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. Disaster planning has historically allocated minimaleffort and finances toward advanced preparedness; however, evidence supportsreduced vulnerability to flood events, saving lives and money, throughappropriate early actions. Among other requirements, effective early actionsystems necessitate the availability of high-quality forecasts to informdecision making. In this study, we evaluate the ability of statistical andphysically based season-ahead prediction models to appropriately triggerflood early preparedness actions based on a 75 % or greater probability ofsurpassing the 80th percentile of historical seasonal streamflow forthe flood-prone Marañón River and Piura River in Peru. Thestatistical prediction model, developed in this work, leverages theasymmetric relationship between seasonal streamflow and the ENSO phenomenon.Additionally, a multi-model (least-squares combination) is also evaluatedagainst current operational practices. The statistical predictiondemonstrates superior performance compared to the physically based model forthe Marañón River by correctly triggering preparedness actions inthree out of four historical occasions, while both the statistical andmulti-model predictions capture all four historical events when the requiredthreshold exceedance probability is reduced to 50 %, with only one falsealarm. For the Piura River, the statistical model proves superior to allother approaches, correctly triggering 28 % more often in the hindcastperiod. Continued efforts should focus on applying this season-aheadprediction framework to additional flood-prone locations where early actionsmay be warranted and current forecast capacity is limited.

Leveraging multi-model season-ahead streamflow forecasts to trigger advanced flood preparedness in Peru