Emerging large ensemble climate datasets produced by multiple general circulation models and their downscaling products challenge the limits of hydrodynamic models because of the immense data size. To overcome this new challenge and estimate the discharge quantiles corresponding to different return periods at all river sections in an entire region, this study proposes an event-based regional approach that uses a nationwide distributed rainfall–runoff model as well as large climate projection ensembles. This approach addresses the high computational burden associated with continuous simulations and solves the problem of conventional event-based simulations serving only a single outlet of a basin. For our analysis, we extracted 372 annual maximum 48 h rainfall events that cover the entirety of Shikoku Island and its eight major river basins. Peak discharges were estimated using a 150 m resolution rainfall–runoff–inundation model. These discharges were then screened using either the peak-over-threshold (POT) method or block maxima (BM) method, and frequency curves were subsequently constructed and evaluated. The primary reason for the necessity of POT or BM was to avoid interference from extraneous low discharges. The POT-based frequency curves showed good accuracy when using peak discharges in the range of the top 10–50%, and the results remain stable within this threshold range. The BM method, employing block sizes of 2–5 years, can generate relatively accurate frequency curves, but the choice of block size introduces significant variations in results among certain basins. Generally, the accuracy of results based on the POT method surpasses that of the BM method. Considering the accuracy, computational cost, and result stability, the POT method is preferred. The error introduced by the regional approach was acceptable with more than half of the relative root-mean-square errors remaining within 10% and basically all of the results are within 20%. The results of the regional approach exhibited good accuracy across climate scenarios and provided consistent information regarding future flood quantiles. This study serves as the foundation for high-resolution future flood risk assessment.

由于数据量巨大，由多个大气环流模型及其降尺度产品产生的新兴大型集合气候数据集挑战了水动力模型的极限。为了克服这一新挑战并估计整个地区所有河流断面不同重现期对应的流量分位数，本研究提出了一种基于事件的区域方法，该方法使用全国分布式降雨径流模型以及大型气候预测集合。这种方法解决了与连续模拟相关的高计算负担，并解决了仅服务流域单个出口的传统基于事件的模拟的问题。为了进行分析，我们提取了 372 个年度最大 48 小时降雨事件，覆盖整个四国岛及其八个主要河流流域。使用 150 m 分辨率的降雨-径流-洪水模型估算峰值流量。然后使用峰值阈值 (POT) 方法或块最大值 (BM) 方法筛选这些放电，并随后构建和评估频率曲线。需要 POT 或 BM 的主要原因是为了避免外部低放电的干扰。当使用前 10-50% 范围内的峰值放电时，基于 POT 的频率曲线显示出良好的准确性，并且结果在此阈值范围内保持稳定。 BM方法采用2-5年的块大小，可以生成相对准确的频率曲线，但块大小的选择会导致某些盆地之间的结果存在显着差异。一般来说，基于POT方法的结果的准确性优于BM方法。考虑到准确性、计算成本和结果稳定性，优选POT方法。区域方法引入的误差是可以接受的，超过一半的相对均方根误差保持在10%以内，基本上所有结果都在20%以内。区域方法的结果在整个气候情景中表现出良好的准确性，并提供了有关未来洪水分位数的一致信息。这项研究为高分辨率未来洪水风险评估奠定了基础。