The present research introduces a model to find the best shape of a dam's spillway under climate change impacts, considering a benchmark problem (i.e., Ute Dam's labyrinth spillway in the Canadian River watershed, New Mexico, USA). A spillway design is based not only on historical data but also on the future hydrologic events. Climate variables were predicted for the years 2021–2050 based on three representative concentration pathway (RCP2.6, RCP4.5, and RCP8.5) scenarios of the general circulation model from the fifth phase of the coupled model intercomparison project (CMIP5) using the statistical downscaling model. Streamflow at the USGS 07226500 streamgage was simulated by a rainfall–runoff model with predicted data. Instantaneous peak flow was estimated using an empirical method. Flood frequency analysis was used for the estimation of the design flood. The shuffled frog-leaping algorithm (SFLA) is used to optimize a labyrinth spillway design and its results were compared with two other nature-inspired algorithms: invasive weed optimization (IWO) and cuckoo search (CS). The spillway was optimized once with the actual design flood (16,143 m³/s) and again with the design flood under climate change (12,250 m³/s). Results revealed that optimization with realistic design flood reduced the concrete volume of the spillway by 37% and under climate change by 43% using the SFLA.

本研究介绍了一种模型，该模型考虑了基准问题（例如，美国新墨西哥州加拿大河流域的优特大坝的迷宫式溢洪道），可以在气候变化影响下找到最佳的溢洪道形状。溢洪道设计不仅基于历史数据，而且还基于未来的水文事件。根据耦合模型比较项目（CMIP5）第五阶段的一般循环模型的三个代表性浓度路径（RCP2.6，RCP4.5和RCP8.5）情景，预测了2021-2050年的气候变量统计缩减模型。USGS 07226500流量的流量是通过降雨径流模型和预测数据进行模拟的。使用经验方法估算瞬时峰值流量。使用洪水频率分析来估算设计洪水。改组的蛙跳算法（SFLA）用于优化迷宫溢洪道设计，并将其结果与其他两种受自然启发的算法进行比较：侵入性杂草优化（IWO）和杜鹃搜索（CS）。溢洪道经过一次实际设计洪水（16,143 m³ / s），并根据气候变化（12,250 m ³ / s）设计洪水。结果表明，使用SFLA，通过现实设计洪水进行的优化使溢洪道的混凝土体积减少了37％，在气候变化下减少了43％。