响。预测模型展示了有效的模拟（ NSE  = 0.957， R ²  = 0.958， MAPE  = 13.47%）和预测能力（相对误差均低于20%）。以频率 37.5%对应的 FV 超过阈值为例，九种关键驱动因素组合下条件风险概率最大为 88.34%，最小概率仅为 6.83%，直观地表明了暴雨和城镇化对城市化的影响。这种风险。这些研究结果可为城市洪水预报、城市水资源管理和城市发展规划提供技术参考。