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 80th 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 prediction demonstrates superior performance compared to the physically based model for the Marañón River by correctly triggering preparedness actions in three out of four historical occasions, while both the statistical and multi-model predictions capture all four historical events when the required threshold exceedance probability is reduced to 50 %, with only one false alarm. For the Piura River, the statistical model proves superior to all other approaches, correctly triggering 28 % more often in the hindcast period. 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秘鲁的高级防洪准备

灾难规划历来为高级备灾分配了最少的努力和资金；然而，证据支持通过适当的早期行动降低对洪水事件的脆弱性，挽救生命和金钱。除其他要求外，有效的早期行动系统需要提供高质量的预测来为决策提供信息。在这项研究中，我们评估了基于统计和基于物理的提前季节预测模型的能力，根据超过易发生洪水的马拉尼翁河的历史季节性流量的 80% 的概率为 75% 或更大的概率，适当地触发洪水早期准备行动。秘鲁的皮乌拉河。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，多模型（最小二乘组合）也根据当前的操作实践进行评估。通过在四分之三的历史事件中正确触发准备行动，统计预测展示了与基于物理的马拉尼翁河模型相比的卓越性能，而当所需的阈值超出概率为减少到 50%，只有一个误报。对于皮乌拉河，统计模型证明优于所有其他方法，在后报期间正确触发的频率高出 28%。应继续努力将这一提前季节预测框架应用于其他可能需要采取早期行动且当前预测能力有限的洪水易发地区。