Abstract. Disaster planning has historically allocated minimal effort and finances toward advanced preparedness, however evidence supports reduced vulnerability to flood events, saving lives and money, through appropriate early actions. Among other requirements, effective early action systems necessitate the availability of high-quality forecasts to inform decision making. In this study, we evaluate the ability of statistical and physically based season-ahead prediction models to appropriately trigger flood early preparedness actions based on a 75 % or greater probability of surpassing the 80^th percentile of historical seasonal streamflow for the flood-prone Marañón River and Piura River in Peru. The statistical prediction model, developed in this work, leverages the asymmetric relationship between seasonal streamflow and the ENSO phenomenon. Additionally, a multi-model (least squares combination) is also evaluated against current operational practices. The statistical and multi-model predictions demonstrate superior performance compared to the physically based model for the Marañón River by correctly triggering preparedness actions in all four historical occasions. For the Piura River, the statistical model proves superior to all other approaches, and even achieves an 86 % hit rate when the required threshold exceedance probability is reduced to 50 %, with only one false alarm. Continued efforts should focus on applying this season-ahead prediction framework to additional flood-prone locations where early actions may be warranted and current forecast capacity is limited.

中文翻译：

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利用多模型提前季节流量预测来触发高级洪水准备

摘要。灾难规划历来只花了最小的精力和资金来进行高级准备，但是证据表明，通过采取适当的早期行动，减少了对洪灾的脆弱性，挽救了生命和金钱。除其他要求外，有效的早期行动系统还需要提供高质量的预测信息来为决策提供依据。在这项研究中，我们评估统计和基于物理的赛季提前预测模型的能力，适当地触发洪水基于超过80的75％或更高的概率早准备行动^日洪水多发的马拉尼翁河和秘鲁皮乌拉河的历史季节性流量的百分位。在这项工作中开发的统计预测模型利用了季节性流量与ENSO现象之间的不对称关系。此外，还将针对当前的操作实践对多模型（最小二乘组合）进行评估。统计和多模型预测通过正确触发所有四个历史场合的备灾行动，显示了与马拉尼翁河基于物理的模型相比要优越的性能。对于皮乌拉河，统计模型证明优于所有其他方法，当所需的阈值超出概率降低到50％时，甚至只有一个错误警报，统计模型的命中率甚至达到86％。